Special Issue "Application of Bioinformatics in Cancers"

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 74951

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Special Issue Editor

Dr. J. Chad Brenner
E-Mail Website
Guest Editor
Department of Otolaryngology—Head and Neck SurgeryDirector, Michigan Otolaryngology and Translational Oncology LaboratoryUniversity of Michigan Health Systems, Ann Arbor, MI 48109, USA
Interests: functional genomic; proteomic and bioinformatics approaches in cancer; sequencing the exomes and transcriptomes of head and neck cancer; drug sensitivities
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Special Issue Information

Dear Colleagues,

Bioinformatics applications in cancer have rapidly evolved over the past several years. Ever since its initial implementation, next generation sequencing has altered our understanding of cancer biology, and the approaches to analyze more and more complex datasets have also become increasingly complex.  Routine bioinformatics pipelines now range from those that rapidly detect and predict functional impact of molecular alterations to those that quantify changes to the tumor microenvironment. For example, several tools that analyze tumor-immune interactions have been successfully developed to assess tumor infiltrating lymphocyte content, microsatellite instability, total mutational burden and neoantigen presentation. Further complexity of integrated omics-based analysis is also now coupled with the emergence of modern machine learning and network-based approaches to analyze large datasets in the context of publicly available resources, such as the cancer genome atlas.

While much of the focus has so far been on annotating molecular alterations as well as infiltrating cell types or cell states in ideal sequencing conditions, alternative and application-specific approaches are now emerging that improve on a wide variety of established analysis techniques. These include techniques that range from improved quantification of copy number and gene expression from formalin fixed tissues as well as applications that require high sensitivity such as the quantification of tumor mutations from liquid biopsies (circulating cell free DNA).  Further novel applications attempt to improve the ability to analyze the distribution and molecular impact of complicated genetic features such as repetitive or transposable endogenous elements (e.g., LINE-1) as well as exogenous genetic elements (e.g., human papilloma virus).

As we develop a better understanding of the limitations of these new informatics approaches, we can ultimately hope to apply these techniques to existing datasets and build well-annotated databases of easily accessible information that can be leveraged in multi-variable analysis pipelines. Similar to the success of SIGdb and cBioPortal, this should help yield new diagnostic and prognostic/predictive biomarkers for standard interventional modalities as well as emerging areas like immuno-oncology, and areas of unmet clinical need. This Special Issue will highlight the current state of the art in bioinformatics applications in cancer biology, and infer future prospects for improving informatics applications through artificial intelligence and machine learning approaches.

Dr. J. Chad Brenner
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bioinformatics
  • Machine learning
  • Artificial Intelligence
  • Network Analysis
  • Single cell sequencing
  • Circulating tumor DNA (ctDNA)
  • Neoantigen Prediction
  • Precision medicine
  • Computational Immunology

Published Papers (27 papers)

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Editorial

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Editorial
Applications of Bioinformatics in Cancer
Cancers 2019, 11(11), 1630; https://doi.org/10.3390/cancers11111630 - 24 Oct 2019
Cited by 5 | Viewed by 1912
Abstract
This series of 25 articles (22 original articles, 3 reviews) is presented by international leaders in bioinformatics and biostatistics [...] Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)

Research

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Article
FoxO3a Inhibits Tamoxifen-Resistant Breast Cancer Progression by Inducing Integrin α5 Expression
Cancers 2022, 14(1), 214; https://doi.org/10.3390/cancers14010214 - 02 Jan 2022
Cited by 1 | Viewed by 558
Abstract
Resistance to endocrine therapy is still a major clinical challenge in the management of estrogen receptor α-positive (ERα+) breast cancer (BC). Here, the role of the Forkhead box class O (FoxO)3a transcription factor in tumor progression has been evaluated in tamoxifen-resistant BC cells [...] Read more.
Resistance to endocrine therapy is still a major clinical challenge in the management of estrogen receptor α-positive (ERα+) breast cancer (BC). Here, the role of the Forkhead box class O (FoxO)3a transcription factor in tumor progression has been evaluated in tamoxifen-resistant BC cells (TamR), expressing lower levels of FoxO3a compared to sensitive ones. FoxO3a re-expression reduces TamR motility (wound-healing and transmigration assays) and invasiveness (matrigel transwell invasion assays) through the mRNA (qRT-PCR) and protein (Western blot) induction of the integrin α5 subunit of the α5β1 fibronectin receptor, a well-known membrane heterodimer controlling cell adhesion and signaling. The induction occurs through FoxO3a binding to a specific Forkhead responsive core sequence located on the integrin α5 promoter (cloning, luciferase, and ChIP assays). Moreover, FoxO3a failed to inhibit migration and invasion in integrin α5 silenced (siRNA) cells, demonstrating integrin α5 involvement in both processes. Finally, using large-scale gene expression data sets, a strong positive correlation between FoxO3a and integrin α5 in ERα+, but not in ER-negative (ERα−), BC patients emerged. Altogether, our data show how the oncosuppressor FoxO3a, by increasing the expression of its novel transcriptional target integrin α5, reverts the phenotype of endocrine-resistant BC toward a lower aggressiveness. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning
Cancers 2019, 11(10), 1606; https://doi.org/10.3390/cancers11101606 - 21 Oct 2019
Cited by 7 | Viewed by 2524
Abstract
The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. [...] Read more.
