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An Overview of Candidate Therapeutic Target Genes in Ovarian Cancer

Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitan”, University of Salerno, 84081 Baronissi, Italy
Genomix4Life, via S. Allende 43/L, 84081 Baronissi, Italy
CRGS-Genome Research Center for Health, University of Salerno Campus of Medicine, 84081 Baronissi, Italy
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2020, 12(6), 1470;
Received: 11 May 2020 / Revised: 31 May 2020 / Accepted: 2 June 2020 / Published: 4 June 2020
(This article belongs to the Special Issue Molecular Genetics of Breast and Ovary Cancer)


Ovarian cancer (OC) shows the highest mortality rate among gynecological malignancies and, because of the absence of specific symptoms, it is frequently diagnosed at an advanced stage, mainly due to the lack of specific and early biomarkers, such as those based on cancer molecular signature identification. Indeed, although significant progress has been made toward improving the clinical outcome of other cancers, rates of mortality for OC are essentially unchanged since 1980, suggesting the need of new approaches to identify and characterize the molecular mechanisms underlying pathogenesis and progression of these malignancies. In addition, due to the low response rate and the high frequency of resistance to current treatments, emerging therapeutic strategies against OC focus on targeting single factors and pathways specifically involved in tumor growth and metastasis. To date, loss-of-function screenings are extensively applied to identify key drug targets in cancer, seeking for more effective, disease-tailored treatments to overcome lack of response or resistance to current therapies. We review here the information relative to essential genes and functional pathways recently discovered in OC, often strictly interconnected with each other and representing promising biomarkers and molecular targets to treat these malignancies.

Graphical Abstract

1. Introduction

Ovarian cancer (OC) is one of the leading causes of cancer death in women, accounting for 295,414 new cases worldwide in 2018 and more than 180,000 victims [1]. According to the International Agency for Research on Cancer (IARC), the estimated global OC incidence for 2020 is 308,069 new cases and 193.811 deaths [2]. OC is an indolent disease, frequently diagnosed at advanced stages due to the lack of specific symptoms; current treatment of OC consists of surgery and systemic adjuvant or neoadjuvant chemotherapy; however, despite complete remission, the majority of initially responsive ovarian tumors often recur [3]. Given the poor prognosis of the disease, there is an urgent need to improve our knowledge of the genetic and molecular basis of OC to provide advances in the early detection and develop new treatment therapies.
Hormonal and reproductive factors are considered among the most significant risk factors for the development of OC. Early menarche or late menopause onset have been associated with a higher OC risk, suggesting that the ovulation-related proinflammatory response may promote malignant transformation and development of this gynecologic disease [4,5,6,7,8,9,10]. As a consequence, pregnancy, breastfeeding, and early menopause that preclude the ovulation, represent protective factors and can decrease the risk of OC developing [11,12,13,14]. Indeed, pregnancy, protects from OC through anovulation and decreased pituitary gonadotropins; multiple pregnancies confer up to 50% reduction of OC risk while nulliparity has been several times associated to ovarian carcinogenesis (29% increase of OC risk) [13,15,16,17]. In line with the anovulatory protective effect, breastfeeding has been inversely correlated to OC risk [18,19,20]. Literature also reports a consistent association between oral contraceptives assumption and reduction of OC risk, in particular when drugs are taken for a period ≥ 10 years [17,21,22,23]; particular effectiveness in risk prevention was observed when intrauterine contraceptives were used [24]. Conversely, the use of hormone replacement therapy (HRT) in post-menopausal women has been associated to an increased risk of ovarian cancer and it has been estimated that 55% of women who have used HRT, even for a short period, have developed OC during their lifetime [25].
According to the most probable tissue of origin, the World Health Organization groups ovarian tumors in surface epithelial cancers (65%), non-epithelial ovarian cancers including germ cell (15%) and sex cord-stromal tumors (10%), metastases (5%), and miscellaneous. Further classification of surface epithelial tumors takes into account cell type (serous, mucinous, endometrioid, clear cells), growth pattern (solid, cystic, surface), amount of fibrous stroma (cystadenoma and cystoadenofibroma) and atypia (benign, borderline, or malignant). Most of the malignant ovarian cancers are surface epithelial (90%) [26]; among them, the most represented ovarian cancer histotype is the serous (30%), followed by the mucinous (20%), endometrioid (15%), and clear cells carcinoma (5–10%) [27].
Few other rare types have also been described, such as Brenner tumors (malignant transitional cell tumor) and some mixed or undifferentiated carcinomas [28].
Lifestyle also affects the incidence of ovarian cancer; smoking, for example, has been associated with small increases in OC risk, in particular of the borderline mucinous type [29,30]. Other factors that negatively influence ovarian cancer onset include a high intake of saturated fats [31], high body mass index [32], and exposure to asbestos [33]. On the other hand, a moderate beneficial effect has been observed on ovarian cancer prevention in women that performed regular physical activity [34].
About 23% of ovarian cancer cases present a familiar inheritance pattern and are defined as hereditary neoplasms [35]; among them, 65–85% are caused by mutations in BRCA1 and BRCA2 genes, involved in the double-strand DNA breaks (DSBs) repair pathway, that cause 54% of OC lifetime risk increase. Other genes involved in DSBs repair and associated to hereditary ovarian cancer include RAD51, PALB2, CHEK2, BARD1, Mre11, RAD50, and NBS1 [36]. Lynch syndrome, caused by mutations in mismatch repair (MMR) genes, also accounts for 10–15% of all hereditary ovarian carcinomas [37]. Lastly, there are genes related to other familiar cancer syndromes linked to an increased risk of ovarian cancer, such as PTEN (PTEN tumor hamartoma syndrome), STK11 (Peutz-Jeghers syndrome) and MUTYH (MUTYH-associated polyposis) [38].
Genome-wide CRISPR-Cas9 dropout screening is emerging as a promising approach for characterization of driver genes of cancer growth [39,40]. Starting from the two main studies available so far, considering hundreds of human cancer cell models to identify essential genes for cell viability [41,42], in this review we deeply analyzed the data generated in a set of 48 OC cell lines in total, with the attempt to shed light on key molecular pathways involved and targetable in such an heterogeneous neoplasia. Focusing on 1213 essential genes in OC cells emerging from both studies, here we reviewed the functional pathways significantly affected by correlating computational information with experimental and, where possible, clinical data available in the literature. Finally, we also focused on the possible role of estrogen receptor-alpha (ERα), a debated candidate gene which is expressed in several OC histotypes, with high expression in serous ones [43]. Endocrine therapy has been used with modest and variable results in the treatment of OC [44], mainly due to tumor heterogeneity, thus it is not surprising that although crucial in OC progression, ERα did not emerge as a “fitness” gene within the investigated cell lines. Given the crucial role of this receptor in hormone-depending cancers and its relevance as therapeutic target, we aimed to extrapolate, among essential genes, those correlated with ERα activity and already implicated in relevant pathways for OC and that may be important for therapeutic purposes.

2. The Molecular Landscape of Ovarian Cancer

Advances in genomics technologies over the past decade have firmly established that, according to their molecular profiles, ovarian cancers can be classified into subtypes, each of them harboring distinct expression patterns, mutations, and epigenetic signatures. As a consequence, critical differences emerged between ovarian cancer subtypes in pathologic features, molecular changes, and clinical outcome; each subtype has been characterized, in fact, in its distinct genetic alterations, disease pathogenesis and progression, and survival outcome in response to therapy.
A recent classification ranks this heterogeneous group of malignancies into two broad categories: type I and type II. Type I ovarian cancers originate from clearly described primary ovarian lesions and comprise mucinous, endometrioid, low-grade serous, clear cell, and transitional cell carcinomas; on the other hand, the originating lesions of type II ovarian cancers are not well described and this category includes high-grade serous carcinomas, undifferentiated carcinomas, and carcinosarcomas [45].
Type I ovarian carcinomas generally develop from ovarian benign neoplasms, which in turn progress towards borderline and invasive carcinomas; they display a less aggressive behavior, with low metastatic spread at the time of diagnosis, stable genome, and generally no TP53 mutations, although several somatic mutations have been described [46]. Type II ovarian carcinomas are generally more aggressive, diagnosed at advanced stages and with unstable genome, and frequently showing mutations in TP53 or altered functions of BRCA1/2 genes [47,48].
In the serous subtype of EOC two groups with different molecular profiles, clinical presentation and prognosis can be distinguished: the high-grade serous ovarian cancer (HGSOC), representing 90% of all serous tumors, and the low-grade serous ovarian cancer (LGSOC) accounting for the remaining 10% [49]. Compared with HGSOCs, associated to a poorer prognosis, usually diagnosed at late stages of the disease and frequently with metastases, the LGSOC group has a better prognosis and a significantly longer survival time [50]. Regarding their origin, HGSOC tends to originate in the fallopian tubes, spreading towards the ovaries and peritoneum, while LGSOC usually originates in the ovary [51,52].
Large-scale analyses combining expression profiles, mutational frequency and copy number alterations of HGSOC revealed a tendency in the upregulation of genes involved in chromosomal instability and cellular proliferation, providing evidence that defects in the homologous recombination DNA repair mechanisms (defective in >50% of cases) play a major role in the etiology of these tumors. Pathogenic somatic variants have been identified in genes involved in cell cycle regulation, DNA recombination, and DNA damage response and repair such as TP53 (>95% of HGSOC), FAT3, CSMD3, NF1, RAD51C, RAD51D, BRIP1, RB1, GABRA6, CDK12, as well as germline and somatic mutations of BRCA1 and BRCA2 and loss of heterozygosity (LOH). Other genes frequently affected are PTEN, RAD51C, ATM, ATR, and many of the Fanconi anemia genes [28].
HGSOC are genomic-instable tumors with a tendency to copy-number variation resulting in the amplification or loss of several genes. More than 50% of HGSOC have homologous DNA repair pathway alterations and inactivation of tumor suppressor genes through gene breakage, mainly represented by BRCA1 and BRCA2. Other characteristic genetic alterations (>20%) include ID4, IRF2BP2, MYC, MECOM, PAX8, ZMYND8, and cyclin E1 (CCNE1) amplifications, the latter associated with resistance to therapy and poor prognosis; loss of PTEN is also predictive of poor prognosis. Signaling pathways frequently dysregulated include RB1, PI3K/Ras, Notch, and FOXM1 [28,53,54].
The molecular landscape of HGSOC reveals a strong tendency towards the variability of these tumors, whose genetic diversity promotes the development of distinct subclones, some of which may acquire pathogenic variants associated with resistance to the treatment and poor prognosis. On the other hand, the genomic instability of this subtype might also create variants that are more sensitive to chemotherapy, thus limiting cancer growth. For example, a better prognosis has been demonstrated for HGSOC in which genomic instability creates a higher response rate in platinum-based and poly ADP ribose polymerase (PARP) inhibition treatments [54].
Conversely to HGSOCs, LGSOCs are less aggressive, have a slower growth rate, and are genomic-stable. Expression profiling of LGSOCs has excluded the involvement of genes related to cell proliferation and DNA repair. Pathogenetic somatic variants of genes involved in signaling pathways are instead implicated, such as KRAS, NRAS, BRAF, ERBB2, and PI3KCA oncogenes. These alterations are accomplished by the frequent hyper-activation of the mitogen-activated protein kinase (MAPK) pathway, currently considered as a reasonable therapeutic target for LGSOCs, which have a poor response to the conventional chemotherapy due to the more competent DNA repair pathways [53,54].
Endometrioid ovarian cancers (EOVC) account for 10–20% of all EOC [55]. EOVC is a distinct and heterogeneous group of EOC; like serous tumors, both high and low-grade subtypes can be distinguished, with high-grade endometrioid tumors being very similar to HGSOC for their genomic instability and response to chemotherapy. Genomic profiling of endometrioid tumors has identified frequent activating mutations in ARID1A, CTNNB1, KMT2D, KMT2B, PIK3CA, PTEN, PP2R1A, and less frequently in KRAS and BRAF genes. Microsatellite instability, resulting from mismatch repair (MMR) deficiency and POLE mutation, was also observed [54,56].
Clear cell ovarian cancer (CCOC) comprises 5–10% of post-menopausal EOC and is characterized by a higher incidence among Asian women. Despite usually diagnosed at an early stage, CCOCs are less responsive to the platinum-based chemotherapy and have poor prognosis at late stages (III–IV) with respect to serous and endometrioid tumors [54,57].
Expression profiling has demonstrated that, from a molecular point of view, CCOC is more similar to lung cancer, endometriosis, and renal carcinoma than to other ovarian cancers [54,58,59,60,61]. The most frequently mutated genes in CCOC are ARID1A (46–57% of cases), CTNNB1, CREBBP, KRAS, MLH1, PIK3CA, PPP2R1A, and PTEN while a lower frequency of mutation is reported for BRCA1, BRCA2, and TP53 [54,62]. Genomic analyses have shown that CCOCs have mutations related to the process of ageing due to spontaneous deamination of methylated cytosines to thymines and leading to C-to-T mutations after DNA replication or misrepair of the DNA. Furthermore, CCOCs (>25%) exhibit a strong APOBEC signature, indicating alteration of genes encoding for enzymes involved in cytosine to uracile deamination [57].
Mucinous ovarian cancers (MOC) are rare when compared to other subtypes and comprise approximately 3% of EOC [63]. This heterogeneous subgroup differs greatly from the other EOC, showing morphological and genomic characteristics that appear to be more closely related to colorectal cancer. Among the genetic abnormalities identified, it is worth mentioning a high frequency of somatic KRAS variants (also found in other ovarian cancer types) and ERBB2 amplifications. Other molecular features distinctive of MOCs are mutations of RAS (45%) and BRAF (22.6%) and the overexpression of HER2. TP53 mutations are also present in 51% of MOC and associated with an increasing degree of malignancy. Additional frequently mutated genes are CDKN2A, CTNNB1, PIK3CA, PTEN, and RRAS2 [53,54].

