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Article

Integrative Analysis of Gene Expression and Promoter Methylation to Differentiate High-Grade Serous Ovarian Cancer from Benign Tumors

by
Ieva Vaicekauskaitė
1,2,
Paulina Kazlauskaitė
2,3,
Rugilė Gineikaitė
1,
Rūta Čiurlienė
4,
Juozas Rimantas Lazutka
1 and
Rasa Sabaliauskaitė
1,2,*
1
Institute of Biosciences, Life Sciences Center, Vilnius University, 10257 Vilnius, Lithuania
2
National Cancer Institute, 08406 Vilnius, Lithuania
3
Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, 08661 Vilnius, Lithuania
4
National Cancer Center, Vilnius University Hospital Santaros Klinikos, 08661 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Biomedicines 2025, 13(2), 441; https://doi.org/10.3390/biomedicines13020441
Submission received: 14 January 2025 / Revised: 3 February 2025 / Accepted: 8 February 2025 / Published: 11 February 2025
(This article belongs to the Special Issue Gynecological Diseases in Cellular and Molecular Perspectives)
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Abstract

:
Background: Ovarian cancer (OC) is the third most common and second most lethal onco-gynecological disease in the world, with high-grade serous ovarian cancer (HGSOC) making up the majority of OC cases worldwide. The current serological biomarkers used for OC diagnosis are lacking sensitivity and specificity, thus new biomarkers are greatly needed. Recently, the chromatin remodeling complex gene ARID1A, Notch and Wnt pathway gene expression, as well as HOX-related gene promoter methylation have been linked with promoting OC. Methods: In this pilot study, 10 gene expression biomarkers and 4 promoter methylation biomarkers were examined as potential diagnostic and prognostic indicators of OC in 65 fresh-frozen gynecologic tumor tissues. Results: Out of 10 genes analyzed, the expression of eight biomarkers was significantly reduced in OC cases compared to benign, and HOX-related gene promoter methylation significantly increased in OC tumors. Out of 14 biomarkers, CTNNB1 showed the best single biomarker separation of HGSOC from benign cases (AUC = 0.97), while a combination of the seven Notch pathway-related gene expressions (NOTCH1, NOTCH2, NOTCH3, NOTCH4, DLL1, JAG2, and HES1) demonstrated the best separation of HGSOC from the benign cases (AUC = 1). Conclusions: The combination of multiple gene expression or gene promoter methylation biomarkers shows great promise for the development of an effective biomarker-based diagnostic approach for OC.

1. Introduction

Ovarian cancer (OC) is the second leading cause of death due to onco-gynecological disease, with more than 300 thousand new cases and 200 thousand deaths reported worldwide each year [1]. The vast majority (90%) of OC cases are of the epithelial type OC, with the largest share (52%) of cases further categorized into high-grade serous type OC (HGSOC). Compared to other types of epithelial OC (endometrioid, clear cell, mucinous, borderline) or non-epithelial (stromal and germ cell) tumors, HGSOC is diagnosed at a late stage (80% of HGSOC diagnosed at stage III-IV, compared to ~60% of other epithelial OC diagnosed at stage I) and is responsible for the largest share of fatalities, with a 5-year survival rate of only 43%, compared to 66–82% for other types of epithelial OC [2]. Despite the characteristically low survival rates (5-year age-standardized net survival rate in Lithuania and the majority of Europe as low as 30–40%) slowly trending upwards [3], it remains critical to have great diagnostic and prognostic tools for OC.
OC does not present any specific symptoms; the only physical indication of a possible tumor are abdominal distention and pain, which are symptoms more typically accredited to gastrointestinal problems. The main diagnostic tests performed for OC are abdominopelvic ultrasonography and the measurement serum protein biomarker CA125 [4]. Despite the wide use of CA125, it is recommended against OC screening using CA125, due to the CA125 blood test failure to reduce the mortality of OC patients [5]. The attempts to increase the accuracy of CA125 with the addition of another serum biomarker HE4 do not provide the desired specificity for a screening assay (sensitivity 79%, specificity 91%) [6].
The current goal for OC researchers is to find new molecular characteristics that could detect cancer and identify patients suitable for targeted treatment [7]. The Cancer Genome Atlas (TCGA) project has been a major effort in identifying genetic alterations prevalent in cancers. It found that approximately 22% of HGSOC patient samples exhibit dysregulation in the Notch pathway [8]. In contrast, the Wnt pathway and the chromatin remodeling complex gene ARID1A are among some of the most dysregulated in non-serous types of OC [9]. Studies show that Notch and Wnt pathways are important regulators of the stemness, differentiation and proliferation in the fallopian tube cells where HGSOC is thought to originate [10], thus its expression levels have the potential to be used as early biomarkers of HGSOC. CTNNB1 and FBXW7 genes are some of the most altered Wnt pathway genes in OC [9], while in the Notch pathway the receptors (NOTCH1-4), ligands (such as DLL1, JAG2), as well as the canonical target (HES1), are some of the most dysregulated Notch genes in cancers overall [11].
Chromatin remodeling complexes contribute to non-gene specific regulation of transcription via control of DNA accessibility to the transcription factors. The process of transcription repression is kickstarted by DNA methyltransferase (DNMT) methylating the DNA cytosine base in the CpG-rich dinucleotide sequences known as CpG islands, frequent around most gene promoters. DNA methylation facilitates the recruitment of histone deacetylases that result in more condensed and transcriptionally inactive chromatin [7]. The hypermethylation of the tumor suppressor gene promoter CpG islands is one of the earliest cancer hallmarks, often predating genetic changes and leading to altered gene expression. Compared to normal fallopian tube epithelium, at least 3868 genes in HGSOC samples are hypermethylated and affect gene transcription [12].
In many types of cancer, homeobox (HOX) genes recruit PRC2 repressive chromatin regulation complex and thus act as transcriptional regulators [13]. HOX genes are often regulated via the hypermethylation of CpG islands in their promoter regions. CDX2, ALX4 and HOPX, three non-canonical HOX genes are hypermethylated in colorectal, pancreatic, gastric and lung cancers [12]; however, the data on their methylation status in OC is less clear. Although the ARID1A gene is the second most mutated gene in non-HGSOC OC, its loss in gynecologic tumor precursors such as endometriosis could be caused by promoter hypermethylation as well [14].
Importantly, the hypermethylation of HOX-related genes and the loss of functional ARID1A are highly associated with resistance to chemotherapy and are potentially druggable through epigenetic therapies [13,15], with DNMT inhibition therapies potentially effective in resensitizing HOX deficient tumors to carboplatin [16], and recent clinical trials running for EZH and BET inhibitors for ARID1A deficient tumors [17].
As part of the goal of creating a biomarker-based diagnostic test for OC, this pilot study examined nine main gene expression biomarkers of the Notch and Wnt pathways and the OC susceptibility gene ARID1A for expression changes in patients with gynecologic tumors. In addition, gynecologic tumor tissue was analyzed for promoter methylation of ARID1A and three homeobox (HOX) related genes. Multiple biomarkers incorporated into the model outperformed single biomarkers in the separation of HGSOC disease from benign counterparts and other types of gynecologic disease, thus providing a promising strategy for the further development of potential diagnostic tests for the detection of HGSOC.