The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Mining of Self-Organizing Map Gene-Expression Portraits Reveals Prognostic Stratification of HPV-Positive Head and Neck Squamous Cell Carcinoma
Cancers 2019, 11(8), 1057; https://doi.org/10.3390/cancers11081057 - 26 Jul 2019
Cited by 13 | Viewed by 1856
Abstract
Patients (pts) with head and neck squamous cell carcinoma (HNSCC) have different epidemiologic, clinical, and outcome behaviors in relation to human papillomavirus (HPV) infection status, with HPV-positive patients having a 70% reduction in their risk of death. Little is known about the molecular [...] Read more.
Patients (pts) with head and neck squamous cell carcinoma (HNSCC) have different epidemiologic, clinical, and outcome behaviors in relation to human papillomavirus (HPV) infection status, with HPV-positive patients having a 70% reduction in their risk of death. Little is known about the molecular heterogeneity in HPV-related cases. In the present study, we aim to disclose the molecular subtypes with potential biological and clinical relevance. Through a literature review, 11 studies were retrieved with a total of 346 gene-expression data points from HPV-positive HNSCC pts. Meta-analysis and self-organizing map (SOM) approaches were used to disclose relevant meta-gene portraits. Unsupervised consensus clustering provided evidence of three biological subtypes in HPV-positive HNSCC: Cl1, immune-related; Cl2, epithelial–mesenchymal transition-related; Cl3, proliferation-related. This stratification has a prognostic relevance, with Cl1 having the best outcome, Cl2 the worst, and Cl3 an intermediate survival rate. Compared to recent literature, which identified immune and keratinocyte subtypes in HPV-related HNSCC, we confirmed the former and we separated the latter into two clusters with different biological and prognostic characteristics. At present, this paper reports the largest meta-analysis of HPV-positive HNSCC studies and offers a promising molecular subtype classification. Upon further validation, this stratification could improve patient selection and pave the way for the development of a precision medicine therapeutic approach. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
False Discovery Rate Control in Cancer Biomarker Selection Using Knockoffs
Cancers 2019, 11(6), 744; https://doi.org/10.3390/cancers11060744 - 29 May 2019
Cited by 6 | Viewed by 1662
Abstract
The discovery of biomarkers that are informative for cancer risk assessment, diagnosis, prognosis and treatment predictions is crucial. Recent advances in high-throughput genomics make it plausible to select biomarkers from the vast number of human genes in an unbiased manner. Yet, control of [...] Read more.
The discovery of biomarkers that are informative for cancer risk assessment, diagnosis, prognosis and treatment predictions is crucial. Recent advances in high-throughput genomics make it plausible to select biomarkers from the vast number of human genes in an unbiased manner. Yet, control of false discoveries is challenging given the large number of genes versus the relatively small number of patients in a typical cancer study. To ensure that most of the discoveries are true, we employ a knockoff procedure to control false discoveries. Our method is general and flexible, accommodating arbitrary covariate distributions, linear and nonlinear associations, and survival models. In simulations, our method compares favorably to the alternatives; its utility of identifying important genes in real clinical applications is demonstrated by the identification of seven genes associated with Breslow thickness in skin cutaneous melanoma patients. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Genomic Profiling of the Steroidogenic Acute Regulatory Protein in Breast Cancer: In Silico Assessments and a Mechanistic Perspective
Cancers 2019, 11(5), 623; https://doi.org/10.3390/cancers11050623 - 04 May 2019
Cited by 4 | Viewed by 1843
Abstract
Cancer is a multifactorial condition with aberrant growth of cells. A substantial number of cancers, breast in particular, are hormone sensitive and evolve due to malfunction in the steroidogenic machinery. Breast cancer, one of the most prevalent form of cancers in women, is [...] Read more.
Cancer is a multifactorial condition with aberrant growth of cells. A substantial number of cancers, breast in particular, are hormone sensitive and evolve due to malfunction in the steroidogenic machinery. Breast cancer, one of the most prevalent form of cancers in women, is primarily stimulated by estrogens. Steroid hormones are made from cholesterol, and regulation of steroid/estrogen biosynthesis is essentially influenced by the steroidogenic acute regulatory (StAR) protein. Although the impact of StAR in breast cancer remains a mystery, we recently reported that StAR protein is abundantly expressed in hormone sensitive breast cancer, but not in its non-cancerous counterpart. Herein, we analyzed genomic profiles, hormone receptor expression, mutation, and survival for StAR and steroidogenic enzyme genes in a variety of hormone sensitive cancers. These profiles were specifically assessed in breast cancer, exploiting The Cancer Genome Atlas (TCGA) datasets. Whereas StAR and key steroidogenic enzyme genes evaluated (CYP11A1, HSD3B, CYP17A1, CYP19A1, and HSD17B) were altered to varying levels in these hormone responsive cancers, amplification of the StAR gene was correlated with poor overall survival of patients afflicted with breast cancer. Amplification of the StAR gene and its correlation to survival was also verified in a number of breast cancer studies. Additionally, TCGA breast cancer tumors associated with aberrant high expression of StAR mRNA were found to be an unfavorable risk factor for survival of patients with breast cancer. Further analyses of tumors, nodal status, and metastases of breast cancer tumors expressing StAR mRNA displayed cancer deaths in stage specific manners. The majority of these tumors were found to express estrogen and progesterone receptors, signifying a link between StAR and luminal subtype breast cancer. Collectively, analyses of genomic and molecular profiles of key steroidogenic factors provide novel insights that StAR plays an important role in the biologic behavior and/or pathogenesis of hormone sensitive breast cancer. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Identification of Novel MicroRNAs and Their Diagnostic and Prognostic Significance in Oral Cancer
Cancers 2019, 11(5), 610; https://doi.org/10.3390/cancers11050610 - 30 Apr 2019
Cited by 70 | Viewed by 3897
Abstract
Background: Oral cancer is one of the most prevalent cancers worldwide. Despite that the oral cavity is easily accessible for clinical examinations, oral cancers are often not promptly diagnosed. Furthermore, to date no effective biomarkers are available for oral cancer. Therefore, there [...] Read more.