3. The Role of Estrogen Receptors in Ovarian Cancer

Estrogens are a class of steroid hormones secreted by granulosa cells in the ovary; the predominant intracellular estrogen is 17β-estradiol (E2). Estrogens are involved in many physiological and pathological processes in the reproductive, cardiovascular, skeletal, endocrine, nervous, and immune systems. They are able to regulate the expression of several genes involved in cell development and proliferation and are known to be involved in breast carcinogenesis. In ovarian cancer, the role of estrogens is still debated but there is a growing body of evidence supporting the contribution of estrogens as risk factors after prolonged exposure to them [64]. Furthermore, there is some evidence supporting the involvement of the estrogen pathway in ovarian cancer progression [65,66]. Estrogen-mediated gene regulation occurs through two estrogen receptors (ERs), members of the nuclear receptor family of transcription factors, ERα and ERβ, encoded by ESR1 and ESR2 genes, respectively [67]. Once activated by estrogens, ERs dimerize and translocate to the nucleus where, after the recruitment of co-regulators, they directly bind to estrogen-response elements (ERE) on the genome and regulate the expression of target genes both positively and negatively [68].
In ovarian tissues, both ERα and ERβ are expressed [69]. In women of childbearing age, ERα is mainly located at theca cells, in the ovarian stroma, in the corpus luteum, and surface epithelium of the ovary. In postmenopausal women, ERα is expressed in the stroma, in the epithelial inclusion cyst, and in the ovarian-surface epithelium. The main locations of ERβ are granulosa cells [70]. In the development of ovarian cancer, ERs show different behavior: ERα shows pro-tumorigenic activities while ERβ acts as a tumor suppressor [43,71,72].
ERα is expressed in more than 50% of OCs and in approximately 80% of HGSOC, where its expression is associated with a poor prognosis [42,73,74]. ERα involvement in ovarian cancer progression is related to several processes, including cell proliferation induction, invasion and metastasis, and chemo-resistance. The binding of estrogens to ERα induces the transcription of genes that stimulate cell proliferation. It has been demonstrated that ERα can mediate mitogenic signaling activation, in OC cells, both in vivo and in vitro; this happens through the expression of several genes including MYC, PGR, and IGFBP3 [43]. It is known that the Mitogen-Activated Protein Kinase (MAPK) signaling can interact with hormonal mediators, such as ERα in its non-genomic pathway. The MAPK signaling pathway is a major regulator of cell proliferation, survival, and differentiation. Hyperactivation of this pathway occurs in EOC via gain of function mutations in Ras or Raf, which is thought to promote neoplastic transformation [75]. Cell proliferation via ERα is also mediated by activation of the Akt, ERK, and PI3K cascades [76,77] in OC cells. O’Donnel et al. observed that ERα mediates both growth response and gene expression changes in ovarian cancer cells exposed to E2. Indeed, many ERα-regulated genes in ovarian cancer cells have been reported, such as regulators of the cell cycle (CCNB1), apoptosis (TNFSF7, TRAP1, UBL1, and CASP4), transcription (FOSL1, TFAP4, EIF2B1), signaling (NOTCH4, IGFBP3, BENE, LCN2, GRSF1), and modulators of cytoskeleton and extracellular matrix remodeling (CTSD, CDH6, CYR61, KRTs 4, 7, and 13, VIM, TGFBI, DES, AKAP12, TRAM1, MMPs 11 and 17, PLAU) that could be involved in invasion and metastasis [78]. It has been also reported that PAX2 is activated by E2 via ERα in breast cancer and it is confirmed that the expression of PAX2 is proportional to the expression of ERα in ovarian serous cancer [79].
Epigenetic mechanisms have emerged as contributing factors to carcinogenesis. A recent work from our group elucidated the role of the DOT1L (disruptor of telomeric silencing-1-like) gene as a regulator of ERα activity in estrogen-responsive OCs [80]. DOT1L, a histone methyl transferase, acts as transcriptional regulator through H3K79 mono-, di-, and tri-methylation. ERα cooperates with DOT1L to modulate, at transcriptional level, the expression of genes involved in OC cell proliferation and other key cellular functions. Indeed, ERα or DOT1L inhibition, with selective antagonists, results in a dose-dependent reduction of OC cell proliferation.
The involvement of ERα has also been described in the invasion and metastasis mechanisms in OC cells where E2 is able to increase the metastatic potential of human epithelial ovarian cancer cell lines and enhance cell migratory potential through an ERα-dependent pathway [81]. Furthermore, the involvement of ERα in the invasion mechanism through the activation of Plexin B1 was also observed. Plexin B1 is an oncogene involved in cell migration that is positively regulated by ERα and negatively regulated by ERβ [82].
Moreover, it has been observed that CXCR7 (C-X-C Chemokine Receptor Type 7) and CXCL11 (C-X-C motif chemokine 11) genes are activated by estrogens through the direct recruitment of ERα and this leads to increased migration and invasion of OC cells [83]. Estrogens are also able to influence the anoikis process by Bit1 involvement. Cancer cells are generally more resistant to anoikis and this contributes to metastasis and invasion. Bit1 (Bcl2-inhibitor of transcription 1) is a mitochondrial protein involved in the cell death machinery after its release from mitochondria. In the cytosol, Bit1 forms a complex with AES (a member of the Groucho family of transcriptional corepressors) and promotes apoptosis. E2-activated ERα, decreases Bit1 level in the cytosol, which determines anoikis reduction in OC cells [84].
Lastly, ERα can influence OC cell response to chemotherapeutic agents. ERα can be activated by cisplatin via ERK cascade activation through the phosphorylation at serine 118. This can induce platinum-resistance by increasing the expression of anti-apoptotic proteins like Bcl-2. Contrarily, ERα downregulation is able to inhibit cisplatin-resistance [76]. All this evidence supports the possibility that, although understudied, ERα represents an effective target in the treatment of OC even though resistant to conventional chemotherapeutic agents.

4. Genome-Wide CRISPR-Cas9 Dropout Screening for Identification of Candidate Therapeutic Target Genes in OC

High-throughput CRISPR-Cas9 functional genomic screenings have allowed to perform a genome-wide perturbation of gene expression and determine the involvement of specific genes in cellular processes, thus understanding the hub genes causing diseases and exploring the responsiveness and resistance to drugs. The most popular and simplest approach to characterize the genetic drivers of tumor growth is the dropout screening, which allows the identification of fitness genes, defined as context-dependent essential genes that regulate the proliferation and/or survival of cancer cells under specific growth conditions. This approach also enables the identification of genes that are essential in cancer but not in normal tissues and therefore represent optimal therapeutic targets with minimal side effects [39,40].
Several studies have pointed out the efficiency of high-throughput CRISPR/Cas9 screening in the identification of cancer-related genes in ovarian cancer. Kodama et al., in their work, performed an in vivo dropout screen in human tumor xenografts using a pooled shRNA library targeting thousands of druggable genes to find out a list of 10 potent drug targets for EOC, including the novel oncogene KPNB1 [85]. He et al. applied a loss-of-function CRISPR screen and recognized DYNLL1 as an inhibitor of DNA end resection, whose loss in BRCA1 deficient HSGOC cells induced resistance to platinum drugs and inhibitors of poly(ADP-ribose) polymerase [86]. Similarly, Fang et al. identified C12orf5, encoding TP53 induced glycolysis and apoptosis regulator (TIGAR), as a novel therapeutic target able to modulate ovarian cancer sensitivity to the PARP inhibitor olaparib [87]. In addition, Qianying et al. shed light on a group of genes involved in cisplatin resistance in ovarian cancer cells, identifying ZNF587B as a novel predictive marker [88], whereas Stover et al. performed a near genome CRISPR/Cas9 screen in BRCA2 mutant HGSOC cell lines and identified BCL2L1 as a gene that mediates resistance to platinum-based chemotherapy [89]. Overall, it is evident that the scientific community is widely focusing on the application of knockout loss-of-function screenings to identify novel exploitable targets in the constant search for effective drugs able to overcome the major problem of chemo-resistance.
Among the large-scale CRISPR-Cas9 dropout screenings generated so far, two independent studies have been performed across hundreds of human cancer cell lines at the Broad and Sanger Research Institutes [41,90,91]. Here, we collected genome-scale CRISPR-Cas9 screening data from the Achilles project at Broad Institute through the DepMap portal [92] and from Sanger Project Score [93]. In total, 48 ovarian cancer cell lines, representative of the main molecular subtypes of OC (Serous, Mucinous, Endometrioid, and Clear Cells) and some rare tumors, were taken into account (Table 1) and 18,333 and 17,995 genes were independently screened from Broad’s and Sanger’s datasets, respectively. The reduction of cell viability upon gene inactivation was quantified using individual gene scores across cell lines (gene dependency profiles) using fully processed gene scores available for download from the Broad and Sanger Cancer Dependency Map webportals. A gene was considered fitness if the CERES score was ≤ -0.5 for Broad’s data and Average Score ≤ 0 for Sanger’s data.

5. Functional Pathways Affected by OC Fitness Genes

By comparing the two datasets above mentioned, 1213 common fitness genes were identified (with 2034 and 1410 essential genes observed in Broad and Sanger studies, respectively).To elucidate the functional pathways connected to fitness genes in the pathological environment of OC, we performed a Gene Ontology (GO) analysis using the Ingenuity Pathway Analysis (IPA, QIAGEN, Redwood City, tool. As a result, we obtained a distribution map of the OC fitness genes made of interconnected nodes across biological processes critical for survival and proliferation of malignant cells and for tumor growth. Crucial signaling pathways, whose alterations represent the hallmarks of cancer, were identified, including cell cycle regulation and DNA repair mechanisms, hypoxia and angiogenesis processes, proliferative signaling, RNA translation and post-translational modifications, protein degradation, nucleotide metabolism, etc. In Table 2, the 54 canonical pathways most significantly affected, together with the involved fitness genes, are reported.

5.1. DNA Damage Response Associated Pathways

Among the most-significant cancer-associated alterations, aberrations in the DNA damage response (DDR) play a major role in OCs. The constituent pathways of the DDR include DNA repair machinery, cell cycle checkpoints, and apoptotic pathways; mutations in any components of these pathways are involved in the ovarian cancer initiation and progression as well as in resistance to therapy [95]. Fitness genes identified by the dropout CRISPR-Cas9 screening in ovarian cancer cell lines include key-genes involved in the cell cycle checkpoint regulation, components of the mismatch repair (MMR) system that recognize and repair DNA abnormalities, members of the homologous recombination (HR) and nucleotide excision repair (NER) pathways (see Table 2 and Figure 1).
Alteration of regulatory mechanisms of the cell cycle results in uncontrolled cell proliferation which is a hallmark of cancer; these alterations occur in cyclins, cyclin-dependent kinases (CDK) and CDK inhibitors. In serous ovarian carcinoma, high expression of P16, P53, and P27 and low expression of P21 and cyclin E has been reported [96]. AURKA, CDC25, cyclin B and PLK1, have been reported to be overexpressed in OC [97]; furthermore, both AURKA and CHEK1 were associated with detrimental outcome in early-stage OC [98]. In this context, targeting of DNA repair mechanisms in combination with inhibition of key regulators of the mitotic process could be useful for ovarian cancer treatment. Indeed, a promising synergistic antitumoral effect between AURKA and CHEK1 inhibitors in ovarian cancer has been described [97]. WEE1 kinase, encoded by the fitness gene WEE1, is frequently expressed in ovarian serous carcinoma and plays a key-role in the G2 cell cycle checkpoint arrest for pre-mitotic DNA repair. Abrogation of the G2 checkpoint through WEE1 inhibition could result in increased antitumor activity of DNA damage-inducing chemotherapeutic agents. WEE1 expression is significantly higher following exposure to chemotherapeutic agents [99]; the combination of chemotherapy with WEE1 inhibitors is therefore particularly promising in ovarian cancers [100]. Replication factor C (RFC) also plays a crucial role in the checkpoint control of cell cycle progression. RCF3 subunit is overexpressed in OC and has a prognostic value in predicting patient survival. RCF3 knockdown has been demonstrated to reduce viability and proliferation in OVCAR-3 cells by blocking the cell cycle in the S-phase and inducing apoptosis, suggesting that it could be a potential target in the clinical practice [101].
Fitness genes identified in ovarian cancer cell lines also included many DDR transducers; among them, a critical role is played by ATR. Once activated by DNA damage, ATR blocks the cyclin-dependent kinases CDK1 and CDK2 (also identified among OC fitness genes) thus preventing cell cycle progression. Cyclin-dependent kinases CDK1 and CDK2 regulate the expression of other proteins involved in DNA repair, cell cycle control, and apoptosis; dysregulation of their activity is frequently associated with inappropriate cell-cycle progression. High ATR expression in ovarian cancer tissues has been linked with poor survival and progression free-survival, while it has been identified among the critical factors in determining platinum sensitivity in cell lines models [102]. A defective DNA-damage response (DDR) is a defining hallmark of high-grade serous ovarian cancer (HGSOC). In HGSOC cell lines, PARP inhibitors (e.g., olaparib) in combination with drugs targeting the ATR/CHK1 axis resulted in tumor regression in BRCA-mutant ovarian cancer [103]. ATR inhibitors can sensitize ovarian tumors to DNA-damaging agents that primarily induce replicative stress as their mechanism of action [104]. Several other fitness genes are interconnected in this pathway, including those encoding for HUS1, RAD1, and RAD9 proteins that form a hetero-trimer acting as a sensor of DNA lesions. HUS1 overexpression has been correlated with worst prognosis and with high expression of P53 and BAX and high mitotic and apoptotic indices in OC [105]. RAD1 and CHEK1, other fitness genes involved in cell cycle regulation, are crucial factors required for the check-point mediated cell cycle arrest and activation of DNA repair by homologous recombination (HR). The two genes have also been associated with a BRCA-like phenotype in hereditary breast and ovarian cancer. In OC cells, RAD1 and CHEK1 knockdown led to decreased cellular viability, increased sensitivity to cisplatin, and decreased HRR efficiency [106], while CHEK1 overexpression was associated with detrimental outcomes in early-stage ovarian cancer [98]. RAD9, a pro-apoptotic protein, has been associated with higher mitotic and apoptotic indices [105].
DNA double-strand break repair by homologous recombination (HR) uses DNA sequence homology and exploits genetic information available on an undamaged sister chromatid or homologous chromosome [107]. The HR process has its core in the nucleation of the RAD51 filament, which competes with the ssDNA-binding protein RPA, whose role is to protect single-strand DNA from degradation and formation of secondary structures that would interfere with repair. Once RAD51 nucleation prevails, the process of strand invasion in the unbroken identical DNA molecule begins and allows the repair mechanism to work properly [108]. Population studies have showed that deleterious mutations in RAD51 paralogs RAD51C and RAD51D confer susceptibility to epithelial ovarian cancer [109], while specific polymorphisms of the RAD51 gene could be used as a biomarker for increased risk of OC [110]. Moreover, RPA availability seems to be related to chemo-resistance in HGSOC [111].
Recently, the HR pathway has attracted considerable attention not only for its role in the repair of DNA damages induced by chemotherapeutic agents, but also because many cancers are, to different extents, defective in HR repair, raising the possibility to exploit this feature for novel cancer treatments. In this context the concept of synthetic lethality acquires high relevance, that leads to cell death when two otherwise non-lethal defects occur simultaneously and synergize; using inhibitors of poly(ADP-ribose) polymerase (PARP), a protein involved in DNA repair processes, it is in fact possible to kill specifically HR-deficient cancer cells [112,113].
The main pathway for the removal of large DNA lesions is instead the nucleotide excision repair (NER); this system is active towards single stranded DNA damages; the damaged strand is removed and the gap filled replaced by DNA synthesized using the undamaged strand, and the two ends are joined together by a DNA ligase. The NER system plays a key role in OC for its prognostic value and in response to treatment. Among fitness genes belonging to the NER pathway we identified some crucial factors, whose mutations are strongly correlated to cancerous phenotype, such as POLE, RPA3, and ERCC genes [95]. Excision repair cross-complementing DNA-helicases ERCC2 and ERCC3 belong to the transcription factor IIH complex and unwind DNA strands that flank the damaged site. Although ERCC2 has been correlated with a more aggressive phenotype in head and neck tumors, the role of both proteins in OC is still unclear [114]. A higher somatic mutation burden has been recently reported in OC for the POLE gene, encoding for an enzyme involved in the DNA repair and replication; the impact of POLE mutations also seems to be more prominent in sporadic OC than in familiar one [115]. Upregulation of the RPA3 gene has been associated with HGSOC proliferation [116].
MMR is critical for the detection of DNA damages as deficiency in this pathway could lead to uncontrolled proliferation. Loss of function in the hub genes of the MMR pathway have been identified in 29% of ovarian cancers and their mutations correlate with the neoplasm stage [95]. In Figure 1 some of the functional pathways specifically related to DNA damage repair mechanisms are reported as an example; in particular nucleotide excision repair (Figure 1a), DNA mismatch repair (Figure 1b), and homologous recombination (Figure 1c) have been depicted, with fitness genes specifically correlated and physical interactions among the different proteins involved in the pathways.

5.2. Hypoxia and Angiogenesis Related Genes

After evading growth suppressors and escaping apoptosis, cancer cells must face hypoxia and low nutrient levels, peculiar characteristics of the tumor microenvironment, to support their energy metabolism and sustain their growth. Therefore, it is not surprising that hypoxia-dependent signaling pathways are commonly de-regulated in cancer cells. A recent study demonstrated that clear cell and serous EOC are under constant endoplasmic reticulum (ER) stress caused by the accumulation of unfolded proteins; due to this, the unfolded protein response (UPR) sensor PERK located in the ER activates and phosphorylates the eukaryotic translation initiation factor eIF2, resulting in a general suppression of translational initiation and global protein synthesis [117].
Interestingly, there are several lines of evidence suggesting that this mechanism is also linked to the estrogen signaling, since a role for eIF2α as a key regulator of estrogen-induced apoptosis has been recently demonstrated in estrogen sensitive MCF-7 breast cancer cells through PERK-mediated phosphorylation [118]. Tumor hypoxia is also a major regulator of the angiogenesis process, where new abnormal vasculature is formed around the tumor, thus providing nutrients to the malignant cells and supporting tumor growth. A key-mediator of angiogenesis in cancer is the VEGF, a cytokine whose expression is regulated by several factors including hypoxia; once activated, VEGF promotes endothelial cells proliferation, migration, and vascular permeability. Strongly implicated in normal ovarian function, VEGF plays a critical role in OCs, high-vascularized tumors; its overexpression represents an early event in ovarian carcinogenesis and is associated with tumor progression and poor prognosis [119,120]. In breast cancer cells, the expression of VEGF is induced by estrogens through the association of ERα to the estrogen response elements (EREs) located within the promoter region of the gene. On the other hand, in endometrial carcinoma cells, VEGF transcription is regulated by 17β-estradiol (E2) through a variant ERE localized ≈1.5 Kb upstream the VEGF transcription start site [120], thus indicating that the estrogens may directly regulate tumor angiogenesis also in ovarian cancer. Key genes involved in VEGF signaling, including OC fitness genes retrieved, are reported in Figure 2.