2. Materials and Methods

2.1. Patient Selection and Sample Collection

The study cohort comprised of 65 patients with suspected ovarian cancer who received salpingo-oophorectomy at the National Cancer Institute, Lithuania, between 2018 and 2023. The study approval was granted by the regional bioethics committee (No. 158200-18/5-988-539). Tissue samples collected from the ovary tissues were removed during the procedure, flash-frozen and immediately stored at –80 °C until further use.
Of the 65 patients included in the study, 56 had malignant gynecologic tumors, 42 had confirmed HGSOC, and 14 had other gynecologic tumors (of them five cases were of borderline ovarian cancer, three synchronous endometrial and endometrioid ovarian cancer, two mucinous, one low grade serous, one endometrioid, one clear cell ovarian cancer, and one granulosa cell tumor), while nine had benign gynecologic conditions (gynecologic cysts or benign tumors, as well as one case of preventative salpingoovarectomy due to BRCA2 mutation). The main clinical features in the study included age at diagnosis, tumor grade, International Federation of Gynecology and Obstetrics (FIGO) stage, and pre-operation serum CA125 concentration, where 35 U/mL was considered a threshold concentration and values below 35 U/mL were considered normal [4]. The clinical features of the patients are provided in Table 1.

2.2. Nucleic Acid Extraction

A small tumor tissue sample (~30 mg) was pulverized in liquid nitrogen using a mortar and pestle and a small amount (~10 mg) was used for either RNA or DNA extractions. DNA samples were extracted first by digesting the samples with Proteinase K (Thermo Scientific, Thermo Fisher scientific (TFS), Vilnius, Lithuania) for 16 h and then performing a standard phenol-chloroform DNA purification and ethanol-based precipitation method. This DNA extraction method is one of the longest-established methods widely used for high-purity and high-molecular-weight DNA extraction and is highly suitable for multiple applications [18]. RNA from ovarian tissues was extracted using a widely validated method using TRIzol Reagent (Invitrogen, TFS, Carlsbad, CA, USA) [19]. In addition to its effective tissue lysis, TRIzol Reagent preserves RNA integrity and stability, ensuring high-quality total RNA [20] suitable for downstream applications, such as cDNA synthesis, delivering consistent results. The purity and concentration of nucleic acid extractions were evaluated using a Nanodrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The nucleic acid samples were stored at –80 °C until use in the methylation-specific PCR (MSP) or cDNA synthesis step.

2.3. Synthesis of cDNA and Quantitative PCR

RNA extracted from gynecologic tissues was used for quantitative analysis of gene expression. A two-step RT-qPCR procedure was followed: 0.5 µg of the total RNA was used for cDNA synthesis following the manufacturer’s protocols of the Maxima First Strand cDNA Synthesis Kit for RT-qPCR with dsDNase (Thermo Scientific, TFS, Vilnius, Lithuania) and ProFlex PCR System (Applied Biosystems, TFS, Singapore). The cDNA synthesis kit produces a high yield of RNA and is highly suitable for subsequent PCR applications [21]. Quantitative PCR (qPCR) was performed on a QuantStudio 5 Real-Time PCR System (Applied Biosystems, TFS, Singapore) using Maxima SYBR Green qPCR Master Mix (2X) kit (Thermo Scientific, TFS, Vilnius, Lithuania) in accordance to the manufacturer’s instructions.
A 10-gene panel consisting of Notch pathway genes (NOTCH1, NOTCH2, NOTCH3, NOTCH4, JAG2, DLL1, HES1), Wnt pathway genes (CTNNB1, FBXW7) and a chromatin remodeling complex SWI/SNF gene ARID1A was analyzed for changes in relative gene expression in gynecologic tissues. GAPDH was used as an internal control gene. The primer sequences used in qPCR reactions are provided in Table S1. An initial analysis of Ct values was conducted on QuantStudio Design and Analysis Software v1.4.3 (Applied Biosystems, TFS, Singapore). Gene expression normalization was performed using the log2 2 − Δ Ct method; these values were used in statistical analysis.

2.4. Methylation Specific PCR

ARID1A and three HOX-related gene (HOPX, ALX4, CDX2) promoter methylation status were tested using methylation-specific PCR (MSP). First, bisulfite modification was applied to gynecologic tissue DNA using an EZ DNA Methylation kit (Zymo Research, Irvine, CA, USA). The MSP primers used in the analysis are provided in Table S1. MSP assays were performed according to Phusion U Hot Start DNA Polymerase (Thermo Scientific, TFS, Vilnius, Lithuania) protocol instructions. According to the three-step cycling protocol the annealing step consisted of 30 s at 60 °C for ALX4 and ARID1A, 58 °C for CDX2, and 62 °C for HOPX. All reactions were performed on the ProFlex PCR System (Applied Biosystems, TFS, Singapore). For each set of reactions, CpG Methylated Human Genomic DNA (Thermo Scientific, TFS, Vilnius, Lithuania) was a positive control and a mix of five healthy human genomic DNA as a wild-type control as well as nuclease-free water for non-template control. The MSP products were visualized using 3% agarose gels (Carl Roth, Karlsruhe, Germany) in 1× TAE buffer (Thermo Scientific, TFS, Vilnius, Lithuania) with 0.6 µg/mL Ethidium Bromide (Carl Roth, Karlsruhe, Germany) staining. TriTrack DNA loading dye (6×) (Thermo Scientific, TFS, Vilnius, Lithuania) was used for loading the gels, and the GeneRuler 100 bp DNA ladder (Thermo Scientific, TFS, Vilnius, Lithuania) was used as a size standard. Gels visualized using UVP ChemStudio (Analytic Jena, Jena, Germany) imaging system.

2.5. Statistical Analysis

For comparisons of two independent groups, Welch’s t-test [22] was chosen due to its high statistical power and ability to minimize the risk of invalid statistical inferences due to unequal sample sizes [23]. The Benjamini-Hochberg method was applied to adjust p-values to effectively control false discovery rates [24]. The contingency between methylation biomarkers and categorical variables examined using Fisher’s exact test due to the small sample size. Receiver operating characteristic (ROC) analysis was used to analyze the performance of biomarkers or their combinations, while the DeLong test was used to compare ROC curves. Logistic regression models were applied for combinations of multiple biomarkers. All analysis and visualization were performed on R (version 4.3.1, R Foundation for statistical computing, Vienna, Austria). The ComplexHeatmap package (version 2.16.0) was used for heatmap visualizations, the pROC package (version 1.18.5) for ROC analysis, the glmnet package (version 4.1-8) for regression analysis and the ggplot2 package (version 3.5.1) for general visualizations. The results were considered statistically significant when p < 0.05.