Background: Oral cancer is one of the most prevalent cancers worldwide. Despite that the oral cavity is easily accessible for clinical examinations, oral cancers are often not promptly diagnosed. Furthermore, to date no effective biomarkers are available for oral cancer. Therefore, there is an urgent need to identify novel biomarkers able to improve both diagnostic and prognostic strategies. In this context, the development of innovative high-throughput technologies for molecular and epigenetics analyses has generated a huge amount of data that may be used for the identification of new cancer biomarkers. Methods: In the present study, GEO DataSets and TCGA miRNA profiling datasets were analyzed in order to identify miRNAs with diagnostic and prognostic significance. Furthermore, several computational approaches were adopted to establish the functional roles of these miRNAs. Results: The analysis of datasets allowed for the identification of 11 miRNAs with a potential diagnostic role for oral cancer. Additionally, eight miRNAs associated with patients’ prognosis were also identified; six miRNAs predictive of patients’ overall survival (OS) and one, hsa-miR-let.7i-3p, associated with tumor recurrence. Conclusions: The integrated analysis of different miRNA expression datasets allows for the identification of a set of miRNAs that, after validation, may be used for the early detection of oral cancers. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Histopathological Imaging–Environment Interactions in Cancer Modeling
Cancers 2019, 11(4), 579; https://doi.org/10.3390/cancers11040579 - 24 Apr 2019
Cited by 5 | Viewed by 1387
Abstract
Histopathological imaging has been routinely conducted in cancer diagnosis and recently used for modeling other cancer outcomes/phenotypes such as prognosis. Clinical/environmental factors have long been extensively used in cancer modeling. However, there is still a lack of study exploring possible interactions of histopathological [...] Read more.
Histopathological imaging has been routinely conducted in cancer diagnosis and recently used for modeling other cancer outcomes/phenotypes such as prognosis. Clinical/environmental factors have long been extensively used in cancer modeling. However, there is still a lack of study exploring possible interactions of histopathological imaging features and clinical/environmental risk factors in cancer modeling. In this article, we explore such a possibility and conduct both marginal and joint interaction analysis. Novel statistical methods, which are “borrowed” from gene–environment interaction analysis, are employed. Analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) data is conducted. More specifically, we examine a biomarker of lung function as well as overall survival. Possible interaction effects are identified. Overall, this study can suggest an alternative way of cancer modeling that innovatively combines histopathological imaging and clinical/environmental data. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Association Analysis of Deep Genomic Features Extracted by Denoising Autoencoders in Breast Cancer
Cancers 2019, 11(4), 494; https://doi.org/10.3390/cancers11040494 - 07 Apr 2019
Cited by 12 | Viewed by 1997
Abstract
Artificial intelligence-based unsupervised deep learning (DL) is widely used to mine multimodal big data. However, there are few applications of this technology to cancer genomics. We aim to develop DL models to extract deep features from the breast cancer gene expression data and [...] Read more.
Artificial intelligence-based unsupervised deep learning (DL) is widely used to mine multimodal big data. However, there are few applications of this technology to cancer genomics. We aim to develop DL models to extract deep features from the breast cancer gene expression data and copy number alteration (CNA) data separately and jointly. We hypothesize that the deep features are associated with patients’ clinical characteristics and outcomes. Two unsupervised denoising autoencoders (DAs) were developed to extract deep features from TCGA (The Cancer Genome Atlas) breast cancer gene expression and CNA data separately and jointly. A heat map was used to view and cluster patients into subgroups based on these DL features. Fisher’s exact test and Pearson’ Chi-square test were applied to test the associations of patients’ groups and clinical information. Survival differences between the groups were evaluated by Kaplan–Meier (KM) curves. Associations between each of the features and patient’s overall survival were assessed using Cox’s proportional hazards (COX-PH) model and a risk score for each feature set from the different omics data sets was generated from the survival regression coefficients. The risk scores for each feature set were binarized into high- and low-risk patient groups to evaluate survival differences using KM curves. Furthermore, the risk scores were traced back to their gene level DAs weights so that the three gene lists for each of the genomic data points were generated to perform gene set enrichment analysis. Patients were clustered into two groups based on concatenated features from the gene expression and CNA data and these two groups showed different overall survival rates (p-value = 0.049) and different ER (Estrogen receptor) statuses (p-value = 0.002, OR (odds ratio) = 0.626). All the risk scores from the gene expression and CNA data and their concatenated one were significantly associated with breast cancer survival. The patients with the high-risk group were significantly associated with patients’ worse outcomes (p-values ≤ 0.0023). The concatenated risk score was enriched by the AMP-activated protein kinase (AMPK) signaling pathway, the regulation of DNA-templated transcription, the regulation of nucleic acid-templated transcription, the regulation of apoptotic process, the positive regulation of gene expression, the positive regulation of cell proliferation, heart morphogenesis, the regulation of cellular macromolecule biosynthetic process, with FDR (false discovery rate) less than 0.05. We confirmed DAs can effectively extract meaningful genomic features from genomic data and concatenating multiple data sources can improve the significance of the features associated with breast cancer patients’ clinical characteristics and outcomes. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach
Cancers 2019, 11(3), 431; https://doi.org/10.3390/cancers11030431 - 26 Mar 2019
Cited by 24 | Viewed by 2996
Abstract
Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated [...] Read more.
Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
A Systematic Pan-Cancer Analysis of Genetic Heterogeneity Reveals Associations with Epigenetic Modifiers
Cancers 2019, 11(3), 391; https://doi.org/10.3390/cancers11030391 - 20 Mar 2019
Cited by 7 | Viewed by 2867
Abstract
Intratumor genetic heterogeneity (ITH) is the main obstacle to effective cancer treatment and a major mechanism of drug resistance. It results from the continuous evolution of different clones of a tumor over time. However, the molecular features underlying the emergence of genetically-distinct subclonal [...] Read more.
Intratumor genetic heterogeneity (ITH) is the main obstacle to effective cancer treatment and a major mechanism of drug resistance. It results from the continuous evolution of different clones of a tumor over time. However, the molecular features underlying the emergence of genetically-distinct subclonal cell populations remain elusive. Here, we conducted an exhaustive characterization of ITH across 2807 tumor samples from 16 cancer types. Integration of ITH scores and somatic variants detected in each tumor sample revealed that mutations in epigenetic modifier genes are associated with higher ITH levels. In particular, genes that regulate genome-wide histone and DNA methylation emerged as being determinant of high ITH. Indeed, the knockout of histone methyltransferase SETD2 or DNA methyltransferase DNMT3A using the CRISPR/Cas9 system on cancer cells led to significant expansion of genetically-distinct clones and culminated in highly heterogeneous cell populations. The ITH scores observed in knockout cells recapitulated the heterogeneity levels observed in patient tumor samples and correlated with a better mitochondrial bioenergetic performance under stress conditions. Our work provides new insights into tumor development, and discloses new drivers of ITH, which may be useful as either predictive biomarkers or therapeutic targets to improve cancer treatment. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer
Cancers 2019, 11(3), 361; https://doi.org/10.3390/cancers11030361 - 13 Mar 2019
Cited by 11 | Viewed by 1772
Abstract
Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. There are also studies exploring integrating the two types of [...] Read more.
Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. There are also studies exploring integrating the two types of measurements for prognosis modeling. However, there is a lack of study rigorously examining whether omics measurements have independent prognostic power conditional on histopathological imaging features, and vice versa. In this article, we adopt a rigorous statistical testing framework and test whether an individual gene expression measurement can improve prognosis modeling conditional on high-dimensional imaging features, and a parallel analysis is conducted reversing the roles of gene expressions and imaging features. In the analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma and liver hepatocellular carcinoma data, it is found that multiple individual genes, conditional on imaging features, can lead to significant improvement in prognosis modeling; however, individual imaging features, conditional on gene expressions, only offer limited prognostic power. Being among the first to examine the independent prognostic power, this study may assist better understanding the “connectedness” between omics profiles and histopathological imaging features and provide important insights for data integration in cancer modeling. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Observed Survival Interval: A Supplement to TCGA Pan-Cancer Clinical Data Resource
Cancers 2019, 11(3), 280; https://doi.org/10.3390/cancers11030280 - 26 Feb 2019
Cited by 15 | Viewed by 2870
Abstract
To drive high-quality omics translational research using The Cancer Genome Atlas (TCGA) data, a TCGA Pan-Cancer Clinical Data Resource was proposed. However, there is an out-of-step issue between clinical outcomes and the omics data of TCGA for skin cutaneous melanoma (SKCM), due to [...] Read more.