5.3. Proliferative Signals

One of the fundamental traits of cancer cells involves their ability to sustain proliferation by deregulating the normal cell growth and division cycle that ensure the homeostasis of cell number and the maintenance of normal tissue architecture and function. Mitogenic signals are mainly represented by growth factors that bind cell-surface or intracellular receptors stimulating cell proliferation.
Among the fitness genes identified in the investigated OC cells, many take part in the insulin receptor pathway (Figure 3a). The insulin receptor can mediate a trophic effect in some transformed cells by activating mitogenic signals [121]. In many preclinical studies, the inhibition of insulin receptor shows a reduction in growth of ovarian cancer models and potentiates the efficacy of platinum-based chemotherapy. However, despite the pre-clinical data, anti-IR targeted strategies lacked efficacy in the clinic [122]. Mitogenic pathways activated by the insulin receptor are the canonical phosphatidylinositol 3-kinase (PI3K)-AKT, mTORC1, and RAS-extracellular signal-regulated kinase (ERK) pathways. Hyperactivation of these pathways is implicated in the development, maintenance, progression, and survival of ovarian cancer. PI3K, AKT, and mTOR, are highly mutated or overexpressed in a high percentage of ovarian cancer patients and are associated with advanced grade and stage disease and poor prognosis. This pathway could represent a target for OC therapies [123]. Moreover it has been noticed that the PI3K/Akt/mTOR signaling is required for E-cadherin downregulation and involved in the invasion mechanism [124]. Another pathway revealed by the IPA analysis is the Ran signaling (Figure 3b). Ran, a member of the Ras GTPase family, is a nucleocytoplasmic shuttle protein that is involved in cell cycle regulation, nuclear-cytoplasmic transport, and plays an important role in cancer cell survival and progression. This protein is highly expressed in epithelial ovarian cancers where it is associated with a poor prognosis [125]. It was seen that Ran downregulation induces caspase-3 associated apoptosis and causes a delay in the tumor growth. These results suggest that Ran could potentially be a suitable therapeutic target for OC treatment [126]. Moreover, other Ran-related factors are known to be involved in ovarian cancers, such as CSE1L, the human homolog of the yeast cse1gene. CSE1L is overexpressed in ovarian cancer where it is related to adverse patient outcomes. CSE1L forms a complex with Ran and importin-α and regulates nucleocytoplasmic traffic and gene expression. CSE1L protects ovarian cancer cells from death both in vitro and in vivo by suppressing the pro-apoptotic RASSF1 gene. The nuclear accumulation of CSE1L improves the expression of pro-oncogenic genes it regulates [127]. In addition to tumor growth, Ran is also involved in metastasis mechanisms. There is a link between Ran and RhoA signaling that contributes to enhanced ovarian cancer cell growth and invasiveness. RhoA is a Rho GTPase able to regulate many aspects of cell invasion and its expression is associated with advanced stage of ovarian cancer. Ran can form a complex with RhoA, leading to RhoA stabilization and activation. This Ran-RhoA signaling complex could be a molecular target for controlling cancer metastasis [128].

5.4. ER-Related Pathways and Fitness Genes in OC

The signaling of ERα mediates mitogenic activation in OC cells by regulating the expression of genes promoting cell proliferation [43]. Among the fitness genes identified by the CRISPR-Cas9 drop-out screening, 35 were directly involved in ER related pathways (see Table 2 and Figure 4), including the MYC oncogene, frequently amplified in OCs, MTOR, CCND1 CDK2 CCNA2 CDK, PCNA, PPP1R12A PIK3C3 EIF4E HDAC3, PPP1CB, involved in cell cycle regulation and cell proliferation, EIF2B3, EIF2B4, EIF2B2, EIF2B5, connected with the activation of the immune response, MED10, MED14, MED17, MED18, MED20, MED21, MED30, MED4, and MED6 members of the Mediator (MED) complex, an evolutionary conserved multiprotein involved in RNA polymerase II-dependent transcription, whose aberrations have been reported in several malignancies including OC [129].
Among others, we noticed the proliferating cell nuclear antigen (PCNA), a processivity factor for DNA polymerase δ, involved in the recruitment of DNA replication-related proteins [130]. It has been observed that PCNA expression can be positively regulated by ERα and correlates to increased cell proliferation and cell cycle progression; moreover, immunostaining assays to evaluate the presence of the protein can be applied to define different prognostic subgroups in ovarian cancer patients [131].
To further characterize the expression and mutational landscape of ERα-associated fitness genes, we explored three Ovarian Serous Cystadenocarcinoma cohorts (PanCancer, Nature 2011 and Firehose Legacy) collected by The Cancer Genome Atlas (TCGA) database, altogether comprising 1680 ovarian cancer tissues from 1668 patients.
Estrogen-receptor pathway associated fitness genes in OC cells were altered in 74% of OC patient tissues and, noteworthy, most frequently amplified than deleted or mutated (Figure 5).
In order to extrapolate genes positively or negatively regulated by ESR1 in OC tissues, we performed a correlation analysis between ESR1 and the 35 fitness genes involved in the ERα signaling. The analysis was performed by comparing gene expression data from the RNA-Seq datasets collected by TCGA in Ovarian Serous Cystadenocarcinoma cohorts with the aid of cBioPortal tool [132,133,134].
As a result, we observed a statistically significant positive Spearman’s correlation (p value < 0.05) between ESR1 and MYC, an estrogen-responsive gene whose overexpression may contribute to acquired resistance in ER+ breast cancers [135]. This association is also supported by experimental evidences in OC cell lines, where estrogen treatment increases tumor burden and induces MYC expression [43]. Conversely, we observed a negative Spearman’s correlation between ESR1 and MED4, MED10, MED6 genes, encoding for three subunits of the Mediator complex. An ESR1 negative correlation was also detected with cell cycle regulators CCNA2 and CDK1 and the translation initiator factor eIF2B2.
Genome-scale CRISPR-Cas9 dropout screening in OC cell lines, combined with TCGA genomic and transcriptomic data, led us to hypothesize that ESR1 signaling might involve multiple interconnected pathways regulated by fitness genes in OC. This observation was further supported by experimental evidence of an ERα involvement in cancer cells proliferation and survival through the regulation of key-genes involved in cell cycle control, apoptosis, transcription, and through the activation of the MAPK signaling pathway, RAN signaling [75], activation of PI3K/AKT/mTOR, and Ras/MEK/ERK cascades [76,77].

5.5. Other Suitable Pathways for Targeted Therapies

Interestingly, among the multiple pathways influenced by ovarian cancer-related fitness genes, the mevalonate pathway, the telomere extension pathway, and different endocytic pathways were also present, each of them already known for its documented involvement in cancer physiology and development.
The mevalonate pathway is implicated in several key metabolic functions, leading to the production of essential sterol isoprenoids, like cholesterol, and non-sterol isoprenoids like dolichol, isopentenyl, and ubiquinone [136]. The rate-limiting enzyme of the mevalonate pathway is the hydroxymethyl-glutaryl coenzyme A (HMG-CoA) reductase (HMGCR), which converts HMG-CoA to mevalonic acid. From mevalonate the dimethylallyl pyrophosphate is then produced, that can in turn be condensed into either farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP), involved in the process of protein prenylation, a fundamental step to facilitate protein attachment to membranes. This process is particularly important for post-translational modifications of Ras, Rho, Rab, and Rac small GTPase family proteins and enhance their membrane localization [137] and it is well known that many of these proteins are established oncogenes, associated with ovarian cancer cell aggressiveness and so influencing disease outcome [138].
While the correlation between the expression of tumor-specific HMGCR and ovarian cancer outcome has been pinpointed [139], it appears evident that statins assumption positively impacts on reducing OC risk [140,141,142], by inhibiting different aspects of the cancerous phenotype [143] and synergizing with chemotherapeutic agents [144,145] to enhance cell death. Indeed, the use of mevalonate pathway antagonist lovastatin has shown significant efficacy in reducing the proliferation of ovarian cancer cells in mouse xenograft models, regulating the expression of several essential genes involved in DNA replication, Rho/PLC signaling, glycolysis, and cholesterol biosynthesis pathways [146]. In addition, simvastatin, a widely used HMGCR inhibitor, has exhibited the ability to induce cell death of metastatic OC cells in syngeneic mouse models, which undergo extensive genetic reprogramming and overexpress mevalonate pathway-associated genes, conferring them resistance to apoptosis [147]. In line with these findings, the use of mevalonate pathway inhibitors, and in particular inhibitors of farnesyltransferase and geranylgeranyltransferase, has displayed marked effects in suppressing ovarian tumor growth, likely inducing autophagy and increased susceptibility to chemotherapy [148]. Lastly, administration of the farnesyl diphosphate synthase inhibitor zelodronic acid, a bisphosphonate, can cooperate with pitavastatin to synergistically inhibit the growth of ovarian cancer cells and induce apoptosis, by altering the subcellular localization of small GTPases [149]. Altogether, the importance of the mevalonate pathway and its derivate metabolites in the pathogenesis and development of ovarian cancer has pointed out the utility of drug repositioning, namely the employment in cancer treatment of drugs generally used for other purposes, to broaden the range of available therapeutic options and possibly overcome chemoresistance.
Telomeres correspond to the terminal parts of chromosomes, and include DNA tandem repeats complexed with proteins, whose activity provides telomere elongation and prevents the DNA damage repair machinery to recognize chromosome ends as double-strand breaks [150]. In this context, a protective and regulatory role is exerted by the shelterin complex, also called telosome, composed by six proteins: TERF1, TERF2, POT1, RAP1, TINF2, and TPP1 [151]. Although telomeres tend to have different lengths in cancer, it appears that ovarian carcinoma cells specifically activate telomerase and maintain short stable telomeres in vitro and in vivo [152]. Moreover, it is not unusual to find telomere fusions in ovarian tumor tissues, also at early stages, suggesting that telomere dysfunction may be essential in the initiation and progression of the disease [153]. Anyway, to date, no specific correlation between OC-specific mortality and telomere length has been found [154], but different genetic variants in telomere-maintenance genes have been associated to OC risk [155], as well as shorter telomere length [156]. Interestingly, it has been observed that alkylating agents treatment of responsive OC cells can produce a downregulation of telomerase activity, a phenotype not replicable in resistant cells [157]. Since telomerase activity is generally higher in cancer cells [158], different strategies have been explored to make it a druggable target to impair cancer cell survival; for example, the relative low hTERT expression in normal cells make it an ideal candidate for immuno-targeting [159]. Additionally, anti-telomerase antisense oligonucleotides [160] and hTERC-targeting siRNAs [161] have provided the possibility for novel fascinating therapeutic approaches. Shelterin complex is also being evaluated for target treatments. Indeed, compounds able to disrupt the telomer–shelterin interaction and uncapped chromosome ends can produce selective cytotoxic effects in tumor cells [162,163].
Finally, endocytosis provides the main cellular mechanism to recycle protein components from cell membrane, internalize external molecules, and attenuate receptor signaling. While clathrin-mediated endocytosis represents the best studied system, other endocytic compartments, like caveolae, contribute to spatio-temporal activation of signaling molecules and constitute platforms for the assembly of signaling complexes linking the endocytic and signaling programs [164]. Endocytic pathways can be involved in cancer progression in different ways, by sustaining oncogenic receptor signaling, regulating cell fate determination, cell cycle, and apoptosis, and by orchestrating the signals essential for directed cell movements [164]. Intracellular transport and membrane traffic through the Golgi complex is instead regulated by to coatomer complex I (COPI)-coated vesicles [165], essential to ensure protein quality control and correct sorting. An intact COPI is also essential for productive autophagy, a process dually involved in tumor progression; COPI members are in fact overexpressed in several types of cancer, including OC, and are associated with poor prognosis. Inhibition of COPI member results in increased cell death and may represent a suitable therapeutic target [166,167].

6. Conclusions

Ovarian cancers are among the most lethal and heterogeneous gynecological malignancies, with distinct clinicopathological and molecular features and prognosis, this representing a major challenge in their classification at both histological and molecular level. Indeed, inter- and intra-tumor heterogeneity seems to be the main cause of treatment failure. Molecular network changes are considered strong hallmarks of OC carcinogenesis and their exploitation an eligible tool for hub molecules discovery and the identification of targeted and personalized therapies. Loss of function screenings have recently emerged as promising approaches for the identification of candidate genes useful for the implementation of novel therapeutic protocols and possible drug repositioning in human cancers. Starting from the two most comprehensive CRISPR-Cas9 dropout screenings performed so far, we highlighted the most significantly affected functional pathways in OC. These involve 1213 genes that emerged as essential for cell viability and influencing more than 50 pathways relevant for the mainly characterized ovarian neoplasms (Figure 6). Most of them are interconnected with each other and some get more attention, such as the widely investigated DNA damage repair, VEGF, mTOR, EIF2, RAN, p53, ATM, iron homeostasis signaling and mevalonate pathway. Among them, the estrogen receptor signaling, although understudied mainly because of the challenging classification between ER-positive and ER-negative OCs and for discordant results of endocrine therapies, represents a traditional candidate gene which is emerging with an alternative look. Indeed, even though this receptor does not appear among the fitness genes for the OC cells considered, ER-related signaling pathways are strongly affected by several OC fitness genes. Moreover, ERα has been demonstrated to physically interact with most of these genes and considering TCGA patient-derived datasets, it results commonly mutated, in OC tissues, together with other ER-related essential genes. Thus, blocking estrogen signaling by targeting one or more of those ER-related genes could prove to be therapeutically effective.

Author Contributions

Conceptualization: E.A., G.P., A.S., R.T. and A.W.; writing and original draft preparation, G.P., A.S., C.F., V.M., and R.T.; writing, review and editing: all authors; funding acquisition: G.G., G.N., F.R., R.T., and A.W. All authors have read and agreed to the published version of the manuscript.


This work was supported by Italian Association for Cancer Research (grant number IG-23068), University of Salerno (Fondi FARB 2017), Regione Campania, Progetto GENOMAeSALUTE (POR Campania FESR 2014/2020, azione 1.5; CUP:B41C17000080007), Regione Campania (‘La Campania lotta contro il cancro’ project Rare-Plat-Net, CUP: B63D18000380007), Genomix4Life Srl and Ministero dell’Istruzione, Università e Ricerca, progr. PON R&I 2014-2020 ‘Dottorati innovativi con caratterizzazione industriale’ XXXV Ciclo (Fellowships DOT1328517, CUP D52G19000580006 to V.M. and DOT1318705, CUP: E66C18000940007 to C.F.). E.A. was supported by Fondazione Umberto Veronesi. V.M., G.P. and C.F. are PhD Students of the Doctorates in Translational Medicine for Development and Active Ageing of the University of Salerno, Biomedical Sciences and Technologies of the University Roma 3 and Veterinary Sciences of the University of Napoli ‘Federico II’, respectively.

Conflicts of Interest

The authors declare no conflict of interest.