3. Results

3.1. Promoter Methylation Changes in Ovarian Cancer Compared to Benign Gynecologic Tissues

We analyzed 65 gynecologic tumor tissue samples using a panel composed of four gene promoter methylation biomarkers and 10 gynecologic cancer-related mRNA expression biomarkers. Promoter methylation of at least one HOX related gene (ALX4, CDX2 or HOPX) or ARID1A gene was detected in 83.08% (54/65) of tumor samples. ALX4 promoter methylation was detected in 44.62% of samples, CDX2 49.23%, HOPX 33.85% and ARID1A 43.08% (Figure 1).
Comparing gene promoter methylation in OC cases with benign tissues, all three HOX-related gene promoters had significant increases (p < 0.04) in methylation frequency in OC; with both ALX4 and CDX2 only 1/9 benign cases showed promoter methylation and no benign cases had HOPX promoter methylation. ARID1A promoter methylation showed no significant difference in OC and benign cases (Supplementary Figure S1).
When compared to benign cases, the significant increase in promoter methylation in HGSOC was detected only for the HOPX promoter (p = 0.04); however, a tendential increase (p = 0.06) was also noticed in ALX4 and CDX2 gene promoters. When comparing the other OC cases (non-HGSOC OC) to benign counterparts, CDX2 and HOPX showed increased promoter methylation (p = 0.01 and 0.05, respectively) (Figure 2). Additionally, in the HGSOC group, a significant increase in ARID1A gene promoter methylation was found in advanced FIGO stage cases (p = 0.05) (Supplementary Figure S2). No further associationsbetween gene promoter methylation status and clinical features of serum CA125 concentration, FIGO stage, grade or age were found (Figure 1).

3.2. Gene Expression Changes in Ovarian Cancer Compared to Benign Gynecologic Tissues

The expression levels of the seven Notch pathway, two Wnt pathway (CTNNB1 and FBXW7), and ARID1A genes were also analyzed in ovarian tissue samples; 8/10 gene expression was significantly lower in OC cases compared to benign (p < 0.03, Fold change (FC) > 1.18), with only ARID1A showing a tendential reduction in expression (p = 0.06, FC = 1.14); JAG2 was not significantly changed (Supplementary Figure S3).
Comparing HGSOC samples with benign samples, a significant reduction in all but JAG2, NOTCH3 and ARID1A gene expression was noted, with CTNNB1 expression decreasing by two-fold (FC = 2.01; p < 0.001), which was the most out of all genes included in the panel. Notably, reduced DLL1 and HES1 expression was also detected in HGSOC when compared to other (non-type II) gynecologic tumors (FC = 1.17 and 1.43, respectively; p < 0.02). Notch receptor NOTCH2 and Wnt gene CTNNB1 expression were also significantly reduced in other gynecologic tumors when compared to benign tissues (FC = 1.32, and 1.62, respectively; p = 0.002) (Figure 3).
HES1 and CTNNB1 expression was significantly lower in high-grade (grade 3) gynecologic cancer compared to low-grade (grade 1) cases (p < 0.05, FC= 1.48, 1.22, respectively), while DLL1 and FBXW7 also showed a tendency for reduced expression in high-grade tumors (p = 0.05, FC = 1.17 and 1.13, respectively) (Supplementary Figure S4). In the HGSOC group, Wnt gene CTNNB1 and FBXW7 expression was significantly correlated with advanced FIGO stage (IV vs. III, FC = 1.20, p = 0.04, FC = 1.13, p = 0.01, respectively) (Supplementary Figure S5).
Wnt genes CTNNB1, FBXW7, and Notch genes NOTCH2, NOTCH3, and NOTCH4 were significantly decreased in cases with a positive CA125 status (CA125 serum concentration > 35 U/mL, cases with increased CA125 (n = 50), CA125 normal serum concentration (n = 5), FC = 1.59, 1.20, 1.29, 1.28, and 1.33, respectively, p < 0.05) (Supplementary Figure S6). No gene expression correlation with age at diagnosis was detected (Figure 1).

3.3. Building an Ovarian Cancer Diagnostic Model

Fourteen biomarkers were analyzed for their prediction of ovarian cancer using ROC analysis. The best predictor of OC vs. benign gynecologic tumors was CTNNB1 expression (AUC = 0.95, accuracy = 0.86, sensitivity = 0.84, specificity = 1); however, the highest accuracy and sensitivity were detected by NOTCH2 (AUC = 0.89, accuracy = 0.89, sensitivity = 0.89, specificity = 0.89) (Supplementary Figure S6). In the promoter methylation biomarker group, the best separator of OC was CDX2 (AUC = 0.72, accuracy = 0.60, sensitivity = 0.55, specificity = 0.89), which was marginally better than other HOX family gene promoters and a significant increase in AUC over ARID1A promoter methylation (ARID1A promoter methylation AUC = 0.49, DeLong test p = 0.03) (Supplementary Figure S7).
The best single biomarker separator of HGSOC from the benign tumor group was CTNNB1 gene expression (AUC = 0.97). In general, gene expression biomarkers were better suited to detect HGSOC than promoter methylation biomarkers with with 6/10 (CTNNB1, DLL1, HES1, FBXW7, NOTCH2, NOTCH4) of gene expression biomarkers significantly improving HGSOC separation from benign samples compared to best promoter methylation separators CDX2 and ALX4 (AUC = 0.69, DeLong p < 0.03) (Figure 4).
We then examined biomarker combinations for their ability to separate the OC cases from benign gynecologic tumors. All five combinations were tested, combining all 14 available biomarkers, i.e., 10 gene expression and 4 gene promoter methylation (mixed biomarker combination), all gene expression biomarkers (10 biomarkers), all promoter methylation biomarkers (four biomarkers); the Notch gene expression biomarker combination which included seven Notch genes in our panel (NOTCH1, NOTCH2, NOTCH3, NOTCH4, DLL1, JAG2, and HES1), and the three HOX-related gene promoter methylation combination. Comparing the combinations we found that a 10 gene expression biomarker combination was able to perfectly separate the two groups. While all five combinations significantly outperformed serum CA125 in both AUC and specificity (CA125 AUC = 0.78, specificity = 0.57), only the 14 biomarker, 10 gene expression biomarker combination and Notch gene expression biomarker combination exceeded CA125 sensitivity and accuracy (sensitivity = 0.98, accuracy = 0.93) and significantly improved on the OC separation form benign samples (p < 0.05) (Supplementary Figure S8).
To achieve the perfect separation of HGSOC cases from benign gynecologic tumors (AUC = 1) the combination of only the seven Notch pathway gene expression was sufficient. While the combination of biomarkers did not significantly improve the separation compared to the best single biomarker CTNNB1 (p = 0.13), it did improve the accuracy and sensitivity (1.00 and 1.00 for combination and 0.92 and 0.91 for CTNNB1 expression, respectively). The promoter methylation biomarker models including the three HOX-related genes or all four methylation biomarkers outperformed serum CA125 specificity, but only the gene expression combinations outperformed HGSOC separation from benign cases (p = 0.04) (Figure 5). Overall, the combination of biomarkers improved both serum biomarker and gene promoter methylation separation of OC or HGSOC samples from benign cases and improved on specificity and accuracy of the potential biomarker test when compared to single biomarkers.