To drive high-quality omics translational research using The Cancer Genome Atlas (TCGA) data, a TCGA Pan-Cancer Clinical Data Resource was proposed. However, there is an out-of-step issue between clinical outcomes and the omics data of TCGA for skin cutaneous melanoma (SKCM), due to the majority of metastatic samples. In clinical cases, the survival time started from the initial SKCM diagnosis, while the omics data were characterized at TCGA sampling. This study aimed to address this issue by proposing an observed survival interval (OBS), which was defined as the time interval from TCGA sampling to patient death or last follow-up. We compared the OBS with the usual recommended overall survival (OS) by associating them with both clinical data and microRNA sequencing data of TCGA-SKCM. We found that the OS of primary SKCM was significantly shorter than that of metastatic SKCM, while the opposite happened if OBS was compared. OS was associated with the pathological stage of both primary and metastatic SKCM, while OBS was associated with the pathological stage of primary SKCM but not that of metastatic SKCM. Five previously cross-validated survival-associated microRNAs were found to be associated with the OBS rather than OS in metastatic SKCM. Thus, the OBS was more appropriate for associating microRNA-omics data of TCGA-SKCM than OS, and it is a timely supplement to TCGA Pan-Cancer Clinical Data Resource. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model
Cancers 2019, 11(2), 270; https://doi.org/10.3390/cancers11020270 - 25 Feb 2019
Cited by 10 | Viewed by 2483
Abstract
Epithelial ovarian cancer patients usually relapse after primary management. We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 [...] Read more.
Epithelial ovarian cancer patients usually relapse after primary management. We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 dataset. Finally, we evaluated the feasibility of the model using ovarian cancer patients from our institute. The 10-gene predictive model demonstrated that the high response group had a longer recurrence-free survival (RFS) (log-rank test, p = 0.015 for TCGA, p = 0.013 for GSE9891 and p = 0.039 for NTUH) and overall survival (OS) (log-rank test, p = 0.002 for TCGA and p = 0.016 for NTUH). In a multivariate Cox hazard regression model, the predictive model (HR: 0.644, 95% CI: 0.436–0.952, p = 0.027) and residual tumor size < 1 cm (HR: 0.312, 95% CI: 0.170–0.573, p < 0.001) were significant factors for recurrence. The predictive model (HR: 0.511, 95% CI: 0.334–0.783, p = 0.002) and residual tumor size < 1 cm (HR: 0.252, 95% CI: 0.128–0.496, p < 0.001) were still significant factors for death. In conclusion, the patients of high response group stratified by the model had good response and favourable prognosis, whereas for the patients of medium to low response groups, introduction of other drugs or clinical trials might be beneficial. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Reverse Engineering Cancer: Inferring Transcriptional Gene Signatures from Copy Number Aberrations with ICAro
Cancers 2019, 11(2), 256; https://doi.org/10.3390/cancers11020256 - 22 Feb 2019
Cited by 1 | Viewed by 1769
Abstract
The characterization of a gene product function is a process that involves multiple laboratory techniques in order to silence the gene itself and to understand the resulting cellular phenotype via several omics profiling. When it comes to tumor cells, usually the translation process [...] Read more.
The characterization of a gene product function is a process that involves multiple laboratory techniques in order to silence the gene itself and to understand the resulting cellular phenotype via several omics profiling. When it comes to tumor cells, usually the translation process from in vitro characterization results to human validation is a difficult journey. Here, we present a simple algorithm to extract mRNA signatures from cancer datasets, where a particular gene has been deleted at the genomic level, ICAro. The process is implemented as a two-step workflow. The first one employs several filters in order to select the two patient subsets: the inactivated one, where the target gene is deleted, and the control one, where large genomic rearrangements should be absent. The second step performs a signature extraction via a Differential Expression analysis and a complementary Random Forest approach to provide an additional gene ranking in terms of information loss. We benchmarked the system robustness on a panel of genes frequently deleted in cancers, where we validated the downregulation of target genes and found a correlation with signatures extracted with the L1000 tool, outperforming random sampling for two out of six L1000 classes. Furthermore, we present a use case correlation with a published transcriptomic experiment. In conclusion, deciphering the complex interactions of the tumor environment is a challenge that requires the integration of several experimental techniques in order to create reproducible results. We implemented a tool which could be of use when trying to find mRNA signatures related to a gene loss event to better understand its function or for a gene-loss associated biomarker research. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Nucleotide Weight Matrices Reveal Ubiquitous Mutational Footprints of AID/APOBEC Deaminases in Human Cancer Genomes
Cancers 2019, 11(2), 211; https://doi.org/10.3390/cancers11020211 - 12 Feb 2019
Cited by 11 | Viewed by 2453
Abstract
Cancer genomes accumulate nucleotide sequence variations that number in the tens of thousands per genome. A prominent fraction of these mutations is thought to arise as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases. These enzymes, collectively called activation induced [...] Read more.
Cancer genomes accumulate nucleotide sequence variations that number in the tens of thousands per genome. A prominent fraction of these mutations is thought to arise as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases. These enzymes, collectively called activation induced deaminase (AID)/APOBECs, deaminate cytosines located within defined DNA sequence contexts. The resulting changes of the original C:G pair in these contexts (mutational signatures) provide indirect evidence for the participation of specific cytosine deaminases in a given cancer type. The conventional method used for the analysis of mutable motifs is the consensus approach. Here, for the first time, we have adopted the frequently used weight matrix (sequence profile) approach for the analysis of mutagenesis and provide evidence for this method being a more precise descriptor of mutations than the sequence consensus approach. We confirm that while mutational footprints of APOBEC1, APOBEC3A, APOBEC3B, and APOBEC3G are prominent in many cancers, mutable motifs characteristic of the action of the humoral immune response somatic hypermutation enzyme, AID, are the most widespread feature of somatic mutation spectra attributable to deaminases in cancer genomes. Overall, the weight matrix approach reveals that somatic mutations are significantly associated with at least one AID/APOBEC mutable motif in all studied cancers. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Potential Applications of DNA, RNA and Protein Biomarkers in Diagnosis, Therapy and Prognosis for Colorectal Cancer: A Study from Databases to AI-Assisted Verification
Cancers 2019, 11(2), 172; https://doi.org/10.3390/cancers11020172 - 01 Feb 2019
Cited by 14 | Viewed by 2538
Abstract
In order to find out the most valuable biomarkers and pathways for diagnosis, therapy and prognosis in colorectal cancer (CRC) we have collected the published CRC biomarkers and established a CRC biomarker database (CBD: http://sysbio.suda.edu.cn/CBD/index.html). In this study, we analysed the single and [...] Read more.