AKAP12A-kinase anchor protein 12
AKTProtein kinase B
APOBECApolipoprotein B mRNA Editing Catalytic Polypeptide-like
ARID1AAT-Rich Interaction Domain 1A
ATMATM serine/threonine kinase
ATRATR serine/threonine kinase
AURKAAurora kinase A
BARD1BRCA1 associated RING domain 1
BAXBCL2 associated X, apoptosis regulator
BCL2BCL2 apoptosis regulator
BCL2L1BCL2 like 1
BENEBenzoate transport protein
Bit1Bcl2-inhibitor of transcription 1
BRAFB-Raf proto-oncogene, serine/threonine kinase
BRCA1BReast CAncer gene 1
BRCA2BReast CAncer gene 2
BRIP1BRCA1 interacting protein C-terminal helicase 1
CASP4Caspase 4
CCOCClear cell ovarian cancer
CDC25Cell division cycle 25 homolog A
CDKCyclin-dependent kinases
CDK1Cyclin-dependent kinases 1
CDK12Cyclin-dependent kinases 12
CDK2Cyclin-dependent kinases 2
CDKN2ACyclin-dependent kinase inhibitor 2A
CHEK1Checkpoint kinase 1
CHEK2Checkpoint kinase 2
COPICOPI-coated vesicles
CREBBPCREB-binding protein
CSE1LHuman homolog of the yeast cse1gene
CSMD3CUB and Sushi multiple domains 3
CTNNB1Catenin beta 1
CTSDCathepsin D
CXCL11C-X-C motif chemokine 11
CXCR7C-X-C Chemokine Receptor Type 7
CYR61Cysteine rich angiogenic inducer 61
DDRDNA damage response
DOT1LDisruptor of telomeric silencing-1-like
DSBDouble-strand DNA breaks
DYNLL1Dynein light chain LC8-type 1
EIF2Eukaryotic translation initiation factor 2
EIF2B1Eukaryotic translation initiation factor 2B subunit alpha
EIF2B2Eukaryotic translation initiation factor 2B subunit beta
EIF2B3Eukaryotic translation initiation factor 2B subunit gamma
EIF2B4Eukaryotic translation initiation factor 2B subunit delta
EIF2B5Eukaryotic translation initiation factor 2B subunit epsilon
EIF4EEukaryotic translation initiation factor 4E
EOCEpithelial ovaria cancer
EOVCEndometrioid ovarian cancers
ERBB2Erb-b2 receptor tyrosine kinase 2
ERCCNA excision repair protein ERCC-1-like protein
ERCC2ERCC excision repair 2
ERCC3ERCC excision repair 3
EREEstrogen-response element
ERKExtracellular regulated MAP kinase
ERαEstrogen receptor α
ERβEstrogen receptor β
ESR1Estrogen receptor 1
ESR2Estrogen receptor 2
FAT3FAT atypical cadherin 3
FOSL1FOS like 1, AP-1 transcription factor subunit
FOXM1Forkhead box M1
FPPFarnesyl pyrophosphate
GABRA6Gamma-aminobutyric acid type A receptor subunit alpha6
GGPPGeranylgeranyl pyrophosphate
GOGene Ontology
GRSF1G-rich RNA sequence binding factor 1
HDAC3Histone deacetylase 3
HDIHuman Development Index
HER2Human epidermal growth factor receptor 2
HGSOCHigh-grade serous ovarian cancer
HMG-CoAHydroxymethyl-glutaryl coenzyme A
HMGCRHydroxymethyl-glutaryl reductase
HRHomologous recombination
HRTHormone replacement therapy
hTERTelomerase reverse transcriptase
HUS1HUS1 checkpoint clamp component
ID4Inhibitor of DNA binding 4, HLH protein
IGFBP3Insulin like growth factor binding protein 3
IPAIngenuity Pathway Analysis
IRF2BP2Interferon regulatory factor 2 binding protein 2
KMT2BLysine methyltransferase 2B
KMT2Dlysine methyltransferase 2D
KPNB1Karyopherin subunit beta 1
KRASKRAS proto-oncogene, GTPase
LGSOCLow-grade serous ovarian cancer
LOHLoss of heterozygosity
MAPKMitogen-Activated Protein Kinase
MECOMMDS1 and EVI1 complex locus
MEDMediator Complex
MED14Mediator Complex Subunit 14
MED17Mediator Complex Subunit 17
MED18Mediator Complex Subunit 18
MED20Mediator Complex Subunit 20
MED21Mediator Complex Subunit 21
MED30Mediator Complex Subunit 30
MED4Mediator Complex Subunit 4
MED6Mediator Complex Subunit 6
MLH1MutL homolog 1
MMPMatrix metalloproteinase
MMRDNA mismatch repair
MOCMucinous ovarian cancers
Mre11MRE11 homolog, double strand break repair nuclease
MTORMammalian target of rapamycin
MTORC1Mammalian target of rapamycin complex 1
MUTYHMutY DNA glycosylase
MYCMYC proto-oncogene
NBS1Nijmegen Breakage Syndrome 1
NERNucleotide excision repair
NF1Neurofibromin 1
NOTCH4Notch Receptor 4
NRASNRAS proto-oncogene, GTPase
OCOvarian cancer
P16Cyclin-dependent kinase inhibitor 2A
P21Cyclin-dependent kinase inhibitor 1
P27Cyclin-dependent kinase inhibitor 1B
PALB2Partner and localizer of BRCA2
PARPPoly ADP ribose polymerase
PAX2Paired box gene 2
PAX8Paired box gene 8
PCNAProliferating cell nuclear antigen
PERKProtein kinase-like Endoplasmic Reticulum Kinase
PGRProgesterone receptor
PIK3C3Phosphatidylinositol 3-Kinase Catalytic Subunit Type 3
PI3KPhosphatidylinositol 3-kinase
PI3KCAPhosphatidylinositol 3-kinase catalytic alpha polypeptide
PLAUPlasminogen activator, urokinase
PLCPhospholipase C
PLK1Polo like kinase 1
POLEDNA polymerase epsilon, catalytic subunit
POT1Protection of telomeres 1
PP2R1AProtein phosphatase 2, regulatory subunit A
PPP1CBProtein Phosphatase 1 Catalytic Subunit Beta
PPP1R12AProtein Phosphatase 1 Regulatory Subunit 12A
PPP2R1AProtein Phosphatase 2 Scaffold Subunit Aalpha
PTENPhosphatase and tensin homolog
RabRab Family Small GTPase
RacRac Family Small GTPase
RAD1RAD1 checkpoint DNA exonuclease
RAD50RAD50 double strand break repair protein
RAD51RAD51 recombinase
RAD51CRAD51 paralog C
RAD51DRAD51 paralog D
RAD9Checkpoint Clamp Component A
RANRas-related nuclear protein
RAP1Ras-related protein 1
RASSF1geneRas Association Domain Family Member 1
RB1Retinoblastoma protein
RCF3Replication factor C subunit 3
RFCReplication factor C
RhoARas homolog family member A
RPAReplication Protein A
RPA3Replication Protein A3
RRAS2Ras-Related Protein R-Ras2
STK11Serine/threonine kinase 11
TCGAThe Cancer Genome Atlas
TERF1Telomeric Repeat Binding Factor 1
TERF2Telomeric Repeat Binding Factor 2
TFAP4Transcription Factor AP-4
TGFBITransforming Growth Factor Beta Induced
TIGARTP53 induced glycolysis and apoptosis regulator
TINF2TERF1 Interacting Nuclear Factor 2
TNFSF7Tumor Necrosis Factor Ligand Superfamily Member 7
TP53Tumor protein p53
TPP1Tripeptidyl peptidase 1
TRAM1Translocation associated membrane protein 1
TRAP1TNF receptor associated protein 1
UBL1Ubiquitin-like protein 1
UPRUnfolded protein response
VEGFVascular endothelial growth factor
WEE1WEE1 G2 checkpoint kinase
ZMYND8Zinc finger MYND-type containing 8
ZNF587BZinc finger protein 587B