4. Discussion

The present study investigated 10 gene expression and four gene promoter methylation biomarkers in gynecologic tumor tissue for the potential of ovarian cancer diagnosis. The biomarkers comprised a set of seven Notch and two Wnt pathway gene expressions, alongside gene expression and promoter methylation of the chromatin remodeling complex SWI/SNF gene ARID1A, as well as three HOX-related gene promoter methylation analyses. To our best knowledge, this is the first such type of study that approaches a novel multivariable OC diagnosis based on different biomarker combinations.
Notch and Wnt are major cell signaling pathways that have been long associated with gynecologic tumors, both with roles in stemness, as evidenced by the fallopian tube organoids [25]. Notch pathway negatively impacts the activity of the Wnt pathway in most cancers; Wnt can also impact the expression of DLL1, HES1 and NOTCH2 on the protein level, while β -catenin can also interact with NOTCH1 and reduce its ubiquitination [26]. A recent analysis of the cancer genome atlas (TCGA) ovarian cancer data, largely comprised of HGSOC samples, has shown a significant reduction in DLL1, JAG1, NOCTH4, and an increase in HES1, NOCTH1 and NOTCH3 expression in OC samples compared to normal tissues [27]. The reduction in gene expression was consistent with our results of lower Notch gene expression in HGSOC samples compared to benign tumor tissues. The TCGA study also revealed significant associations between JAG2 and NOTCH1 and progress-free survival and NOCTH4 with the FIGO stage, the latter was not detected in our study [27]. A similar study did not find any significant changes in CTNNB1 mRNA expression in HGSOC compared to normal tissues (The Genotype-Tissue Expression project (GTEx)); however, there was a correlation between CTNNB1 expression and clinical stage, also confirmed in our study [28]. Meanwhile, FBXW7 has been found to be downregulated in ovarian cancer cells in TCGA data, tumor tissues and cell lines as well [29].
Homeobox (HOX) genes are an evolutionary conservative group of transcription factors bearing a homeodomain that have important functions in embryogenesis and carcinogenesis. Many HOX genes are described as tumor-suppressors and are downregulated via promoter hypermethylation [13]. The homeobox gene superclass has 235 functional genes, CDX2 belongs to the ANTP-class, the most well-known class of HOX, while ALX4 and HOPX belong to PRD-class [30], showing a vast diversity in the HOX genes.
HOPX expression is a novel tumor-associated stromal biomarker, it was found to be a potential molecular driver of OC, particularly of the “mesenchymal” OC subtype [31]. HOPX has been identified as one of the most cancer-specific hypermethylated biomarkers, with hypermethylation detected in 90% of primary gastric tumors [32], 62.5% (10/16) colorectal [33], and 69% (9/14) endometrial cancers [34], the latter was considerably higher than the 39% hypermethylation in our OC samples.
ALX4, another tumor suppressor in the HOX family is involved in the promotion of ovarian cancer metastasis: ALX4, together with the other HOX protein HOXB13 promotes epithelial to mesenchymal transition factor SLUG in ovarian cancer cell lines [35]. Moreover, TCGA analysis via weighted gene co-expression network analysis (WGCNA) selected ALX4 as one of the 14 differentially expressed genes associated with ovarian cancer overall survival [36]. According to TCGA and GEO data, ALX4 is also amongst the most differentially methylated genes in ovarian cancer, and its hypermethylation is associated with chemoresistance [37]. The ALX4 promoter was also methylated in 69.44% (75/108) of cases and none of the eight normal breast tissues [38] while in colorectal cancer patients’ serum, methylation was detected in 68% (17/25) of cases and 12% (3/25) of controls [39], a similar rate to our 50% and 11% in OC and benign cases, respectively.
CDX2 promoter methylation was detected in 71.67% (43/60) of colorectal cancer cases [40], and 55.45% (61/110) of lung cancer cases [41]. Interestingly, CDX2 was found to inhibit Wnt signaling in lung cancer cells via suppression of c-MYC, cyclin D1 and survivin expression [41]. Importantly, β -catenin and CDX2 nuclear expression have been associated with longer survival [36]. In another ovarian cancer study, immunohistochemical staining showed that only 15.1% (8/53) of tissues show CDX2 expression and CDX2 was found to contribute to chemoresistance via the promotion of MDR1 (multidrug resistance 1) gene expression [42].
TCGA comparison to GTEx normal tissue gene expression of Notch pathway genes showed an AUC of NOCTH1 (0.603), NOTCH2 (0.532), NOTCH3 (0.739), NOTCH4 (0.948), HES1 (0.642), DLL1 (0.823), JAG2 (0.769) [27], while in comparison our smaller study showed slightly higher AUC values of NOTCH1 (0.765), NOTCH2 (0.884), HES1 (0.950), DLL1 (0.907), and lower AUCs of NOCTH3 (0.714), NOTCH4 (0.907) and JAG2 (0.500) when comparing HGSOC and benign gynecologic tumors.
A few other publications have previously hypothesized that Notch pathway genes correlate with ovarian cancer recurrence and can discriminate between high- and low-risk OC. A 10 Notch gene combination, which included HES1 and JAG2, has been able to classify TCGA and two other OC datasets into high- and low-risk groups independently from other clinical variables [43]. Another 11 Notch genes successfully differentiated OC from normal tissues and its signature predicted a 10-year prognosis with an AUC of 0.79 [44]. On the other hand, gene promoter methylation combination models have also been suggested for discrimination of HGSOC vs. benign tissues, with HOXA9 (another HOX family gene), EN1 and CA125 pre-operative levels reaching a sensitivity of 98.8% and specificity of 91.7%, while single HOXA9 had a comparatively lower sensitivity of 95% [45].
While OC is commonly stratified by tumor histology into Type I and Type II disease, it is clear that genetic and epigenetic variation exists even within histological groups. Even within the Type II (HGSOC) group, which is believed to almost unanimously present with TP53 mutations, there is significant heterogeneity in the prognosis and molecular features, specifically in gene expression. This variability was first identified by the initial TCGA ovarian cancer study and has been confirmed by many others. However, to this day, no genetic test or specific treatment has been developed to account for these variations [46]. In our study, the CTNNB1, FBXW7, NOTCH2 mRNA as well as the ARID1A promoter hypermethylation biomarkers were associated with the HGSOC tumor stage, suggesting possible implications for the prognosis, despite our study lacking more in-depth prognosis data. The identification of new OC biomarkers could also inform clinical decisions and new treatment options. Chemoresistance is one of the hallmarks of HGSOC; as highlighted above, ALX4, and CDX2 promoter methylation biomarkers are associated with chemoresistance; an early study in colon cancer cell lines showed successful restoration of CDX2 expression via epigenetic therapies, such as DNMT inhibitors [47], thus proving a novel treatment approach for HOX-related gene expression deficient tumors. Similarly, ARID1A loss is associated with poor prognosis and is targetable via EZH2, HDAC6, multikinase or PARP inhibition [48].
The proposed diagnostic panels were able to improve on the current biomarker CA125 separation of the malignant tumors and most strikingly outperformed CA125 in specificity by 43% (CA125 specificity 0.57, gene expression combination specificity = 1). CA125 serum concentration increase may be present in cases of non-gynecologic conditions, such as osteoporosis, osteoarthritis, and hypercholesterolemia, causing the CA125 specificity to fall to 73–77% [49]. Low specificity is one of the downfalls of the CA125 and other serum biomarker tests as it may result in high false positive rates and cause unnecessary treatment through invasive tests, such as biopsies, thus biomarkers with lower specificity rates are not suitable for screening purposes. The adoption of more precise genetic biomarker-based tests could improve unnecessary treatments and may be an option for OC screening.
Although our study provided a relatively small cohort of ovarian cancer cases, the Notch gene biomarker combination was able to perfectly discriminate HGSOC cases from the benign gynecologic tumor cases, albeit the best single biomarker CTNNB1 was also able to sufficiently separate these cases by itself. The inclusion of multiple biomarkers is more likely to address the complexity and heterogeneity of HGSOC, shown by the improved diagnostic accuracy and sensitivity compared to single biomarkers (a 9% improvement in sensitivity, and 8% improvement in accuracy in the gene expression biomarker model compared to CTNNB1 expression alone). However, a larger study of non-invasive samples is necessary to confirm if the proposed biomarker panels can reach the necessary specificity of 99.6% and sensitivity of 75% needed for screening tests [50].