In order to find out the most valuable biomarkers and pathways for diagnosis, therapy and prognosis in colorectal cancer (CRC) we have collected the published CRC biomarkers and established a CRC biomarker database (CBD: http://sysbio.suda.edu.cn/CBD/index.html). In this study, we analysed the single and multiple DNA, RNA and protein biomarkers as well as their positions in cancer related pathways and protein-protein interaction (PPI) networks to describe their potential applications in diagnosis, therapy and prognosis. CRC biomarkers were collected from the CBD. The RNA and protein biomarkers were matched to their corresponding DNAs by the miRDB database and the PubMed Gene database, respectively. The PPI networks were used to investigate the relationships between protein biomarkers and further detect the multiple biomarkers. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were used to analyse biological functions of the biomarkers. AI classification techniques were utilized to further verify the significances of the multiple biomarkers in diagnosis and prognosis for CRC. We showed that a large number of the DNA, RNA and protein biomarkers were associated with the diagnosis, therapy and prognosis in various degrees in the CRC biomarker networks. The CRC biomarkers were closely related to the CRC initiation and progression. Moreover, the biomarkers played critical roles in cellular proliferation, apoptosis and angiogenesis and they were involved in Ras, p53 and PI3K pathways. There were overlaps among the DNA, RNA and protein biomarkers. AI classification verifications showed that the combined multiple protein biomarkers played important roles to accurate early diagnosis and predict outcome for CRC. There were several single and multiple CRC protein biomarkers which were associated with diagnosis, therapy and prognosis in CRC. Further, AI-assisted analysis revealed that multiple biomarkers had potential applications for diagnosis and prognosis in CRC. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Prognostic Biomarkers in Pancreatic Cancer: Avoiding Errata When Using the TCGA Dataset
Cancers 2019, 11(1), 126; https://doi.org/10.3390/cancers11010126 - 21 Jan 2019
Cited by 19 | Viewed by 4141
Abstract
Data from the Cancer Genome Atlas (TCGA) are now easily accessible through web-based platforms with tools to assess the prognostic value of molecular alterations. Pancreatic tumors have heterogeneous biology and aggressiveness ranging from the deadly adenocarcinoma (PDAC) to the better prognosis, neuroendocrine tumors. [...] Read more.
Data from the Cancer Genome Atlas (TCGA) are now easily accessible through web-based platforms with tools to assess the prognostic value of molecular alterations. Pancreatic tumors have heterogeneous biology and aggressiveness ranging from the deadly adenocarcinoma (PDAC) to the better prognosis, neuroendocrine tumors. We assessed the availability of the pancreatic cancer TCGA data (TCGA_PAAD) from several repositories and investigated the nature of each sample and how non-PDAC samples impact prognostic biomarker studies. While the clinical and genomic data (n = 185) were fairly consistent across all repositories, RNAseq profiles varied from 176 to 185. As a result, 35 RNAseq profiles (18.9%) corresponded to a normal, inflamed pancreas or non-PDAC neoplasms. This information was difficult to obtain. By considering gene expression data as continuous values, the expression of the 5312 and 4221 genes were significantly associated with the progression-free and overall survival respectively. Considering the cohort was not curated, only 4 and 14, respectively, had prognostic value in the PDAC-only cohort. Similarly, mutations in key genes or well-described miRNA lost their prognostic significance in the PDAC-only cohort. Therefore, we propose a web-based application to assess biomarkers in the curated TCGA_PAAD dataset. In conclusion, TCGA_PAAD curation is critical to avoid important biological and clinical biases from non-PDAC samples. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
The Potential Mechanism of Bufadienolide-Like Chemicals on Breast Cancer via Bioinformatics Analysis
Cancers 2019, 11(1), 91; https://doi.org/10.3390/cancers11010091 - 14 Jan 2019
Cited by 7 | Viewed by 2328
Abstract
Bufadienolide-like chemicals are mostly composed of the active ingredient of Chansu and they have anti-inflammatory, tumor-suppressing, and anti-pain activities; however, their mechanism is unclear. This work used bioinformatics analysis to study this mechanism via gene expression profiles of bufadienolide-like chemicals: (1) Differentially expressed [...] Read more.