  1. World Health Organization. Global Cancer Observatory GLOBOCAN. 2018. Available online: (accessed on 10 May 2020).
  2. Cancer Tomorrow Powered by GLOBOCAN. 2018. Available online: (accessed on 28 May 2020).
  3. Moufarrij, S.; Dandapani, M.; Arthofer, E.; Gomez, S.; Srivastava, A.; Lopez-Acevedo, M.; Villagra, A.; Chiappinelli, K.B. Epigenetic therapy for ovarian cancer: Promise and progress. Clin. Epigenetics 2019, 11, 7. [Google Scholar] [CrossRef] [PubMed]
  4. Pelucchi, C.; Galeone, C.; Talamini, R.; Bosetti, C.; Montella, M.; Negri, E.; Franceschi, S.; La Vecchia, C. Lifetime ovulatory cycles and ovarian cancer risk in 2 Italian case-control studies. Am. J. Obstet. Gynecol. 2007, 196, 83.e1–83.e7. [Google Scholar] [CrossRef] [PubMed]
  5. Whittemore, A.S.; Harris, R.; Ltnyre, J. Characteristics Relating to ovarian Cancer Risk: Collaborative Analysis of 12 US case-Control Studies. Am. J. Epidemiol. 1992, 136, 1212–1220. [Google Scholar] [CrossRef] [PubMed]
  6. Gong, T.-T.; Wu, Q.-J.; Vogtmann, E.; Lin, B.; Wang, Y.-L. Age at menarche and risk of ovarian cancer: A meta-analysis of epidemiological studies. Int. J. Cancer 2012, 132, 2894–2900. [Google Scholar] [CrossRef] [PubMed]
  7. Tsilidis, K.K.; Allen, N.; Key, T.J.; Dossus, L.; Lukanova, A.; Bakken, K.; Lund, E.; Fournier, A.; Overvad, K.; Hansen, L.; et al. Oral contraceptive use and reproductive factors and risk of ovarian cancer in the European Prospective Investigation into Cancer and Nutrition. Br. J. Cancer 2011, 105, 1436–1442. [Google Scholar] [CrossRef] [PubMed][Green Version]
  8. Gates, M.A.; Rosner, B.A.; Hecht, J.L.; Tworoger, S.S. Risk Factors for Epithelial Ovarian Cancer by Histologic Subtype. Am. J. Epidemiol. 2009, 171, 45–53. [Google Scholar] [CrossRef][Green Version]
  9. Walker, J.L.; Powell, C.B.; Chen, L.-M.; Carter, J.; Jump, V.L.B.; Parker, L.P.; Borowsky, M.E.; Gibb, R.K. Society of Gynecologic Oncology recommendations for the prevention of ovarian cancer. Cancer 2015, 121, 2108–2120. [Google Scholar] [CrossRef]
  10. Mallen, A.; Soong, T.R.; Townsend, M.K.; Wenham, R.M.; Crum, C.; Tworoger, S.S. Surgical prevention strategies in ovarian cancer. Gynecol. Oncol. 2018, 151, 166–175. [Google Scholar] [CrossRef]
  11. Slatnik, C.L.; Duff, E. Ovarian cancer. Nurse Pr. 2015, 40, 47–54. [Google Scholar] [CrossRef]
  12. Chien, J.; Poole, E.M. Ovarian Cancer Prevention, Screening, and Early Detection: Report from the 11th Biennial Ovarian Cancer Research Symposium. Int. J. Gynecol. Cancer 2017, 27, S20–S22. [Google Scholar] [CrossRef]
  13. Rasmussen, E.L.K.; Hannibal, C.G.; Dehlendorff, C.; Baandrup, L.; Junge, J.; Vang, R.; Kurman, R.J.; Kjær, S.K. Parity, infertility, oral contraceptives, and hormone replacement therapy and the risk of ovarian serous borderline tumors: A nationwide case-control study. Gynecol. Oncol. 2017, 144, 571–576. [Google Scholar] [CrossRef] [PubMed]
  14. Liu, Y.; Ma, L.; Yang, X.; Bie, J.; Li, D.; Sun, C.; Zhang, J.; Meng, Y.; Lin, J. Menopausal Hormone Replacement Therapy and the Risk of Ovarian Cancer: A Meta-Analysis. Front. Endocrinol. 2019, 10, 801. [Google Scholar] [CrossRef] [PubMed][Green Version]
  15. Chiaffarino, F.; Pelucchi, C.; Parazzini, F.; Negri, E.; Franceschi, S.; Talamini, R.; Conti, E.; Montella, M.; La Vecchia, C. Reproductive and hormonal factors and ovarian cancer. Ann. Oncol. 2001, 12, 337–341. [Google Scholar] [CrossRef] [PubMed]
  16. Gaitskell, K.; Green, J.; Pirie, K.; Barnes, I.; Hermon, C.; Reeves, G.K.; Beral, V. Histological subtypes of ovarian cancer associated with parity and breastfeeding in the prospective Million Women Study. Int. J. Cancer 2017, 142, 281–289. [Google Scholar] [CrossRef] [PubMed]
  17. Koushik, A.; Grundy, A.; Abrahamowicz, M.; Arseneau, J.; Gilbert, L.; Gotlieb, W.H.; Lacaille, J.; Mes-Masson, A.-M.; Parent, M.-E.; Provencher, D.; et al. Hormonal and reproductive factors and the risk of ovarian cancer. Cancer Causes Control 2017, 28, 393–403. [Google Scholar] [CrossRef]
  18. Luan, N.-N.; Wu, Q.-J.; Gong, T.-T.; Vogtmann, E.; Wang, Y.-L.; Lin, B. Breastfeeding and ovarian cancer risk: A meta-analysis of epidemiologic studies. Am. J. Clin. Nutr. 2013, 98, 1020–1031. [Google Scholar] [CrossRef] [PubMed][Green Version]
  19. Riman, T.; Nilsson, S.; Persson, I.R. Review of epidemiological evidence for reproductive and hormonal factors in relation to the risk of epithelial ovarian malignancies. Acta Obstet. Gynecol. Scand. 2004, 83, 783–795. [Google Scholar] [CrossRef]
  20. Li, D.-P.; Du, C.; Zhang, Z.-M.; Li, G.-X.; Yu, Z.-F.; Wang, X.; Li, P.; Cheng, C.; Liu, Y.-P.; Zhao, Y. Breastfeeding and Ovarian Cancer Risk: A Systematic Review and Meta-analysis of 40 Epidemiological Studies. Asian Pac. J. Cancer Prev. 2014, 15, 4829–4837. [Google Scholar] [CrossRef][Green Version]
  21. Risch, H.A.; Marrett, L.D.; Howe, G.R. Parity, Contraception, Infertility, and the Risk of Epithelial Ovarian Cancer. Am. J. Epidemiol. 1994, 140, 585–597. [Google Scholar] [CrossRef]
  22. Franceschi, S.; Parazzini, F.; Negri, E.; Booth, M.; La Vecchia, C.; Beral, V.; Tzonou, A.; Trichopoulos, D. Pooled analysis of 3 european case-control studies of epithelial ovarian cancer: III. Oral contraceptive use. Int. J. Cancer 1991, 49, 61–65. [Google Scholar] [CrossRef]
  23. Collaborative Group on Epidemiological Studies of Ovarian Cancer; Beral, V.; Doll, R.; Hermon, C.; Peto, R.; Reeves, G. Ovarian cancer and oral contraceptives: Collaborative reanalysis of data from 45 epidemiological studies including 23 257 women with ovarian cancer and 87 303 controls. Lancet 2008, 371, 303–314. [Google Scholar] [CrossRef][Green Version]
  24. Huang, Z.; Gao, Y.; Wen, W.; Li, H.; Zheng, W.; Shu, X.-O.; Beeghly-Fadiel, A. Contraceptive methods and ovarian cancer risk among Chinese women: A report from the Shanghai Women’s Health Study. Int. J. Cancer 2015, 137, 607–614. [Google Scholar] [CrossRef] [PubMed][Green Version]
  25. Collaborative Group on Epidemiological Studies of Ovarian Cancer; Beral, V.; Gaitskell, K.; Hermon, C.; Moser, K.; Reeves, G.; Peto, R. Menopausal hormone use and ovarian cancer risk: Individual participant meta-analysis of 52 epidemiological studies. Lancet 2015, 385, 1835–1842. [Google Scholar] [CrossRef][Green Version]
  26. Kurman, R.J.; Carcangiu, M.L.; Herrington, C.S.; Young, R.H. WHO Classification of Tumours of Female Reproductive Organs, 4th ed.; IARC: Lyon, France, 2014.
  27. Stewart, C.; Ralyea, C.; Lockwood, S. Ovarian Cancer: An Integrated Review. Semin. Oncol. Nurs. 2019, 35, 151–156. [Google Scholar] [CrossRef] [PubMed]
  28. Lisio, M.-A.; Fu, L.; Goyeneche, A.; Gao, Z.-H.; Telleria, C.M. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int. J. Mol. Sci. 2019, 20, 952. [Google Scholar] [CrossRef] [PubMed][Green Version]
  29. Collaborative Group on Epidemiological Studies of Ovarian Cancer; Beral, V.; Gaitskell, K.; Hermon, C.; Moser, K.; Reeves, G.; Peto, R. Ovarian cancer and smoking: Individual participant meta-analysis including 28 114 women with ovarian cancer from 51 epidemiological studies. Lancet Oncol. 2012, 13, 946–956. [Google Scholar] [CrossRef]
  30. Faber, M.T.; Kjær, S.K.; Dehlendorff, C.; Chang-Claude, J.; Andersen, K.K.; Høgdall, E.; Webb, P.M.; Jordan, S.J.; Australian Cancer Study (Ovarian Cancer); Australian Ovarian Cancer Study Group; et al. Cigarette smoking and risk of ovarian cancer: A pooled analysis of 21 case-control studies. Cancer Causes Control 2013, 24, 989–1004. [Google Scholar] [CrossRef][Green Version]
  31. Merritt, M.A.; Tzoulaki, I.; Brandt, P.A.V.D.; Schouten, L.J.; Tsilidis, K.K.; Weiderpass, E.; Patel, C.J.; Tjønneland, A.; Hansen, L.; Overvad, K.; et al. Nutrient-wide association study of 57 foods/nutrients and epithelial ovarian cancer in the European Prospective Investigation into Cancer and Nutrition study and the Netherlands Cohort Study. Am. J. Clin. Nutr. 2015, 103, 161–167. [Google Scholar] [CrossRef]
  32. Collaborative Group on Epidemiological Studies of Ovarian Cancer. Ovarian cancer and body size: Individual participant meta-analysis including 25,157 women with ovarian cancer from 47 epidemiological studies. PLoS Med. 2012, 9, e1001200. [Google Scholar] [CrossRef][Green Version]
  33. Reid, A.; De Klerk, N.; Musk, A.W. (Bill) Does Exposure to Asbestos Cause Ovarian Cancer? A Systematic Literature Review and Meta-analysis. Cancer Epidemiol. Biomarkers Prev. 2011, 20, 1287–1295. [Google Scholar] [CrossRef][Green Version]
  34. Olsen, C.M.; Bain, C.J.; Jordan, S.J.; Nagle, C.; Green, A.C.; Whiteman, D.C.; Webb, P.M. Australian Cancer Study (Ovarian Cancer) and Australian Ovarian Cancer Study Group Recreational Physical Activity and Epithelial Ovarian Cancer: A Case-Control Study, Systematic Review, and Meta-analysis. Cancer Epidemiol. Biomark. Prev. 2007, 16, 2321–2330. [Google Scholar] [CrossRef] [PubMed][Green Version]
  35. Walsh, T.; Casadei, S.; Lee, M.K.; Pennil, C.C.; Nord, A.S.; Thornton, A.M.; Roeb, W.; Agnew, K.J.; Stray, S.M.; Wickramanayake, A.; et al. Mutations in 12 genes for inherited ovarian, fallopian tube, and peritoneal carcinoma identified by massively parallel sequencing. Proc. Natl. Acad. Sci. USA 2011, 108, 18032–18037. [Google Scholar] [CrossRef] [PubMed][Green Version]
  36. Toss, A.; Tomasello, C.; Razzaboni, E.; Contu, G.; Grandi, G.; Cagnacci, A.; Schilder, R.J.; Cortesi, L. Hereditary Ovarian Cancer: Not OnlyBRCA1 and 2 Genes. Biomed. Res. Int. 2015, 2015, 1–11. [Google Scholar] [CrossRef] [PubMed][Green Version]
  37. Lynch, H.T.; Casey, M.J.; Snyder, C.L.; Bewtra, C.; Lynch, J.F.; Butts, M.; Godwin, A.K. Hereditary ovarian carcinoma: Heterogeneity, molecular genetics, pathology, and management. Mol. Oncol. 2009, 3, 97–137. [Google Scholar] [CrossRef]
  38. American Cancer Association. Available online: (accessed on 28 May 2020).
  39. Hart, T.; Chandrashekhar, M.; Aregger, M.; Steinhart, Z.; Brown, K.R.; MacLeod, G.; Mis, M.; Zimmermann, M.; Fradet-Turcotte, A.; Sun, S.; et al. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell 2015, 163, 1515–1526. [Google Scholar] [CrossRef][Green Version]
  40. Yu, J.; Yusa, K. Genome-wide CRISPR-Cas9 screening in mammalian cells. Methods 2019, 29–35. [Google Scholar] [CrossRef]
  41. Behan, F.M.; Iorio, F.; Picco, G.; Gonçalves, E.; Beaver, C.M.; Migliardi, G.; Santos, R.; Rao, Y.; Sassi, F.; Pinnelli, M.; et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 2019, 568, 511–516. [Google Scholar] [CrossRef]
  42. Matsuo, K.; Sheridan, T.B.; Mabuchi, S.; Yoshino, K.; Hasegawa, K.; Studeman, K.D.; Im, D.D.; Rosenshein, N.B.; Roman, L.D.; Sood, A.K. Estrogen receptor expression and increased risk of lymphovascular space invasion in high-grade serous ovarian carcinoma. Gynecol. Oncol. 2014, 133, 473–479. [Google Scholar] [CrossRef][Green Version]
  43. Andersen, C.; Sikora, M.J.; Boisen, M.M.; Ma, T.; Christie, A.; Tseng, G.; Park, Y.S.; Luthra, S.; Chandran, U.; Haluska, P.; et al. Active Estrogen Receptor-alpha Signaling in Ovarian Cancer Models and Clinical Specimens. Clin. Cancer Res. 2017, 23, 3802–3812. [Google Scholar] [CrossRef][Green Version]
  44. Paleari, L.; DeCensi, A. Endocrine therapy in ovarian cancer. Curr. Opin. Obstet. Gynecol. 2018, 30, 17–22. [Google Scholar] [CrossRef]
  45. Koshiyama, M.; Matsumura, N.; Konishi, I. Recent Concepts of Ovarian Carcinogenesis: Type I and Type II. BioMed Res. Int. 2014, 2014, 1–11. [Google Scholar] [CrossRef] [PubMed][Green Version]
  46. Kurman, R.J.; Shih, I.-M. Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer—Shifting the paradigm. Hum. Pathol. 2011, 42, 918–931. [Google Scholar] [CrossRef] [PubMed][Green Version]
  47. Santin, A.D.; Zhan, F.; Bellone, S.; Palmieri, M.; Canè, S.; Bignotti, E.; Anfossi, S.; Gokden, M.; Dunn, D.; Roman, J.J.; et al. Gene expression profiles in primary ovarian serous papillary tumors and normal ovarian epithelium: Identification of candidate molecular markers for ovarian cancer diagnosis and therapy. Int. J. Cancer 2004, 112, 14–25. [Google Scholar] [CrossRef] [PubMed]
  48. Salani, R.; Kurman, R.J.; Giuntoli, R.; Gardner, G.; Bristow, R.; Wang, T.-L.; Shih, I.-M. Assessment of TP53 mutation using purified tissue samples of ovarian serous carcinomas reveals a higher mutation rate than previously reported and does not correlate with drug resistance. Int. J. Gynecol. Cancer 2008, 18, 487–491. [Google Scholar] [CrossRef]
  49. Gockley, A.A.; Melamed, A.; Bregar, A.J.; Clemmer, J.T.; Birrer, M.; Schorge, J.O.; Del Carmen, M.G.; Rauh-Hain, J.A. Outcomes of Women With High-Grade and Low-Grade Advanced-Stage Serous Epithelial Ovarian Cancer. Obstet. Gynecol. 2017, 129, 439–447. [Google Scholar] [CrossRef] [PubMed][Green Version]
  50. Kurman, R.J. Origin and molecular pathogenesis of ovarian high-grade serous carcinoma. Ann. Oncol. 2013, 24, x16–x21. [Google Scholar] [CrossRef]
  51. Plaxe, S. Epidemiology of low-grade serous ovarian cancer. Am. J. Obstet. Gynecol. 2008, 198, 459.e1–459.e9. [Google Scholar] [CrossRef]
  52. Gilks, C.B. Subclassification of ovarian surface epithelial tumors based on correlation of histologic and molecular pathologic data. Int. J. Gynecol. Pathol. 2004, 23, 200–205. [Google Scholar] [CrossRef]
  53. Ceppi, L.; Birrer, M.J. Translational Advances in Gyneacologic Cancers, 1st ed.; Academic Press: London, UK, 2017. [Google Scholar]
  54. Hirst, J.; Crow, J.; Godwin, A.K. Ovarian Cancer Genetics: Subtypes and Risk Factors. Ovarian Cancer Pathog. Treat. 2018. [Google Scholar] [CrossRef][Green Version]
  55. Cho, K.R.; Shih, I.M. Ovarian cancer. Annu. Rev. Pathol. 2009, 4, 287–313. [Google Scholar] [CrossRef]
  56. Pierson, W.E.; Peters, P.N.; Chang, M.T.; Chen, L.-M.; Quigley, D.A.; Ashworth, A.; Chapman, J.S. An integrated molecular profile of endometrioid ovarian cancer. Gynecol. Oncol. 2020, 157, 55–61. [Google Scholar] [CrossRef] [PubMed]
  57. Serebrenik, A.A.; Argyris, P.P.; Jarvis, M.C.; Brown, W.L.; Bazzaro, M.; Vogel, R.I.; Erickson, B.K.; Lee, S.-H.; Goergen, K.M.; Maurer, M.J.; et al. The DNA Cytosine Deaminase APOBEC3B is a Molecular Determinant of Platinum Responsiveness in Clear Cell Ovarian Cancer. Clin. Cancer Res. 2020. [Google Scholar] [CrossRef] [PubMed][Green Version]