5. Conclusions

The present study shows the utility of epigenetic and transcriptional biomarkers and their combinations in the discrimination of HGSOC from benign or non-serous gynecologic tumors. By applying logistic regression, we determined that a combination of transcriptomic and epigenetic biomarkers exhibit more diagnostic accuracy than single biomarkers alone. While further validation in larger cohorts and liquid biopsy samples are necessary for developing a feasible screening strategy for OC, the study provides the groundwork for building multiomic diagnostic approaches for OC.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines13020441/s1, Supplements Table S1 Primer sequences used in RT-qPCR and MSP experiments. Supplementary Figure S1 HOX-related (ALX4, HOPX, CDX2) and ARID1A gene promoter methylation in ovarian cancer (OC, n = 56) and benign (n = 9) cases. Supplementary Figure S2 HOX-related (ALX4, HOPX, CDX2) and ARID1A gene promoter methylation in high-grade serous ovarian cancer (HGSOC) group in relation to FIGO stage group (II—Stage 2, n = 3, III—Stage 3, n = 27, IV—Stage 4, n = 12). Supplementary Figure S3 Notch pathway related (DLL1, HES1, JAG2, NOTCH1, NOTCH2, NOTCH3, NOTCH4), Wnt pathway related (CTNNB1 and FBXW7) and ARID1A gene expression in ovarian cancer (OC, n = 56) and benign (n = 9) cases. Supplementary Figure S4 Boxplots depicting gene expression in gynecologic tumors in relation to grade group (G1—grade 1 n = 6, G3—grade 3 n = 42). Supplementary Figure S5 Boxplots depicting gene expression in HGSOC group in relation to FIGO stage group (II—Stage 2, n = 3, III—Stage 3, n = 27, IV—Stage 4, n = 12). Supplementary Figure S6 Boxplots depicting gene expression in gynecologic tumors in association with CA125 serum concentration status (increased CA125 > 35 U/mL, n = 50 vs. Norm, n = 5). Supplementary Figure S7 Promoter methylation and gene expression biomarkers separation of ovarian cancer (n = 56) and benign cases (n = 9). AUC—area under the curve, npv—negative predictive value, tpr—true positive rate, fpr—false positive rate. Supplementary Figure S8 ROC plot and its metrics for the gene expression and promoter methylation biomarker combination and serum CA125 biomarker separation of ovarian cancer (n = 55, one case is missing due to missing DLL1 expression result) and benign cases (n = 9). Gene expression biomarker combination (all 10 genes), and mixed biomarker combination (all 14 biomarkers) curves overlap. NOTCH gene expression biomarker combination includes seven genes (NOTCH1-4, HES1, DLL1, JAG2), HOX gene promoter methylation biomarker combination includes three genes (HOPX, ALX4, CDX2), serum CA125 threshold concentration considered 35 U/mL.

Author Contributions

Conceptualization—R.S. and I.V.; Data Curation—R.S., I.V., R.Č., R.G. and P.K.; Formal Analysis—I.V., R.G. and P.K.; Funding Acquisition—R.S. and P.K.; Investigation—I.V., R.G. and P.K.; Methodology—R.S. and I.V.; Project Administration—R.S., Resources—R.S. and R.Č.; Supervision—R.S. and J.R.L.; Visualization—I.V.; Writing—Original Draft Preparation—I.V. and R.S.; Writing—Review and Editing—all authors. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science Foundation from the National Cancer Institute, Lithuania. Parts of the gene expression experiments were supported by Research Council of Lithuania grant No. P-SV-23-36.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki. The study was approved by the Regional Bioethics Committee No. 158200-18/5-988-539.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request due to patient data privacy concerns.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
FCFold change
HGSOCHigh-grade serous ovarian cancer
HOXHomeobox domain
MSPmethylation specific PCR