Bufadienolide-like chemicals are mostly composed of the active ingredient of Chansu and they have anti-inflammatory, tumor-suppressing, and anti-pain activities; however, their mechanism is unclear. This work used bioinformatics analysis to study this mechanism via gene expression profiles of bufadienolide-like chemicals: (1) Differentially expressed gene identification combined with gene set variation analysis, (2) similar small -molecule detection, (3) tissue-specific co-expression network construction, (4) differentially regulated sub-networks related to breast cancer phenome, (5) differentially regulated sub-networks with potential cardiotoxicity, and (6) hub gene selection and their relation to survival probability. The results indicated that bufadienolide-like chemicals usually had the same target as valproic acid and estradiol, etc. They could disturb the pathways in RNA splicing, the apoptotic process, cell migration, extracellular matrix organization, adherens junction organization, synaptic transmission, Wnt signaling, AK-STAT signaling, BMP signaling pathway, and protein folding. We also investigated the potential cardiotoxicity and found a dysregulated subnetwork related to membrane depolarization during action potential, retinoic acid receptor binding, GABA receptor binding, positive regulation of nuclear division, negative regulation of viral genome replication, and negative regulation of the viral life cycle. These may play important roles in the cardiotoxicity of bufadienolide-like chemicals. The results may highlight the potential anticancer mechanism and cardiotoxicity of Chansu, and could also explain the ability of bufadienolide-like chemicals to be used as hormones and anticancer and vasoprotectives agents. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach
Cancers 2019, 11(1), 45; https://doi.org/10.3390/cancers11010045 - 04 Jan 2019
Cited by 6 | Viewed by 2193
Abstract
Epidermal growth factor receptor (EGFR) inhibitors have benefitted cancer patients worldwide, but resistance inevitably develops over time, resulting in treatment failures. An accurate prediction model for acquired resistance (AR) to EGFR inhibitors is critical for early diagnosis and according intervention, but is not [...] Read more.
Epidermal growth factor receptor (EGFR) inhibitors have benefitted cancer patients worldwide, but resistance inevitably develops over time, resulting in treatment failures. An accurate prediction model for acquired resistance (AR) to EGFR inhibitors is critical for early diagnosis and according intervention, but is not yet available due to personal variations and the complex mechanisms of AR. Here, we have developed a novel pipeline to build a meta-analysis-based, multivariate model for personalized pathways in AR to EGFR inhibitors, using sophisticated machine learning algorithms. Surprisingly, the model achieved excellent predictive performance, with a cross-study validation area under curve (AUC) of over 0.9, and generalization performance on independent cohorts of samples, with a perfect AUC score of 1. Furthermore, the model showed excellent transferability across different cancer cell lines and EGFR inhibitors, including gefitinib, erlotinib, afatinib, and cetuximab. In conclusion, our model achieved high predictive accuracy through robust cross study validation, and enabled individualized prediction on newly introduced data. We also discovered common pathway alteration signatures for AR to EGFR inhibitors, which can provide directions for other follow-up studies. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Pathological and Molecular Characteristics of Colorectal Cancer with Brain Metastases
Cancers 2018, 10(12), 504; https://doi.org/10.3390/cancers10120504 - 10 Dec 2018
Cited by 10 | Viewed by 1795
Abstract
Background: Colorectal cancers (CRC) with brain metastases (BM) are scarcely described. The main objective of this study was to determine the molecular profile of CRC with BM. Methods: We included 82 CRC patients with BM. KRAS, NRAS, BRAF and mismatch repair [...] Read more.
Background: Colorectal cancers (CRC) with brain metastases (BM) are scarcely described. The main objective of this study was to determine the molecular profile of CRC with BM. Methods: We included 82 CRC patients with BM. KRAS, NRAS, BRAF and mismatch repair (MMR) status were investigated on primary tumors (n = 82) and BM (n = 38). ALK, ROS1, cMET, HER-2, PD-1, PD-L1, CD3 and CD8 status were evaluated by immunohistochemistry, and when recommended, by fluorescence in situ hybridization. Results: In primary tumors, KRAS, NRAS and BRAF mutations were observed in 56%, 6%, and 6% of cases, respectively. No ROS1, ALK and cMET rearrangement was detected. Only one tumor presented HER-2 amplification. Molecular profiles were mostly concordant between BM and paired primary tumors, except for 9% of discordances for RAS mutation. CD3, CD8, PD-1 and PD-L1 expressions presented some discordance between primary tumors and BM. In multivariate analysis, multiple BM, lung metastases and PD-L1+ tumor were predictive of poor overall survival. Conclusions: CRCs with BM are associated with high frequency of RAS mutations and significant discordance for RAS mutational status between BM and paired primary tumors. Multiple BM, lung metastases and PD-L1+ have been identified as prognostic factors and can guide therapeutic decisions for CRC patients with BM. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Network Pharmacology to Unveil the Biological Basis of Health-Strengthening Herbal Medicine in Cancer Treatment
Cancers 2018, 10(11), 461; https://doi.org/10.3390/cancers10110461 - 21 Nov 2018
Cited by 63 | Viewed by 4140
Abstract
Health-strengthening (Fu-Zheng) herbs is a representative type of traditional Chinese medicine (TCM) widely used for cancer treatment in China, which is in contrast to pathogen eliminating (Qu-Xie) herbs. However, the commonness in the biological basis of health-strengthening herbs remains [...] Read more.