  58. Kandalaft, P.L.; Gown, A.M.; Isacson, C. The Lung-Restricted Marker Napsin A Is Highly Expressed in Clear Cell Carcinomas of the Ovary. Am. J. Clin. Pathol. 2014, 142, 830–836. [Google Scholar] [CrossRef] [PubMed]
  59. Domcke, S.; Sinha, R.; Levine, D.A.; Sander, C.; Schultz, N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nat. Commun. 2013, 4, 2126. [Google Scholar] [CrossRef] [PubMed]
  60. Zorn, K. Gene Expression Profiles of Serous, Endometrioid, and Clear Cell Subtypes of Ovarian and Endometrial Cancer. Clin. Cancer Res. 2005, 11, 6422–6430. [Google Scholar] [CrossRef][Green Version]
  61. Schwartz, D.R.; Kardia, S.L.R.; Shedden, K.; Kuick, R.; Michailidis, G.; Taylor, J.M.G.; Misek, D.; Wu, R.; Zhai, Y.; Darrah, D.M.; et al. Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas. Cancer Res. 2002, 62, 4722–4729. [Google Scholar]
  62. Su, Y.-F.; Tsai, E.-M.; Chen, C.-C.; Wu, C.-C.; Er, T.K. Targeted sequencing of a specific gene panel detects a high frequency of ARID1A and PIK3CA mutations in ovarian clear cell carcinoma. Clin. Chim. Acta 2019, 494, 1–7. [Google Scholar] [CrossRef]
  63. Babaier, A.; Ghatage, P. Mucinous Cancer of the Ovary: Overview and Current Status. Diagnostics 2020, 10, 52. [Google Scholar] [CrossRef][Green Version]
  64. Lenhard, M.; Tereza, L.; Heublein, S.; Ditsch, N.; Himsl, I.; Mayr, D.; Friese, D.M.K.; Jeschke, U. Steroid hormone receptor expression in ovarian cancer: Progesterone receptor B as prognostic marker for patient survival. BMC Cancer 2012, 12, 553. [Google Scholar] [CrossRef][Green Version]
  65. Smyth, J.F.; Gourley, C.; Walker, G.; MacKean, M.J.; Stevenson, A.; Williams, A.; Nafussi, A.A.; Rye, T.; Rye, R.; Stewart, M.; et al. Antiestrogen Therapy Is Active in Selected Ovarian Cancer Cases: The Use of Letrozole in Estrogen Receptor-Positive Patients. Clin. Cancer Res. 2007, 13, 3617–3622. [Google Scholar] [CrossRef][Green Version]
  66. Argenta, P.A.; Thomas, S.G.; Judson, P.L.; Downs, L.S.; Geller, M.A.; Carson, L.F.; Jonson, A.L.; Ghebre, R. A phase II study of fulvestrant in the treatment of multiply-recurrent epithelial ovarian cancer. Gynecol. Oncol. 2009, 113, 205–209. [Google Scholar] [CrossRef] [PubMed]
  67. Mangelsdorf, D.J.; Thummel, C.; Beato, M.; Herrlich, P.; Schütz, G.; Umesono, K.; Blumberg, B.; Kastner, P.; Mark, M.; Chambon, P.; et al. The Nuclear Receptor Superfamily: The Second Decade. Cell 1995, 83, 835–839. [Google Scholar] [CrossRef][Green Version]
  68. Charn, T.H.; Liu, E.T.-B.; Chang, E.C.; Lee, Y.K.; Katzenellenbogen, J.A.; Katzenellenbogen, B.S. Genome-wide dynamics of chromatin binding of estrogen receptors alpha and beta: Mutual restriction and competitive site selection. Mol. Endocrinol. 2009, 24, 47–59. [Google Scholar] [CrossRef][Green Version]
  69. Brandenberger, A.W.; Tee, M.K.; Jaffe, R.B. Estrogen Receptor Alpha (ER-?) and Beta (ER-?) mRNAs in Normal Ovary, Ovarian Serous Cystadenocarcinoma and Ovarian Cancer Cell Lines: Down-Regulation of ER-? in Neoplastic Tissues. J. Clin. Endocrinol. Metab. 1998, 83, 1025–1028. [Google Scholar] [CrossRef] [PubMed][Green Version]
  70. Tang, Z.-R.; Zhang, R.; Lian, Z.; Deng, S.-L.; Yu, K. Estrogen-Receptor Expression and Function in Female Reproductive Disease. Cells 2019, 8, 1123. [Google Scholar] [CrossRef][Green Version]
  71. Lazennec, G. Estrogen receptor beta, a possible tumor suppressor involved in ovarian carcinogenesis. Cancer Lett. 2006, 231, 151–157. [Google Scholar] [CrossRef][Green Version]
  72. Bossard, C.; Busson, M.; Vindrieux, D.; Gaudin, F.; Machelon, V.; Brigitte, M.; Jacquard, C.; Pillon, A.; Balaguer, P.; Balabanian, K.; et al. Potential Role of Estrogen Receptor Beta as a Tumor Suppressor of Epithelial Ovarian Cancer. PLoS ONE 2012, 7, e44787. [Google Scholar] [CrossRef][Green Version]
  73. Schlumbrecht, M.P.; Xie, S.S.; Shipley, G.L.; Urbauer, D.L.; Broaddus, R.R. Molecular clustering based on ERα and EIG121 predicts survival in high-grade serous carcinoma of the ovary/peritoneum. Mod. Pathol. 2011, 24, 453–462. [Google Scholar] [CrossRef][Green Version]
  74. Halon, A.; Materna, V.; Drag-Zalesinska, M.; Nowak-Markwitz, E.; Gansukh, T.; Donizy, P.; Spaczyński, M.; Zabel, M.; Dietel, M.; Lage, H.; et al. Estrogen Receptor Alpha Expression in Ovarian Cancer Predicts Longer Overall Survival. Pathol. Oncol. Res. 2011, 17, 511–518. [Google Scholar] [CrossRef][Green Version]
  75. Lannigan, D.A. Estrogen receptor phosphorylation. Steroids 2003, 68, 1–9. [Google Scholar] [CrossRef]
  76. Matsumura, S.; Ohta, T.; Yamanouchi, K.; Liu, Z.; Sudo, T.; Kojimahara, T.; Seino, M.; Narumi, M.; Tsutsumi, S.; Takahashi, T.; et al. Activation of estrogen receptor α by estradiol and cisplatin induces platinum-resistance in ovarian cancer cells. Cancer Boil. Ther. 2016, 18, 730–739. [Google Scholar] [CrossRef] [PubMed][Green Version]
  77. Kimura, A.; Ohmichi, M.; Kawagoe, J.; Kyo, S.; Mabuchi, S.; Takahashi, T.; Ohshima, C.; Arimoto-Ishida, E.; Nishio, Y.; Inoue, M.; et al. Induction of hTERT expression and phosphorylation by estrogen via Akt cascade in human ovarian cancer cell lines. Oncogene 2004, 23, 4505–4515. [Google Scholar] [CrossRef] [PubMed][Green Version]
  78. O’Donnell, A.; MacLeod, K.; Burns, D.J.; Smyth, J.F.; Langdon, S.P. Estrogen receptor-α mediates gene expression changes and growth response in ovarian cancer cells exposed to estrogen. Endocr. Relat. Cancer 2005, 12, 851–866. [Google Scholar] [CrossRef] [PubMed][Green Version]
  79. Wang, M.; Ma, H. Paired box gene 2 is associated with estrogen receptor α in ovarian serous tumors: Potential theory basis for targeted therapy. Mol. Clin. Oncol. 2016, 5, 323–326. [Google Scholar] [CrossRef][Green Version]
  80. Salvati, A.; Gigantino, V.; Nassa, G.; Giurato, G.; Alexandrova, E.; Rizzo, F.; Tarallo, R.; Weisz, A. The Histone Methyltransferase DOT1L Is a Functional Component of Estrogen Receptor Alpha Signaling in Ovarian Cancer Cells. Cancers 2019, 11, 1720. [Google Scholar] [CrossRef] [PubMed][Green Version]
  81. Park, S.-H.; Cheung, L.W.T.; Wong, A.S.T.; Leung, P.C.K. Estrogen Regulates Snail and Slug in the Down-Regulation of E-Cadherin and Induces Metastatic Potential of Ovarian Cancer Cells through Estrogen Receptor α. Mol. Endocrinol. 2008, 22, 2085–2098. [Google Scholar] [CrossRef]
  82. Liu, Y.; Sun, C.; Mo, Y.; Liu, H.; Zhai, S.; Zhou, H. ERα and ERβ oppositely regulated plexin B1 expression and migration of ovarian cancer SKOV-3 cells. Int. J. Clin. Exp. Med. 2018, 11, 3484–3493. [Google Scholar]
  83. Benhadjeba, S.; Edjekouane, L.; Sauvé, K.; Carmona, E.; Tremblay, A. Feedback control of the CXCR7/CXCL11 chemokine axis by estrogen receptor α in ovarian cancer. Mol. Oncol. 2018, 12, 1689–1705. [Google Scholar] [CrossRef][Green Version]
  84. Zheng, J.; Zhou, J.; Xie, X.; Xie, B.; Lin, J.; Xu, Z.; Zhang, W. Estrogen Decreases Anoikis of Ovarian Cancer Cell Line Caov-3 Through Reducing Release of Bit1. DNA Cell Boil. 2014, 33, 847–853. [Google Scholar] [CrossRef]
  85. Kodama, M.; Kodama, T.; Newberg, J.Y.; Katayama, H.; Kobayashi, M.; Hanash, S.M.; Yoshihara, K.; Wei, Z.; Tien, J.C.; Rangel, R.; et al. In vivo loss-of-function screens identify KPNB1 as a new druggable oncogene in epithelial ovarian cancer. Proc. Natl. Acad. Sci. USA 2017, 114, E7301–E7310. [Google Scholar] [CrossRef][Green Version]
  86. He, Y.; Meghani, K.; Caron, M.-C.; Yang, C.; Ronato, D.A.; Bian, J.; Sharma, A.; Moore, J.; Niraj, J.; Detappe, A.; et al. DYNLL1 binds to MRE11 to limit DNA end resection in BRCA1-deficient cells. Nature 2018, 563, 522–526. [Google Scholar] [CrossRef] [PubMed]
  87. Fang, P.; De Souza, C.; Minn, K.; Chien, J. Genome-scale CRISPR knockout screen identifies TIGAR as a modifier of PARP inhibitor sensitivity. Commun. Boil. 2019, 2, 335. [Google Scholar] [CrossRef][Green Version]
  88. Ouyang, Q.; Liu, Y.; Tan, J.; Li, J.; Yang, D.; Zeng, F.; Huang, W.; Kong, Y.; Liu, Z.; Zhou, H.; et al. Loss of ZNF587B and SULF1 contributed to cisplatin resistance in ovarian cancer cell lines based on Genome-scale CRISPR/Cas9 screening. Am. J. Cancer Res. 2019, 9, 988–998. [Google Scholar] [PubMed]
  89. Stover, E.H.; Baco, M.B.; Cohen, O.; Li, Y.Y.; Christie, E.; Bagul, M.; Goodale, A.; Lee, Y.; Pantel, S.; Rees, M.G.; et al. Pooled Genomic Screens Identify Anti-apoptotic Genes as Targetable Mediators of Chemotherapy Resistance in Ovarian Cancer. Mol. Cancer Res. 2019, 17, 2281–2293. [Google Scholar] [CrossRef] [PubMed][Green Version]
  90. Meyers, R.; Bryan, J.G.; McFarland, J.M.; Weir, B.A.; Sizemore, A.E.; Xu, H.; Dharia, N.V.; Montgomery, P.G.; Cowley, G.S.; Pantel, S.; et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 2017, 49, 1779–1784. [Google Scholar] [CrossRef] [PubMed][Green Version]
  91. Dempster, D.J.; Jordan, R.; Mariya, K.; Pan, J.; Guillaume, K.; Root., E.D.; Aviad, T. Extracting Biological Insights from the Project Achilles Genome-Scale CRISPR Screens in Cancer Cell Lines. bioRxiv 2020. [Google Scholar] [CrossRef]
  92. Broad Institute. DepMap Portal. Available online: (accessed on 10 May 2020).
  93. Sanger Institute. Cancer Dependency Map Project Score. Available online: (accessed on 10 May 2020).
  94. Blayney, J.K.; Davison, T.; McCabe, N.; Walker, S.; Keating, K.; Delaney, T.; Greenan, C.; Williams, A.R.; McCluggage, W.G.; Capes-Davis, A.; et al. Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line. Nucleic Acids Res. 2016, 44, e137. [Google Scholar] [CrossRef][Green Version]
  95. Mirza-Aghazadeh-Attari, M.; Ostadian, C.; Saei, A.A.; Mihanfar, A.; Darband, S.G.; Sadighparvar, S.; Kaviani, M.; Kafil, H.S.; Yousefi, B.; Majidinia, M.; et al. DNA damage response and repair in ovarian cancer: Potential targets for therapeutic strategies. DNA Repair 2019, 80, 59–84. [Google Scholar] [CrossRef]
  96. Nam, E.J.; Kim, Y.T. Alteration of cell-cycle regulation in epithelial ovarian cancer. Int. J. Gynecol. Cancer 2008, 18, 1169–1182. [Google Scholar] [CrossRef]
  97. The Cancer Genome Atlas Research Network Integrated genomic analyses of ovarian carcinoma. Nature 2011, 474, 609–615. [CrossRef]
  98. Sanabria, A.L.A.; Jiménez, C.N.; Sánchez, V.C.; Serrano-Oviedo, L.; Andrés-Pretel, F.; Burgos, M.; Galán-Moya, E.M.; Montero, J.C.; Llopis, J.; Pandiella, A.; et al. Synthetic Lethality Interaction Between Aurora Kinases and CHEK1 Inhibitors in Ovarian Cancer. Mol. Cancer Ther. 2017, 16, 2552–2562. [Google Scholar] [CrossRef] [PubMed][Green Version]
  99. Slipicevic, A.; Holth, A.; Hellesylt, E.; Trope, C.G.; Davidson, B.; Flørenes, V.A. Wee1 is a novel independent prognostic marker of poor survival in post-chemotherapy ovarian carcinoma effusions. Gynecol. Oncol. 2014, 135, 118–124. [Google Scholar] [CrossRef] [PubMed]
  100. Schmid, B.C.; Oehler, M.K. New perspectives in ovarian cancer treatment. Maturitas 2014, 77, 128–136. [Google Scholar] [CrossRef] [PubMed]
  101. Shen, H.; Xu, J.; Zhao, S.; Shi, H.; Yao, S.; Jiang, N. ShRNA-mediated silencing of the RFC3 gene suppress ovarian tumor cells proliferation. Int. J. Clin. Exp. Pathol. 2015, 8, 8968–8975. [Google Scholar] [PubMed]
  102. Abdel-Fatah, T.M.; Arora, A.; Moseley, P.; Coveney, C.; Perry, C.; Johnson, K.; Kent, C.; Ball, G.; Chan, S.; Madhusudan, S. ATM, ATR and DNA-PKcs expressions correlate to adverse clinical outcomes in epithelial ovarian cancers. BBA Clin. 2014, 2, 10–17. [Google Scholar] [CrossRef] [PubMed][Green Version]
  103. Kim, H.; George, E.; Ragland, R.L.; Rafail, S.; Zhang, R.; Krepler, C.; Morgan, M.A.; Herlyn, M.; Brown, E.J.; Simpkins, F.; et al. Targeting the ATR/CHK1 Axis with PARP Inhibition Results in Tumor Regression in BRCA-Mutant Ovarian Cancer Models. Clin. Cancer Res. 2016, 23, 3097–3108. [Google Scholar] [CrossRef][Green Version]
  104. Gralewska, P.; Gajek, A.; Marczak, A.; Rogalska, A. Participation of the ATR/CHK1 pathway in replicative stress targeted therapy of high-grade ovarian cancer. J. Hematol. Oncol. 2020, 13, 1–16. [Google Scholar] [CrossRef] [PubMed][Green Version]
  105. De La Torre, J.; Gil-Moreno, A.; García, Á.; Rojo, F.; Xercavins, J.; Salido, E.; Freire, R. Expression of DNA Damage Checkpoint Protein Hus1 in Epithelial Ovarian Tumors Correlates With Prognostic Markers. Int. J. Gynecol. Pathol. 2008, 27, 24–32. [Google Scholar] [CrossRef] [PubMed]
  106. Lopes, J.L.; Chaudhry, S.; Lopes, G.S.; Levin, N.K.; Tainsky, M. FANCM, RAD1, CHEK1 and TP53I3 act as BRCA-like tumor suppressors and are mutated in hereditary ovarian cancer. Cancer Genet. 2019, 57–64. [Google Scholar] [CrossRef]
  107. Dudas, A.; Chovanec, M. DNA double-strand break repair by homologous recombination. Mutat. Res. 2004, 566, 131–167. [Google Scholar] [CrossRef]
  108. Li, X.; Heyer, W.-D. Homologous recombination in DNA repair and DNA damage tolerance. Cell Res. 2008, 18, 99–113. [Google Scholar] [CrossRef] [PubMed][Green Version]
  109. Song, H.; Dicks, E.; Ramus, S.; Tyrer, J.P.; Intermaggio, M.P.; Hayward, J.; Edlund, C.K.; Conti, D.; Harrington, P.; Fraser, L.; et al. Contribution of Germline Mutations in the RAD51B, RAD51C, and RAD51D Genes to Ovarian Cancer in the Population. J. Clin. Oncol. 2015, 33, 2901–2907. [Google Scholar] [CrossRef][Green Version]
  110. Malisic, E.; Krivokuća, A.; Boljevic, I.; Janković, R. Impact of RAD51 G135C and XRCC1 Arg399Gln polymorphisms on ovarian carcinoma risk in Serbian women. Cancer Biomark. 2015, 15, 685–691. [Google Scholar] [CrossRef] [PubMed]
  111. Fortier, E.A.; Drobetsky, E.; Wurtele, H. Know your limits: RPA availability and chemoresistance in ovarian cancer. Oncotarget 2019, 10, 800–802. [Google Scholar] [CrossRef] [PubMed]
  112. Chernikova, S.B.; Game, J.C.; Brown, J.M. Inhibiting homologous recombination for cancer therapy. Cancer Biol. Ther. 2012, 13, 61–68. [Google Scholar] [CrossRef][Green Version]
  113. Kopa, P.; Macieja, A.; Galita, G.; Witczak, Z.J.; Poplawski, T. DNA Double Strand Breaks Repair Inhibitors: Relevance as Potential New Anticancer Therapeutics. Curr. Med. Chem. 2019, 26, 1483–1493. [Google Scholar] [CrossRef]
  114. Zhao, M.; Li, S.; Zhou, L.; Shen, Q.; Zhu, H.; Zhu, X. Prognostic values of excision repair cross-complementing genes mRNA expression in ovarian cancer patients. Life Sci. 2018, 194, 34–39. [Google Scholar] [CrossRef]