References

  1. Bray, F.; Laversanne, M.; Sung, H.; Ferlay, J.; Siegel, R.L.; Soerjomataram, I.; Jemal, A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J. Clin. 2024, 74, 229–263. [Google Scholar] [CrossRef] [PubMed]
  2. Torre, L.A.; Trabert, B.; DeSantis, C.E.; Miller, K.D.; Samimi, G.; Runowicz, C.D.; Gaudet, M.M.; Jemal, A.; Siegel, R.L. Ovarian cancer statistics, 2018. CA A Cancer J. Clin. 2018, 68, 284–296. [Google Scholar] [CrossRef] [PubMed]
  3. Allemani, C.; Matsuda, T.; Carlo, V.D.; Harewood, R.; Matz, M.; Nikšić, M.; Bonaventure, A.; Valkov, M.; Johnson, C.J.; Estève, J.; et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): Analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet 2018, 391, 1023–1075. [Google Scholar] [CrossRef] [PubMed]
  4. Brincat, M.R.; Mira, A.R.; Lawrence, A. Current and Emerging Strategies for Tubo-Ovarian Cancer Diagnostics. Diagnostics 2023, 13, 3331. [Google Scholar] [CrossRef]
  5. Ledermann, J.A.; Matias-Guiu, X.; Amant, F.; Concin, N.; Davidson, B.; Fotopoulou, C.; González-Martin, A.; Gourley, C.; Leary, A.; Lorusso, D.; et al. ESGO–ESMO–ESP consensus conference recommendations on ovarian cancer: Pathology and molecular biology and early, advanced and recurrent disease. Ann. Oncol. 2024, 35, 248–266. [Google Scholar] [CrossRef]
  6. Zhang, M.; Cheng, S.; Jin, Y.; Zhao, Y.; Wang, Y. Roles of CA125 in diagnosis, prediction, and oncogenesis of ovarian cancer. Biochim. Biophys. Acta-(BBA)-Rev. Cancer 2021, 1875, 188503. [Google Scholar] [CrossRef]
  7. Reyes, H.D.; Devor, E.J.; Warrier, A.; Newtson, A.M.; Mattson, J.; Wagner, V.; Duncan, G.N.; Leslie, K.K.; Gonzalez-Bosquet, J. Differential DNA methylation in high-grade serous ovarian cancer (HGSOC) is associated with tumor behavior. Sci. Rep. 2019, 9, 17996. [Google Scholar] [CrossRef]
  8. Bell, D.; Berchuck, A.; Birrer, M.; Chien, J.; Cramer, D.W.; Dao, F.; Dhir, R.; Disaia, P.; Gabra, H.; Glenn, P.; et al. Integrated genomic analyses of ovarian carcinoma. Nature 2011, 474, 609–615. [Google Scholar] [CrossRef]
  9. Teer, J.K.; Yoder, S.; Gjyshi, A.; Nicosia, S.V.; Zhang, C.; Monteiro, A.N. Mutational heterogeneity in non-serous ovarian cancers. Sci. Rep. 2017, 7, 9728. [Google Scholar] [CrossRef]
  10. Kessler, M.; Hoffmann, K.; Brinkmann, V.; Thieck, O.; Jackisch, S.; Toelle, B.; Berger, H.; Mollenkopf, H.J.; Mangler, M.; Sehouli, J.; et al. The Notch and Wnt pathways regulate stemness and differentiation in human fallopian tube organoids. Nat. Commun. 2015, 6, 8989. [Google Scholar] [CrossRef]
  11. Anusewicz, D.; Orzechowska, M.; Bednarek, A.K. Notch Signaling Pathway in Cancer-Review with Bioinformatic Analysis. Cancers 2021, 13, 768. [Google Scholar] [CrossRef] [PubMed]
  12. Rodrigues, M.F.S.D.; Esteves, C.M.; Xavier, F.C.A.; Nunes, F.D. Methylation status of homeobox genes in common human cancers. Genomics 2016, 108, 185–193. [Google Scholar] [CrossRef] [PubMed]
  13. Paço, A.; de Bessa Garcia, S.A.; Freitas, R. Methylation in HOX Clusters and Its Applications in Cancer Therapy. Cells 2020, 9, 1613. [Google Scholar] [CrossRef] [PubMed]
  14. Xie, H.; Chen, P.; Huang, H.W.; Liu, L.P.; Zhao, F. Reactive oxygen species downregulate ARID1A expression via its promoter methylation during the pathogenesis of endometriosis. Eur. Rev. Med. Pharm. Sci. 2017, 21, 4509–4515. [Google Scholar]
  15. Berns, K.; Caumanns, J.J.; Hijmans, E.M.; Gennissen, A.M.; Severson, T.M.; Evers, B.; Wisman, G.B.A.; Meersma, G.J.; Lieftink, C.; Beijersbergen, R.L.; et al. ARID1A mutation sensitizes most ovarian clear cell carcinomas to BET inhibitors. Oncogene 2018, 37, 4611–4625. [Google Scholar] [CrossRef]
  16. Zhao, L.; Guo, H.; Chen, X.; Zhang, W.; He, Q.; Ding, L.; Yang, B. Tackling drug resistance in ovarian cancer with epigenetic targeted drugs. Eur. J. Pharmacol. 2022, 927, 175071. [Google Scholar] [CrossRef]
  17. Mandal, J.; Mandal, P.; Wang, T.L.; Shih, I.M. Treating ARID1A mutated cancers by harnessing synthetic lethality and DNA damage response. J. Biomed. Sci. 2022, 29, 71. [Google Scholar] [CrossRef]
  18. Pearson, H.; Stirling, D. DNA Extraction from Tissue. Methods Mol. Biol. 2003, 226, 33–34. [Google Scholar] [CrossRef]
  19. Rio, D.C.; Ares, M.; Hannon, G.J.; Nilsen, T.W. Purification of RNA using TRIzol (TRI reagent). Cold Spring Harb. Protoc. 2010, 2010, pdb-prot5439. [Google Scholar] [CrossRef]
  20. Peterson, S.M.; Freeman, J.L. RNA isolation from embryonic zebrafish and cDNA synthesis for gene expression analysis. J. Vis. Exp. Jove 2009, 30, 1470. [Google Scholar] [CrossRef]
  21. Torrejón, D.; Cárdenas, J.; Juárez, D.; Espinoza, J.; Proleón, A.; Agurto-Arteaga, A.; Lazo, F.; Leguía, M.; Urra, F.A.; Sánchez, E.F.; et al. Comparison of Four Methods of RNA Extraction and cDNA Synthesis from The Venom of Peruvian Snakes of the Genus Bothrops of Clinical Importance. Int. J. Mol. Sci. 2023, 24, 11161. [Google Scholar] [CrossRef] [PubMed]
  22. Welch, B.L. The Significance of the Difference Between Two Means When the Population Variances Are Unequal. Biometrika 1938, 29, 350–362. [Google Scholar] [CrossRef]
  23. Delacre, M.; Lakens, D.; Leys, C. Why Psychologists Should by Default Use Welch’s t-test Instead of Student’s t-test. Int. Rev. Soc. Psychol. 2017, 30, 92–101. [Google Scholar] [CrossRef]
  24. Benjaminit, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 1995, 57, 289–300. [Google Scholar] [CrossRef]
  25. Bocchicchio, S.; Tesone, M.; Irusta, G. Convergence of Wnt and Notch signaling controls ovarian cancer cell survival. J. Cell. Physiol. 2019, 234, 22130–22143. [Google Scholar] [CrossRef]
  26. Kumar, V.; Vashishta, M.; Kong, L.; Wu, X.; Lu, J.J.; Guha, C.; Dwarakanath, B.S. The Role of Notch, Hedgehog, and Wnt Signaling Pathways in the Resistance of Tumors to Anticancer Therapies. Front. Cell Dev. Biol. 2021, 9, 650772. [Google Scholar] [CrossRef]
  27. Chen, B.; Jiang, K.; Wang, H.; Miao, L.; Lin, X.; Chen, Q.; Jing, L.; Lu, X. NOTCH Pathway Genes in Ovarian Cancer: Clinical Significance and Associations with Immune Cell Infiltration. Front. Biosci.-Landmark 2023, 28, 220. [Google Scholar] [CrossRef]
  28. AmeliMojarad, M.; AmeliMojarad, M.; Cui, X.; Shariati, P. Pan-cancer analysis of CTNNB1 with potential as a therapeutic target for human tumorigenesis. Inform. Med. Unlocked 2023, 42, 101331. [Google Scholar] [CrossRef]
  29. Fiore, R.D.; Suleiman, S.; Drago-Ferrante, R.; Subbannayya, Y.; Suleiman, S.; Vasileva-Slaveva, M.; Yordanov, A.; Pentimalli, F.; Giordano, A.; Calleja-Agius, J. The Role of FBXW7 in Gynecologic Malignancies. Cells 2023, 12, 1415. [Google Scholar] [CrossRef]
  30. Holland, P.W.; Booth, H.A.F.; Bruford, E.A. Classification and nomenclature of all human homeobox genes. BMC Biol. 2007, 5, 47. [Google Scholar] [CrossRef]
  31. Celik, S.; Logsdon, B.A.; Battle, S.; Drescher, C.W.; Rendi, M.; Hawkins, R.D.; Lee, S.I. Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer. Genome Med. 2016, 8, 66. [Google Scholar] [CrossRef] [PubMed]
  32. Ooki, A.; Yamashita, K.; Kikuchi, S.; Sakuramoto, S.; Katada, N.; Kokubo, K.; Kobayashi, H.; Kim, M.S.; Sidransky, D.; Watanabe, M. Potential utility of HOP homeobox gene promoter methylation as a marker of tumor aggressiveness in gastric cancer. Oncogene 2010, 29, 3263–3275. [Google Scholar] [CrossRef] [PubMed]
  33. Katoh, H.; Yamashita, K.; Waraya, M.; Margali, O.; Ooki, A.; Tamaki, H.; Sakagami, H.; Kokubo, K.; Sidransky, D.; Watanabe, M. Epigenetic silencing of HOPX promotes cancer progression in colorectal cancer. Neoplasia 2012, 14, 559–571. [Google Scholar] [CrossRef]
  34. Yamaguchi, S.; Asanoma, K.; Takao, T.; Kato, K.; Wake, N. Homeobox gene HOPX is epigenetically silenced in human uterine endometrial cancer and suppresses estrogen-stimulated proliferation of cancer cells by inhibiting serum response factor. Int. J. Cancer 2009, 124, 2577–2588. [Google Scholar] [CrossRef]
  35. Yuan, H.; Kajiyama, H.; Ito, S.; Chen, D.; Shibata, K.; Hamaguchi, M.; Kikkawa, F.; Senga, T. HOXB13 and ALX4 induce SLUG expression for the promotion of EMT and cell invasion in ovarian cancer cells. Oncotarget 2015, 6, 13359. [Google Scholar] [CrossRef]
  36. Wang, Y.; Lei, L.; Chi, Y.G.; Liu, L.B.; Yang, B.P. A comprehensive understanding of ovarian carcinoma survival prognosis by novel biomarkers. Eur. Rev. Med. Pharmacol. Sci. 2019, 23, 8257–8264. [Google Scholar] [CrossRef]
  37. Duan, C.; Yan, Z.; Wu, C.; Zhou, X.; Bao, W. DNA methylation characteristics associated with chemotherapy resistance in epithelial ovarian cancer. Heliyon 2024, 10, 27212. [Google Scholar] [CrossRef]
  38. Yang, J.; Han, F.; Liu, W.; Chen, H.; Hao, X.; Jiang, X.; Yin, L.; Huang, Y.; Cao, J.; Zhang, H.; et al. ALX4, an epigenetically down regulated tumor suppressor, inhibits breast cancer progression by interfering Wnt/β-catenin pathway. J. Exp. Clin. Cancer Res. 2017, 36, 170. [Google Scholar] [CrossRef]
  39. Salehi, R.; Atapour, N.; Vatandoust, N.; Farahani, N.; Ahangari, F.; Salehi, A. Methylation pattern of ALX4 gene promoter as a potential biomarker for blood-based early detection of colorectal cancer. Adv. Biomed. Res. 2015, 4, 252. [Google Scholar] [CrossRef]
  40. Wang, Y.; Li, Z.; Li, W.; Liu, S.; Han, B. Methylation of CDX2 gene promoter in the prediction of treatment efficacy in colorectal cancer. Oncol. Lett. 2018, 16, 195–198. [Google Scholar] [CrossRef]
  41. Liu, X.; Zhang, X.; Zhan, Q.; Brock, M.V.; Herman, J.G.; Guo, M. CDX2 serves as a Wnt signaling inhibitor and is frequently methylated in lung cancer. Cancer Biol. Ther. 2012, 13, 1152–1157. [Google Scholar] [CrossRef] [PubMed]
  42. Koh, I.; Hinoi, T.; Sentani, K.; Hirata, E.; Nosaka, S.; Niitsu, H.; Miguchi, M.; Adachi, T.; Yasui, W.; Ohdan, H.; et al. Regulation of multidrug resistance 1 expression by CDX2 in ovarian mucinous adenocarcinoma. Cancer Med. 2016, 5, 1546–1555. [Google Scholar] [CrossRef] [PubMed]
  43. Chen, F.; Liu, N. A 10-gene expression signature of Notch pathway predicts recurrence in ovarian carcinoma. Oncol. Lett. 2015, 10, 1704–1708. [Google Scholar] [CrossRef] [PubMed]
  44. Pi, Y.; Sun, F.; Zhang, Z.; Liu, X.; Lou, G. A Novel Notch-Related Gene Signature for Prognosis and Immune Response Prediction in Ovarian Cancer. Medicina 2023, 59, 1277. [Google Scholar] [CrossRef]
  45. Montavon, C.; Gloss, B.S.; Warton, K.; Barton, C.A.; Statham, A.L.; Scurry, J.P.; Tabor, B.; Nguyen, T.V.; Qu, W.; Samimi, G.; et al. Prognostic and diagnostic significance of DNA methylation patterns in high grade serous ovarian cancer. Gynecol. Oncol. 2012, 124, 582–588. [Google Scholar] [CrossRef]
  46. Tavares, V.; Marques, I.S.; de Melo, I.G.; Assis, J.; Pereira, D.; Medeiros, R. Paradigm Shift: A Comprehensive Review of Ovarian Cancer Management in an Era of Advancements. Int. J. Mol. Sci. 2024, 25, 1845. [Google Scholar] [CrossRef]
  47. Graule, J.; Uth, K.; Fischer, E.; Centeno, I.; Galván, J.A.; Eichmann, M.; Rau, T.T.; Langer, R.; Dawson, H.; Nitsche, U.; et al. CDX2 in colorectal cancer is an independent prognostic factor and regulated by promoter methylation and histone deacetylation in tumors of the serrated pathway. Clin. Epigenetics 2018, 10, 120. [Google Scholar] [CrossRef]
  48. Khalique, S.; Naidoo, K.; Attygalle, A.D.; Kriplani, D.; Daley, F.; Lowe, A.; Campbell, J.; Jones, T.; Hubank, M.; Fenwick, K.; et al. Optimised ARID1A immunohistochemistry is an accurate predictor of ARID1A mutational status in gynaecological cancers. J. Pathol. Clin. Res. 2018, 4, 154–166. [Google Scholar] [CrossRef]
  49. Englisz, A.; Smycz-Kubańska, M.; Mielczarek-Palacz, A. Sensitivity and Specificity of Selected Biomarkers and Their Combinations in the Diagnosis of Ovarian Cancer. Diagnostics 2024, 14, 949. [Google Scholar] [CrossRef]
  50. Clarke-Pearson, D.L. Clinical practice. Screening for ovarian cancer. N. Engl. J. Med. 2009, 361, 170–177. [Google Scholar] [CrossRef]
Biomedicines 13 00441 g001
Figure 1. A heatmap of promoter methylation and relative gene expression in gynecologic tumors. ARID1A met—ARID1A promoter methylation, OC—ovarian cancer, HGSOC—high-grade serous ovarian cancer, other—other malignant gynecologic tumors, G1—grade 1, G3—grade 3, CA125 increase—CA125 serum concentration > 35 U/mL, NA—data not available.
Figure 1. A heatmap of promoter methylation and relative gene expression in gynecologic tumors. ARID1A met—ARID1A promoter methylation, OC—ovarian cancer, HGSOC—high-grade serous ovarian cancer, other—other malignant gynecologic tumors, G1—grade 1, G3—grade 3, CA125 increase—CA125 serum concentration > 35 U/mL, NA—data not available.
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Figure 2. HOX-related (ALX4, HOPX, CDX2) and ARID1A gene promoter methylation in high-grade ovarian cancer (HGSOC, n = 42), non-HGOSC ovarian cancer (Other, n = 14) and benign (n = 9) cases.
Figure 2. HOX-related (ALX4, HOPX, CDX2) and ARID1A gene promoter methylation in high-grade ovarian cancer (HGSOC, n = 42), non-HGOSC ovarian cancer (Other, n = 14) and benign (n = 9) cases.
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Figure 3. Boxplots depicting gene expression in three gynecologic tumor groups: benign (n = 9, blue), high-grade serous ovarian cancer (HGSOC, n = 42, dark pink), and non-HGSOC gynecologic tumors (Other, n = 14, light pink).
Figure 3. Boxplots depicting gene expression in three gynecologic tumor groups: benign (n = 9, blue), high-grade serous ovarian cancer (HGSOC, n = 42, dark pink), and non-HGSOC gynecologic tumors (Other, n = 14, light pink).
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Figure 4. ROC plot and its metrics for the gene expression and promoter methylation biomarkers separation of the HGSOC (n = 42) and benign cases (n = 9). ALX4 and CDX2 gene promoter methylation biomarker curves overlap. AUC—area under the curve, npv—negative predictive value, tpr—true positive rate, fpr—false positive rate.
Figure 4. ROC plot and its metrics for the gene expression and promoter methylation biomarkers separation of the HGSOC (n = 42) and benign cases (n = 9). ALX4 and CDX2 gene promoter methylation biomarker curves overlap. AUC—area under the curve, npv—negative predictive value, tpr—true positive rate, fpr—false positive rate.
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Figure 5. Receiver operator characteristic (ROC) plot and its metrics for the gene expression, promoter methylation biomarker, and serum CA125 biomarker separation of the HGSOC (n = 42) and benign cases (n = 9). Gene expression biomarker combination (all 10 genes), Mixed biomarker combination (all 14 biomarkers), and Notch gene expression biomarker combination (NOTCH1-4, HES1, DLL1, JAG2) curves overlap. HOX gene promoter methylation biomarker combination includes three genes (HOPX, ALX4, CDX2), serum CA125 threshold concentration considered 35 U/mL.
Figure 5. Receiver operator characteristic (ROC) plot and its metrics for the gene expression, promoter methylation biomarker, and serum CA125 biomarker separation of the HGSOC (n = 42) and benign cases (n = 9). Gene expression biomarker combination (all 10 genes), Mixed biomarker combination (all 14 biomarkers), and Notch gene expression biomarker combination (NOTCH1-4, HES1, DLL1, JAG2) curves overlap. HOX gene promoter methylation biomarker combination includes three genes (HOPX, ALX4, CDX2), serum CA125 threshold concentration considered 35 U/mL.
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Table 1. Clinical features of the study cohort. NA—data not available; SD—standard deviation.
Table 1. Clinical features of the study cohort. NA—data not available; SD—standard deviation.
Clinical FeatureOvarian CancerBenign Gynecologic TumorsAll
n56965
Histology group
HGSOC42 (75.00%) 42 (64.62%)
Other gynecologic malignant tumors14 (25.00%) 14 (21.54%)
Benign gynecologic tumors 9 (100.00%)9 (13.85%)
Average age at diagnosis (±SD)59.16 (±9.69)53.67 (±9.33)58.40 (±9.76)
Median CA125 U/mL (min–max)478.50 (34.00–5384.00)18.22 (10.00–118.00)419.30 (10.00–5384.00)
FIGO stage
I9 (16.07%) 9 (13.85%)
II3 (5.36%) 3 (4.62%)
III30 (53.57 %) 29 (44.62%)
IV14 (25.00%) 14 (21.54%)
NA 9 (100.00%)9 (13.85%)
Grade
G16 (10.71%) 7 (10.61%)
G342 (75.00%) 42 (63.64%)
NA8 (14.29%)9 (100.00%)17 (25.76%)
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Vaicekauskaitė, I.; Kazlauskaitė, P.; Gineikaitė, R.; Čiurlienė, R.; Lazutka, J.R.; Sabaliauskaitė, R. Integrative Analysis of Gene Expression and Promoter Methylation to Differentiate High-Grade Serous Ovarian Cancer from Benign Tumors. Biomedicines 2025, 13, 441. https://doi.org/10.3390/biomedicines13020441