  115. Zhu, Q.; Zhang, J.; Chen, Y.; Hu, Q.; Shen, H.; Huang, R.; Liu, Q.; Kaur, J.; Long, M.D.; Battaglia, S.; et al. Whole-exome sequencing of ovarian cancer families uncovers putative predisposition genes. Int. J. Cancer 2019, 146, 2147–2155. [Google Scholar] [CrossRef]
  116. Wu, M.; Sun, Y.; Wu, J.; Liu, G. Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis. Med. Sci. Monit. 2020, 26, e92210-1. [Google Scholar] [CrossRef]
  117. Samanta, S.; Tamura, S.; Dubeau, L.; Mhawech-Fauceglia, P.; Miyagi, Y.; Kato, H.; Lieberman, R.; Buckanovich, R.J.; Lin, Y.G.; Neamati, N. Clinicopathological significance of endoplasmic reticulum stress proteins in ovarian carcinoma. Sci. Rep. 2020, 10, 1–12. [Google Scholar] [CrossRef] [PubMed]
  118. Sengupta, S.; Sevigny, C.; Bhattacharya, P.; Jordan, V.C.; Clarke, R. Estrogen-Induced Apoptosis in Breast Cancers Is Phenocopied by Blocking Dephosphorylation of Eukaryotic Initiation Factor 2 Alpha (eIF2α) Protein. Mol. Cancer Res. 2019, 17, 918–928. [Google Scholar] [CrossRef]
  119. Weis, S.M.A.; Cheresh, D. Tumor angiogenesis: Molecular pathways and therapeutic targets. Nat. Med. 2011, 17, 1359–1370. [Google Scholar] [CrossRef] [PubMed]
  120. Spannuth, W.A.; Nick, A.M.; Jennings, N.B.; Armaiz-Pena, G.N.; Mangala, L.S.; Danes, C.G.; Lin, Y.G.; Merritt, W.M.; Thaker, P.H.; Kamat, A.A.; et al. Functional significance of VEGFR-2 on ovarian cancer cells. Int. J. Cancer 2009, 124, 1045–1053. [Google Scholar] [CrossRef] [PubMed][Green Version]
  121. Kalli, K.R.; Falowo, O.I.; Bale, L.K.; Zschunke, M.A.; Roche, P.C.; Conover, C.A. Functional Insulin Receptors on Human Epithelial Ovarian Carcinoma Cells: Implications for IGF-II Mitogenic Signaling. Endocrinology 2002, 143, 3259–3267. [Google Scholar] [CrossRef] [PubMed]
  122. Liefers-Visser, J.; Meijering, R.; Reyners, A.; Van Der Zee, A.; De Jong, S. IGF system targeted therapy: Therapeutic opportunities for ovarian cancer. Cancer Treat. Rev. 2017, 60, 90–99. [Google Scholar] [CrossRef][Green Version]
  123. Ghoneum, A.; Said, N. PI3K-AKT-mTOR and NFκB Pathways in Ovarian Cancer: Implications for Targeted Therapeutics. Cancers 2019, 11, 949. [Google Scholar] [CrossRef][Green Version]
  124. Lau, M.-T.; Leung, P.C.K. The PI3K/Akt/mTOR signaling pathway mediates insulin-like growth factor 1-induced E-cadherin down-regulation and cell proliferation in ovarian cancer cells. Cancer Lett. 2012, 326, 191–198. [Google Scholar] [CrossRef]
  125. Cáceres-Gorriti, K.Y.; Carmona, E.; Barrès, V.; Rahimi, K.; Létourneau, I.J.; Tonin, P.N.; Provencher, D.; Mes-Masson, A.-M. RAN Nucleo-Cytoplasmic Transport and Mitotic Spindle Assembly Partners XPO7 and TPX2 Are New Prognostic Biomarkers in Serous Epithelial Ovarian Cancer. PLoS ONE 2014, 9, e91000. [Google Scholar] [CrossRef]
  126. Barrès, V.; Ouellet, V.; Lafontaine, J.; Tonin, P.; Provencher, D.; Mes-Masson, A.-M. An essential role for Ran GTPase in epithelial ovarian cancer cell survival. Mol. Cancer 2010, 9, 272. [Google Scholar] [CrossRef][Green Version]
  127. Lorenzato, A.; Biolatti, M.; Delogu, G.; Capobianco, G.; Farace, C.; Dessole, S.; Cossu, A.G.M.; Tanda, F.; Madeddu, R.; Olivero, M.; et al. AKT activation drives the nuclear localization of CSE1L and a pro-oncogenic transcriptional activation in ovarian cancer cells. Exp. Cell Res. 2013, 319, 2627–2636. [Google Scholar] [CrossRef]
  128. Zaoui, K.; Boudhraa, Z.; Khalifé, P.; Carmona, E.; Provencher, D.; Mes-Masson, A.-M. Ran promotes membrane targeting and stabilization of RhoA to orchestrate ovarian cancer cell invasion. Nat. Commun. 2019, 10, 2666. [Google Scholar] [CrossRef] [PubMed][Green Version]
  129. Moldovan, G.-L.; Pfander, B.; Jentsch, S. PCNA, the Maestro of the Replication Fork. Cell 2007, 129, 665–679. [Google Scholar] [CrossRef] [PubMed][Green Version]
  130. Liao, X.-H.; Lu, D.-L.; Wang, N.; Liu, L.-Y.; Wang, Y.; Li, Y.-Q.; Yan, T.-B.; Sun, X.-G.; Hu, P.; Zhang, T.-C. Estrogen receptor α mediates proliferation of breast cancer MCF-7 cells via a p21/PCNA/E2F1-dependent pathway. FEBS J. 2014, 281, 927–942. [Google Scholar] [CrossRef] [PubMed][Green Version]
  131. Thomas, H.; Nasim, M.; Sarraf, C.; Alison, M.; Love, S.; Lambert, H.; Price, P. Proliferating cell nuclear antigen (PCNA) immunostaining—A prognostic factor in ovarian cancer? Br. J. Cancer 1995, 71, 357–362. [Google Scholar] [CrossRef][Green Version]
  132. Cerami, E.; Gao, J.; Dogrusoz, U.; Gross, B.E.; Sumer, S.O.; Aksoy, B.A.; Skanderup, A.J.; Byrne, C.J.; Heuer, M.L.; Larsson, E.; et al. The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012, 2, 401-4. [Google Scholar] [CrossRef][Green Version]
  133. Gao, J.; Aksoy, B.A.; Dogrusoz, U.; Dresdner, G.; Gross, B.; Sumer, S.O.; Sun, Y.; Skanderup, A.J.; Sinha, R.; Larsson, E.; et al. Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci. Signal. 2013, 6, pl1. [Google Scholar] [CrossRef][Green Version]
  134. cBioPortal for Cancer Genomics. Available online: (accessed on 10 May 2020).
  135. Fallah, Y.; Brundage, J.; Allegakoen, P.; Shajahan-Haq, A. MYC-Driven Pathways in Breast Cancer Subtypes. Biomolecules 2017, 7, 53. [Google Scholar] [CrossRef]
  136. Goldstein, J.L.; Brown, M.S. Regulation of the mevalonate pathway. Nature 1990, 343, 425–430. [Google Scholar] [CrossRef]
  137. Konstantinopoulos, P.A.; Karamouzis, M.V.; Papavassiliou, A.G. Post-translational modifications and regulation of the RAS superfamily of GTPases as anticancer targets. Nat. Rev. Drug Discov. 2007, 6, 541–555. [Google Scholar] [CrossRef]
  138. Cheng, K.W.; Agarwal, R.; Mills, G.B. Ras-Superfamily GTP-ases in Ovarian Cancer. Cancer Treat. Res. 2009, 149, 229–240. [Google Scholar] [CrossRef]
  139. Brennan, D.; Brändstedt, J.; Rexhepaj, E.; Foley, M.E.; Pontén, F.; Uhlén, M.; Gallagher, W.; O’Connor, D.; O’Herlihy, C.; Jirström, K. Tumour-specific HMG-CoAR is an independent predictor of recurrence free survival in epithelial ovarian cancer. BMC Cancer 2010, 10, 125. [Google Scholar] [CrossRef] [PubMed][Green Version]
  140. Friedman, G.D.; Flick, E.D.; Udaltsova, N.; Chan Pharm, D.J.; Quesenberry, C.P.; Habel, L.A. Screening statins for possible carcinogenic risk: Up to 9 years of follow-up of 361 859 recipients. Pharmacoepidemiol. Drug Saf. 2007, 17, 27–36. [Google Scholar] [CrossRef] [PubMed]
  141. Yu, O.; Boudreau, D.M.; Buist, D.S.M.; Miglioretti, D.L. Statin use and female reproductive organ cancer risk in a large population-based setting. Cancer Causes Control 2008, 20, 609–616. [Google Scholar] [CrossRef] [PubMed][Green Version]
  142. Liu, Y.; Qin, A.; Li, T.; Qin, X.; Li, S. Effect of statin on risk of gynecologic cancers: A meta-analysis of observational studies and randomized controlled trials. Gynecol. Oncol. 2014, 133, 647–655. [Google Scholar] [CrossRef] [PubMed]
  143. Stine, J.E.; Guo, H.; Sheng, X.; Han, X.; Schointuch, M.N.; Gilliam, T.P.; Gehrig, P.A.; Zhou, C.; Bae-Jump, V. The HMG-CoA reductase inhibitor, simvastatin, exhibits anti-metastatic and anti-tumorigenic effects in ovarian cancer. Oncotarget 2015, 7, 946–960. [Google Scholar] [CrossRef]
  144. Martirosyan, A.; Clendening, J.W.A.; Goard, C.; Penn, L.Z. Lovastatin induces apoptosis of ovarian cancer cells and synergizes with doxorubicin: Potential therapeutic relevance. BMC Cancer 2010, 10, 103. [Google Scholar] [CrossRef][Green Version]
  145. Robinson, E.; Nandi, M.; Wilkinson, L.L.; Arrowsmith, D.M.; Curtis, A.; Richardson, A. Preclinical evaluation of statins as a treatment for ovarian cancer. Gynecol. Oncol. 2013, 129, 417–424. [Google Scholar] [CrossRef]
  146. Kobayashi, Y.; Kashima, H.; Wu, R.-C.; Jung, J.-G.; Kuan, J.-C.; Gu, J.; Xuan, J.; Sokoll, L.; Visvanathan, K.; Shih, I.-M.; et al. Mevalonate Pathway Antagonist Suppresses Formation of Serous Tubal Intraepithelial Carcinoma and Ovarian Carcinoma in Mouse Models. Clin. Cancer Res. 2015, 21, 4652–4662. [Google Scholar] [CrossRef][Green Version]
  147. Greenaway, J.B.; Virtanen, C.; Osz, K.; Revay, T.; Hardy, D.; Shepherd, T.; DiMattia, G.; Petrik, J. Ovarian tumour growth is characterized by mevalonate pathway gene signature in an orthotopic, syngeneic model of epithelial ovarian cancer. Oncotarget 2016, 7, 47343–47365. [Google Scholar] [CrossRef][Green Version]
  148. Kobayashi, Y.; Kashima, H.; Rahmanto, Y.S.; Banno, K.; Yu, Y.; Matoba, Y.; Watanabe, K.; Iijima, M.; Takeda, T.; Kunitomi, H.; et al. Drug repositioning of mevalonate pathway inhibitors as antitumor agents for ovarian cancer. Oncotarget 2017, 8, 72147–72156. [Google Scholar] [CrossRef][Green Version]
  149. Abdullah, M.I.; Abed, M.N.; Richardson, A. Inhibition of the mevalonate pathway augments the activity of pitavastatin against ovarian cancer cells. Sci. Rep. 2017, 7, 8090. [Google Scholar] [CrossRef] [PubMed]
  150. Blackburn, E.; Epel, E.; Lin, J. Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection. Science 2015, 350, 1193–1198. [Google Scholar] [CrossRef] [PubMed][Green Version]
  151. Xin, H.; Liu, D.; Songyang, Z. The telosome/shelterin complex and its functions. Genome Biol. 2008, 9, 232. [Google Scholar] [CrossRef] [PubMed]
  152. Counter, C.M.; Hirte, H.W.; Bacchetti, S.; Harley, C.B. Telomerase activity in human ovarian carcinoma. Proc. Natl. Acad. Sci. USA 1994, 91, 2900–2904. [Google Scholar] [CrossRef] [PubMed][Green Version]
  153. Huda, N.; Xu, Y.; Bates, A.M.; Rankin, D.A.; Kannan, N.; Gilley, D. Onset of Telomere Dysfunction and Fusions in Human Ovarian Carcinoma. Cells 2019, 8, 414. [Google Scholar] [CrossRef][Green Version]
  154. Kotsopoulos, J.; Prescott, J.; De Vivo, I.; Fan, I.; McLaughlin, J.; Rosen, B.; Risch, H.; Sun, P.A.; Narod, S. Telomere length and mortality following a diagnosis of ovarian cancer. Cancer Epidemiol. Biomark. Prev. 2014, 23, 2603–2606. [Google Scholar] [CrossRef] [PubMed][Green Version]
  155. Sun, Y.; Tao, W.; Huang, M.; Wu, X.; Gu, J. Genetic variants in telomere-maintenance genes are associated with ovarian cancer risk and outcome. J. Cell. Mol. Med. 2016, 21, 510–518. [Google Scholar] [CrossRef]
  156. Martínez-Delgado, B.; Yanowsky, K.; Inglada, L.; De La Hoya, M.; Caldes, T.; Vega, A.; Blanco, A.; Martin, T.; González-Sarmiento, R.; Blasco, M.A.; et al. Shorter telomere length is associated with increased ovarian cancer risk in both familial and sporadic cases. J. Med. Genet. 2012, 49, 341–344. [Google Scholar] [CrossRef]
  157. Kunifuji, Y.; Gotoh, S.; Abe, T.; Miura, M.; Karasaki, Y. Down-regulation of telomerase activity by anticancer drugs in human ovarian cancer cells. Anti-Cancer Drugs 2002, 13, 595–598. [Google Scholar] [CrossRef]
  158. Shay, J.; Bacchetti, S. A survey of telomerase activity in human cancer. Eur. J. Cancer 1997, 33, 787–791. [Google Scholar] [CrossRef]
  159. Patel, K.P.; Vonderheide, R.H. Telomerase as a tumor-associated antigen for cancer immunotherapy. Cytotechnology 2004, 45, 91–99. [Google Scholar] [CrossRef] [PubMed][Green Version]
  160. Saretzki, G. Telomerase inhibition as cancer therapy. Cancer Lett. 2003, 194, 209–219. [Google Scholar] [CrossRef]
  161. Chen, M.; Xing, L.-N. siRNA-mediated inhibition of hTERC enhances radiosensitivity of cervical cancer. Asian Pac. J. Cancer Prev. 2012, 13, 5975–5979. [Google Scholar] [CrossRef] [PubMed][Green Version]
  162. Bidzinska, J.; Baginski, M.; Skladanowski, A. Novel anticancer strategy aimed at targeting shelterin complexes by the induction of structural changes in telomeric DNA: Hitting two birds with one stone. Curr. Cancer Drug Targets 2014, 14, 201–216. [Google Scholar] [CrossRef]
  163. Bejarano, L.; Schuhmacher, A.J.; Méndez, M.; Megias, D.; Blanco-Aparicio, C.; Martínez, S.; Pastor, J.; Squatrito, M.; Blasco, M.A. Inhibition of TRF1 Telomere Protein Impairs Tumor Initiation and Progression in Glioblastoma Mouse Models and Patient-Derived Xenografts. Cancer Cell 2017, 32, 590–607.e4. [Google Scholar] [CrossRef][Green Version]
  164. Lanzetti, L.; Di Fiore, P.P. Endocytosis and Cancer: An ‘Insider’ Network with Dangerous Liaisons. Traffic 2008, 9, 2011–2021. [Google Scholar] [CrossRef]
  165. Arakel, E.C.; Schwappach, B. Formation of COPI-coated vesicles at a glance. J. Cell Sci. 2018, 131, jcs209890. [Google Scholar] [CrossRef] [PubMed][Green Version]
  166. Claerhout, S.; Dutta, B.; Bossuyt, W.; Zhang, F.; Nguyen-Charles, C.; Dennison, J.B.; Yu, Q.; Yu, S.; Balazsi, G.; Lu, Y.; et al. Abortive Autophagy Induces Endoplasmic Reticulum Stress and Cell Death in Cancer Cells. PLoS ONE 2012, 7, e39400. [Google Scholar] [CrossRef][Green Version]
  167. Shtutman, M.; Roninson, I.B. A subunit of coatomer protein complex offers a novel tumor-specific target through a surprising mechanism. Autophagy 2011, 7, 1551–1552. [Google Scholar] [CrossRef][Green Version]
Figure 1. Network reconstruction analysis and functional enrichment of ovarian cancer fitness genes representative of DNA Damage Response (DDR) mechanisms, performed using GeneMANIA ( (a) Nucleotide excision repair (NER) mechanism. (b) DNA mismatch repair mechanism. (c) Homologous recombination (HR) mechanism. Protein–protein physical interactions between fitness genes in OC (internal shell) and related genes connected to them (external shell) are shown. Each node represents a gene and the edge width is proportional to the strength of the interactions. Gene association to biological processes is represented with a color code (legends).
Figure 1. Network reconstruction analysis and functional enrichment of ovarian cancer fitness genes representative of DNA Damage Response (DDR) mechanisms, performed using GeneMANIA ( (a) Nucleotide excision repair (NER) mechanism. (b) DNA mismatch repair mechanism. (c) Homologous recombination (HR) mechanism. Protein–protein physical interactions between fitness genes in OC (internal shell) and related genes connected to them (external shell) are shown. Each node represents a gene and the edge width is proportional to the strength of the interactions. Gene association to biological processes is represented with a color code (legends).