AMA Style

Vaicekauskaitė I, Kazlauskaitė P, Gineikaitė R, Čiurlienė R, Lazutka JR, Sabaliauskaitė R. Integrative Analysis of Gene Expression and Promoter Methylation to Differentiate High-Grade Serous Ovarian Cancer from Benign Tumors. Biomedicines. 2025; 13(2):441. https://doi.org/10.3390/biomedicines13020441

Chicago/Turabian Style

Vaicekauskaitė, Ieva, Paulina Kazlauskaitė, Rugilė Gineikaitė, Rūta Čiurlienė, Juozas Rimantas Lazutka, and Rasa Sabaliauskaitė. 2025. "Integrative Analysis of Gene Expression and Promoter Methylation to Differentiate High-Grade Serous Ovarian Cancer from Benign Tumors" Biomedicines 13, no. 2: 441. https://doi.org/10.3390/biomedicines13020441

APA Style

Vaicekauskaitė, I., Kazlauskaitė, P., Gineikaitė, R., Čiurlienė, R., Lazutka, J. R., & Sabaliauskaitė, R. (2025). Integrative Analysis of Gene Expression and Promoter Methylation to Differentiate High-Grade Serous Ovarian Cancer from Benign Tumors. Biomedicines, 13(2), 441. https://doi.org/10.3390/biomedicines13020441

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