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Figure 2. Network reconstruction analysis and functional enrichment of ovarian cancer fitness genes representative of the VEGF signaling, performed using GeneMANIA ( Protein–protein physical interactions between fitness genes in OC (internal shell) and related genes connected to them (external shell) are shown. Each node represents a gene and the edge width is proportional to the strength of interactions. Gene association to biological processes is represented with a color code (legend).
Figure 2. Network reconstruction analysis and functional enrichment of ovarian cancer fitness genes representative of the VEGF signaling, performed using GeneMANIA ( Protein–protein physical interactions between fitness genes in OC (internal shell) and related genes connected to them (external shell) are shown. Each node represents a gene and the edge width is proportional to the strength of interactions. Gene association to biological processes is represented with a color code (legend).
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Figure 3. Network reconstruction analysis and functional enrichment of ovarian cancer fitness genes representative of proliferative signaling, performed using GeneMANIA ( (a) Insulin receptor related genes. (b) Ran signaling related genes. Protein–protein physical interactions between fitness genes in OC (internal shell) and related genes connected to them (external shell) are shown. Each node represents a gene and the edge width is proportional to the strength of interactions. Gene association to biological processes is represented with a color code (legend). Abbreviation: Ran, Ras-related Nuclear protein.
Figure 3. Network reconstruction analysis and functional enrichment of ovarian cancer fitness genes representative of proliferative signaling, performed using GeneMANIA ( (a) Insulin receptor related genes. (b) Ran signaling related genes. Protein–protein physical interactions between fitness genes in OC (internal shell) and related genes connected to them (external shell) are shown. Each node represents a gene and the edge width is proportional to the strength of interactions. Gene association to biological processes is represented with a color code (legend). Abbreviation: Ran, Ras-related Nuclear protein.
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Figure 4. Network reconstruction analysis and functional enrichment of ovarian cancer fitness genes representative of the endoplasmic reticulum (ER)-related pathways, performed using GeneMANIA ( (a) Estrogen receptor signaling related genes. (b) Estrogen-mediated S-phase Entry related genes. Protein–protein physical interactions between fitness genes in OC (internal shell) and related genes connected to them (external shell) are shown. Each node represents a gene and the edge width is proportional to the strength of interactions. Gene association to biological processes is represented with a color code (legends). ESR1 has been inserted among the pathway-related genes to show fitness genes physically interacting with it.
Figure 4. Network reconstruction analysis and functional enrichment of ovarian cancer fitness genes representative of the endoplasmic reticulum (ER)-related pathways, performed using GeneMANIA ( (a) Estrogen receptor signaling related genes. (b) Estrogen-mediated S-phase Entry related genes. Protein–protein physical interactions between fitness genes in OC (internal shell) and related genes connected to them (external shell) are shown. Each node represents a gene and the edge width is proportional to the strength of interactions. Gene association to biological processes is represented with a color code (legends). ESR1 has been inserted among the pathway-related genes to show fitness genes physically interacting with it.
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Figure 5. OncoPrint representation of the molecular landscape for ERα-associated fitness genes in ovarian cancer tissues collected in The Cancer Genome Atlas (TCGA), according to study of origin (Firehose Legacy, Nature 2011 and PanCancer Atlas). The OncoPrint provides an overview of genomic alterations (legend) per each gene across ovarian cancer cohorts (rows). ERα-associated fitness genes were altered in 74% of OC samples.
Figure 5. OncoPrint representation of the molecular landscape for ERα-associated fitness genes in ovarian cancer tissues collected in The Cancer Genome Atlas (TCGA), according to study of origin (Firehose Legacy, Nature 2011 and PanCancer Atlas). The OncoPrint provides an overview of genomic alterations (legend) per each gene across ovarian cancer cohorts (rows). ERα-associated fitness genes were altered in 74% of OC samples.
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Figure 6. Canonical pathway enrichment analysis involving OC fitness genes using Ingenuity Pathway Analysis (IPA) and EnrichmentMap. Edges between nodes were generated using an overlap coefficient threshold of 0.3 and their width is proportional to the number of shared genes.
Figure 6. Canonical pathway enrichment analysis involving OC fitness genes using Ingenuity Pathway Analysis (IPA) and EnrichmentMap. Edges between nodes were generated using an overlap coefficient threshold of 0.3 and their width is proportional to the number of shared genes.
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Table 1. Ovarian cancer cell lines, representative of different subtypes, used for cancer-related fitness genes identification.
Table 1. Ovarian cancer cell lines, representative of different subtypes, used for cancer-related fitness genes identification.
Ovarian Cancer SubtypeCell Lines
High Grade SerousCaov-3, COV318, COV362, HEY A8, JHOS-2, JHOS-4, Kuramochi, OAW28, ONCO-DG-1, OV-90, OVCAR-8, Caov-4, HEY, OVCAR-5*, TYK-nu, OVCAR-3, OVMIU, PEO1, PEO4
EndometrioidA2780, TOV-112D, A2780ADR, IGROV-1, OVK18, A2780cis
MucinousCOV644, JHOM-1, RMUG-S, EFO-27, MCAS
SerousSNU-8, UWB1.289, OAW42, OC 314, OVCA420
Mixed59M, OV7
Brenner TumorSNU-840
Granulosa Cells TumorCOV434
UnspecifiedDOV13, EFO-21
* Ambiguous cell line: suspected to have an upper gastrointestinal origin [94].
Table 2. Canonical pathway analysis performed by Ingenuity Pathway Analysis (IPA) on ovarian cancer (OC) fitness genes.
Table 2. Canonical pathway analysis performed by Ingenuity Pathway Analysis (IPA) on ovarian cancer (OC) fitness genes.
Cell cycle regulation and DNA damage response (DDR)
Cell Cycle Control of Chromosomal Replication1.26E−22CDC45, CDC6, CDC7, CDK1, CDK11A, CDK2, CDK7, CDK9, CDT1, DBF4, MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, ORC1, ORC5, ORC6, PCNA, POLA1, POLA2, POLD1, POLE, PRIM1, RPA1, RPA2, RPA3, TOP2A
Mitotic Roles of Polo-Like Kinase1.00E−17ANAPC1, ANAPC10, ANAPC11, ANAPC2, ANAPC4, ANAPC5, CCNB1, CDC16, CDC20, CDC23, CDC26, CDC27, CDC7, CDK1, ESPL1, FBXO5, KIF11, KIF23, PKMYT1, PLK1, PLK4, PPP2R1A, PRC1, RAD21, SMC1A, SMC3, WEE1
Nucleotide Excision Repair Pathway2.00E−14CCNH, CDK7, ERCC2, ERCC3, GTF2H1, POLR2B, POLR2C, POLR2D, POLR2E, POLR2F, POLR2G, POLR2H, POLR2I, POLR2K, POLR2L, RPA1, RPA2, RPA3
Role of CHK Proteins in Cell Cycle Checkpoint Control3.47E−09ATR, CDK1, CDK2, CHEK1, CLSPN, HUS1, PCNA, PLK1, PPP2R1A, RAD1, RAD17, RAD9A, RFC2, RFC3, RFC4, RFC5, RPA1
DNA damage-induced 14-3-3σ Signaling2.75E−06ATR, CCNB1, CDK1, CDK2, HUS1, RAD1, RAD17, RAD9A
Mismatch Repair in Eukaryotes8.71E−06PCNA, POLD1, RFC2, RFC3, RFC4, RFC5, RPA1
Role of BRCA1 in DNA Damage Response1.74E−05ACTB, ATR, ATRIP, CHEK1, PLK1, RAD51, RBBP8, RFC2, RFC3, RFC4, RFC5, RPA1, SMARCB1, SMARCE1, TOPBP1
Cell Cycle: G2/M DNA Damage Checkpoint Regulation4.07E−05ATR, AURKA, CCNB1, CDK1, CDK7, CHEK1, PKMYT1, PLK1, SKP1, TOP2A, WEE1
Cell Cycle: G1/S Checkpoint Regulation1.86E−04ATR, CCND1, CDK2, GNL3, HDAC3, MAX, MYC, PAK1IP1, RPL11, RPL5, SIN3A, SKP1
Cyclins and Cell Cycle Regulation3.24E−04ATR, CCNA2, CCNB1, CCND1, CCNH, CDK1, CDK2, CDK7, HDAC3, PPP2R1A, SIN3A, SKP1, WEE1
Estrogen-mediated S-phase Entry1.07E−02CCNA2, CCND1, CDK1, CDK2, MYC
Role of p14/p19ARF in Tumor Suppression1.70E−02NPM1, PIK3C3, RAC1, SF3A1, UBTF
DNA Double-Strand Break Repair by Homologous Recombination3.47E−02POLA1, RAD51, RPA1
Hypoxia and Angiogenesis
EIF2 Signaling7.94E−66ACTB, CCND1, CDK11A, EIF1, EIF1AX, EIF2B2, EIF2B3, EIF2B4, EIF2B5, EIF2S1, EIF2S2, EIF2S3, EIF3A, EIF3B, EIF3D, EIF3E, EIF3F, EIF3G, EIF3I, EIF3M, EIF4A1, EIF4A3, EIF4E, EIF4G1, EIF5, FAU, GRB2, HSPA5, MYC, PABPC1, PDPK1, PIK3C3, PPP1CB, RPL10A, RPL11, RPL12, RPL13, RPL13A, RPL14, RPL15, RPL17, RPL18, RPL18A, RPL19, RPL23, RPL23A, RPL24, RPL26, RPL27, RPL27A, RPL28, RPL3, RPL30, RPL31, RPL32, RPL34, RPL35, RPL36, RPL37, RPL37A, RPL38, RPL4, RPL5, RPL6, RPL7, RPL7A, RPL7L1, RPL8, RPLP0, RPLP1, RPLP2, RPS11, RPS12, RPS13, RPS14, RPS15, RPS15A, RPS16, RPS18, RPS19, RPS2, RPS20, RPS21, RPS23, RPS24, RPS25, RPS27A, RPS28, RPS29, RPS3, RPS4X, RPS5, RPS6, RPS7, RPS8, RPS9, RPSA, UBA52, WARS1
VEGF Signaling7.24E−04ACTB, BCL2L1, EIF1, EIF1AX, EIF2B2, EIF2B3, EIF2B4, EIF2B5, EIF2S1, EIF2S2, EIF2S3, GRB2, PIK3C3, PTPN11
Proliferative Signaling
Regulation of eIF4 and p70S6K Signaling1.26E−29EIF1, EIF1AX, EIF2B2, EIF2B3, EIF2B4, EIF2B5, EIF2S1, EIF2S2, EIF2S3, EIF3A, EIF3B, EIF3D, EIF3E, EIF3F, EIF3G, EIF3I, EIF3M, EIF4A1, EIF4A3, EIF4E, EIF4G1, FAU, GRB2, MTOR, PABPC1, PDPK1, PIK3C3, PPP2R1A, RPS11, RPS12, RPS13, RPS14, RPS15, RPS15A, RPS16, RPS18, RPS19, RPS2, RPS20, RPS21, RPS23, RPS24, RPS25, RPS27A, RPS28, RPS29, RPS3, RPS4X, RPS5, RPS6, RPS7, RPS8, RPS9, RPSA
mTOR Signaling3.98E−18CDC42, EIF3A, EIF3B, EIF3D, EIF3E, EIF3F, EIF3G, EIF3I, EIF3M, EIF4A1, EIF4A3, EIF4E, EIF4G1, FAU, GNB1L, MTOR, PDPK1, PIK3C3, PPP2R1A, RAC1, RHOQ, RPS11, RPS12, RPS13, RPS14, RPS15, RPS15A, RPS16, RPS18, RPS19, RPS2, RPS20, RPS21, RPS23, RPS24, RPS25, RPS27A, RPS28, RPS29, RPS3, RPS4X, RPS5, RPS6, RPS7, RPS8, RPS9, RPSA, RPTOR
Iron homeostasis signaling pathway2.24E−07ACO2, ATP6AP1, ATP6V0B, ATP6V0C, ATP6V0D1, ATP6V1A, ATP6V1B2, ATP6V1C1, ATP6V1D, ATP6V1E1, ATP6V1F, ATP6V1G1, ATP6V1H, CIAO1, HSCB, HSPA9, ISCU, LYRM4, MMS19, NFS1, NUBP1, NUBP2, PCBP1, SKP1
Glucocorticoid Receptor Signaling1.58E−06ACTB, BCL2L1, CCNH, CDK7, ERCC2, ERCC3, GRB2, GTF2A1, GTF2A2, GTF2B, GTF2E1, GTF2E2, GTF2F1, GTF2F2, GTF2H1, HSPA5, HSPA9, MED14, PIK3C3, POLR2B, POLR2C, POLR2D, POLR2E, POLR2F, POLR2G, POLR2H, POLR2I, POLR2K, POLR2L, RAC1, SMARCB1, SMARCE1, TAF1, TAF10, TAF12, TAF2, TAF6, TAF7, TSG101, UBE2I
RAN Signaling1.62E−04CSE1L, KPNB1, RAN, RANGAP1, RCC1, XPO1
Insulin Receptor Signaling4.47E−04CRKL, EIF2B2, EIF2B3, EIF2B4, EIF2B5, EIF4E, GRB2, MTOR, PDPK1, PIK3C3, PPP1CB, PPP1R10, PPP1R11, PPP1R12A, PPP1R7, PTPN11, RHOQ, RPTOR
Estrogen Receptor Signaling6.92E−04CCND1, DDX5, EIF2B2, EIF2B3, EIF2B4, EIF2B5, EIF4E, GRB2, HDAC3, MED10, MED14, MED17, MED18, MED20, MED21, MED30, MED4, MED6, MTOR, MYC, NRF1, PCNA, PELP1, PIK3C3, POLR2B, PPP1CB, PPP1R12A, SDHC, SOD2, TFAM, TRRAP, UQCRFS1
Translation and post-translational modifications
Assembly of RNA Polymerase II Complex3.98E−23CCNH, CDK7, DR1, ERCC2, ERCC3, GTF2A1, GTF2A2, GTF2B, GTF2E1, GTF2E2, GTF2F1, GTF2H1, POLR2B, POLR2C, POLR2D, POLR2E, POLR2F, POLR2G, POLR2H, POLR2I, POLR2K, POLR2L, TAF1, TAF10, TAF12, TAF2, TAF6, TAF7
Cleavage and Polyadenylation of Pre-mRNA2.51E−08CPSF1, CPSF2, CPSF3, CPSF6, CSTF3, NUDT21, PABPN1, WDR33
Assembly of RNA Polymerase I Complex2.51E−08POLR1A, POLR1B, POLR1C, POLR1E, POLR2F, TAF1B, TAF1C, UBTF
Assembly of RNA Polymerase III Complex6.17E−08BRF1, BRF2, GTF3A, GTF3C1, GTF3C2, GTF3C4, GTF3C5, SF3A1
Sumoylation Pathway2.82E−05CDC42, PCNA, RAC1, RAN, RANGAP1, RCC1, RFC2, RFC3, RFC4, RFC5, RHOQ, RNF4, RPA1, SAE1, SENP6, UBA2, UBE2I
Spliceosomal Cycle2.82E−03U2AF1/U2AF1L5, U2AF2
Mechanisms of Viral Exit from Host Cells4.27E−05ACTB, CHMP2A, CHMP3, CHMP4B, CHMP6, SNF8, TSG101, VPS25, VPS28, XPO1
Remodeling of Epithelial Adherens Junctions5.13E−05ACTB, ACTR2, ACTR3, ARPC2, ARPC3, ARPC4, DNM1L, DNM2, HGS, TUBA1B, TUBA1C, TUBB, TUBG1
Superpathway of Geranylgeranyldiphosphate Biosynthesis I (via Mevalonate)2.00E−03FNTB, GGPS1, HMGCR, HMGCS1, MVK
Regulation of Actin-based Motility by Rho4.07E−03ACTB, ACTR2, ACTR3, ARPC2, ARPC3, ARPC4, CDC42, PFN1, PPP1CB, PPP1R12A, RAC1, RHOQ
Actin Nucleation by ARP-WASP Complex4.57E−03ACTR2, ACTR3, ARPC2, ARPC3, ARPC4, CDC42, GRB2, PPP1R12A, RAC1, RHOQ
Caveolar-mediated Endocytosis Signaling5.01E−03ACTB, ARCN1, COPA, COPB1, COPB2, COPE, COPG1, COPZ1, DNM2, ITGAV
Pyrimidine Deoxyribonucleotides De Novo Biosynthesis I5.13E−03CMPK1, DTYMK, DUT, RRM1, RRM2
Inhibition of ARE-Mediated mRNA Degradation Pathway5.25E−03CNOT1, CNOT3, DDX6, EXOSC2, EXOSC3, EXOSC4, EXOSC5, EXOSC6, EXOSC7, EXOSC8, EXOSC9, PABPN1, PPP2R1A, XRN1
Telomere Extension by Telomerase6.76E−03TERF1, TINF2, XRCC5, XRCC6
Clathrin-mediated Endocytosis Signaling7.08E−03ACTB, ACTR2, ACTR3, ARPC2, ARPC3, ARPC4, CDC42, CLTC, CSNK2B, DNM1L, DNM2, GAK, GRB2, HGS, PIK3C3, RAC1, RPS27A, TSG101, UBA52
Oxidized GTP and dGTP Detoxification8.13E−03DDX6, RUVBL2
Geranylgeranyldiphosphate Biosynthesis8.13E−03FNTB, GGPS1
Tight Junction Signaling1.70E−02ACTB, CDC42, CPSF1, CPSF2, CPSF3, CPSF6, CSTF3, GOSR2, NAPA, NAPG, NSF, NUDT21, PPP2R1A, RAC1, SYMPK, YKT6
Mevalonate Pathway I3.47E−02HMGCR, HMGCS1, MVK
Following, the fitness genes specifically involved in the most meaningful pathways representing the key hallmarks of OC will be summarized.

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MDPI and ACS Style

Alexandrova, E.; Pecoraro, G.; Sellitto, A.; Melone, V.; Ferravante, C.; Rocco, T.; Guacci, A.; Giurato, G.; Nassa, G.; Rizzo, F.; Weisz, A.; Tarallo, R. An Overview of Candidate Therapeutic Target Genes in Ovarian Cancer. Cancers 2020, 12, 1470.

AMA Style

Alexandrova E, Pecoraro G, Sellitto A, Melone V, Ferravante C, Rocco T, Guacci A, Giurato G, Nassa G, Rizzo F, Weisz A, Tarallo R. An Overview of Candidate Therapeutic Target Genes in Ovarian Cancer. Cancers. 2020; 12(6):1470.

Chicago/Turabian Style

Alexandrova, Elena, Giovanni Pecoraro, Assunta Sellitto, Viola Melone, Carlo Ferravante, Teresa Rocco, Anna Guacci, Giorgio Giurato, Giovanni Nassa, Francesca Rizzo, Alessandro Weisz, and Roberta Tarallo. 2020. "An Overview of Candidate Therapeutic Target Genes in Ovarian Cancer" Cancers 12, no. 6: 1470.

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