Health-strengthening (Fu-Zheng) herbs is a representative type of traditional Chinese medicine (TCM) widely used for cancer treatment in China, which is in contrast to pathogen eliminating (Qu-Xie) herbs. However, the commonness in the biological basis of health-strengthening herbs remains to be holistically elucidated. In this study, an innovative high-throughput research strategy integrating computational and experimental methods of network pharmacology was proposed, and 22 health-strengthening herbs were selected for the investigation. Additionally, 25 pathogen-eliminating herbs were included for comparison. First, based on network-based, large-scale target prediction, we analyzed the target profiles of 1446 TCM compounds. Next, the actions of 166 compounds on 420 antitumor or immune-related genes were measured using a unique high-throughput screening strategy by high-throughput sequencing, referred to as HTS2. Furthermore, the structural information and the antitumor activity of the compounds in health-strengthening and pathogen-eliminating herbs were compared. Using network pharmacology analysis, we discovered that: (1) Functionally, the predicted targets of compounds from health strengthening herbs were enriched in both immune-related and antitumor pathways, similar to those of pathogen eliminating herbs. As a case study, galloylpaeoniflorin, a compound in a health strengthening herb Radix Paeoniae Alba (Bai Shao), was found to exert antitumor effects both in vivo and in vitro. Yet the inhibitory effects of the compounds from pathogen eliminating herbs on tumor cells proliferation as a whole were significantly stronger than those in health-strengthening herbs (p < 0.001). Moreover, the percentage of assay compounds in health-strengthening herbs with the predicted targets enriched in the immune-related pathways (e.g., natural killer cell mediated cytotoxicity and antigen processing and presentation) were significantly higher than that in pathogen-eliminating herbs (p < 0.05). This finding was supported by the immune-enhancing effects of a group of compounds from health-strengthening herbs indicated by differentially expressed genes in the HTS2 results. (2) Compounds in the same herb may exhibit the same or distinguished mechanisms in cancer treatment, which was demonstrated as the compounds influence pathway gene expressions in the same or opposite directions. For example, acetyl ursolic acid and specnuezhenide in a health-strengthening herb Fructus Ligustri lucidi (Nv Zhen Zi) both upregulated gene expressions in T cell receptor signaling pathway. Together, this study suggested greater potentials in tumor immune microenvironment regulation and tumor prevention than in direct killing tumor cells of health-strengthening herbs generally, and provided a systematic strategy for unveiling the commonness in the biological basis of health-strengthening herbs in cancer treatment. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Article
Predicting 90-Day Mortality in Locoregionally Advanced Head and Neck Squamous Cell Carcinoma after Curative Surgery
Cancers 2018, 10(10), 392; https://doi.org/10.3390/cancers10100392 - 22 Oct 2018
Cited by 11 | Viewed by 1883
Abstract
Purpose: To propose a risk classification scheme for locoregionally advanced (Stages III and IV) head and neck squamous cell carcinoma (LA-HNSCC) by using the Wu comorbidity score (WCS) to quantify the risk of curative surgeries, including tumor resection and radical neck dissection. Methods: [...] Read more.
Purpose: To propose a risk classification scheme for locoregionally advanced (Stages III and IV) head and neck squamous cell carcinoma (LA-HNSCC) by using the Wu comorbidity score (WCS) to quantify the risk of curative surgeries, including tumor resection and radical neck dissection. Methods: This study included 55,080 patients with LA-HNSCC receiving curative surgery between 2006 and 2015 who were identified from the Taiwan Cancer Registry database; the patients were classified into two groups, mortality (n = 1287, mortality rate = 2.34%) and survival (n = 53,793, survival rate = 97.66%), according to the event of mortality within 90 days of surgery. Significant risk factors for mortality were identified using a stepwise multivariate Cox proportional hazards model. The WCS was calculated using the relative risk of each risk factor. The accuracy of the WCS was assessed using mortality rates in different risk strata. Results: Fifteen comorbidities significantly increased mortality risk after curative surgery. The patients were divided into low-risk (WCS, 0–6; 90-day mortality rate, 0–1.57%), intermediate-risk (7–11; 2.71–9.99%), high-risk (12–16; 17.30–20.00%), and very-high-risk (17–18 and >18; 46.15–50.00%) strata. The 90-day survival rates were 98.97, 95.85, 81.20, and 53.13% in the low-, intermediate-, high-, and very-high-risk patients, respectively (log-rank p < 0.0001). The five-year overall survival rates after surgery were 70.86, 48.62, 22.99, and 18.75% in the low-, intermediate-, high-, and very-high-risk patients, respectively (log-rank p < 0.0001). Conclusion: The WCS is an accurate tool for assessing curative-surgery-related 90-day mortality risk and overall survival in patients with LA-HNSCC. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Review

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Review
Bioinformatics Analysis for Circulating Cell-Free DNA in Cancer
Cancers 2019, 11(6), 805; https://doi.org/10.3390/cancers11060805 - 11 Jun 2019
Cited by 26 | Viewed by 3868
Abstract
Molecular analysis of cell-free DNA (cfDNA) that circulates in plasma and other body fluids represents a “liquid biopsy” approach for non-invasive cancer screening or monitoring. The rapid development of sequencing technologies has made cfDNA a promising source to study cancer development and progression. [...] Read more.
Molecular analysis of cell-free DNA (cfDNA) that circulates in plasma and other body fluids represents a “liquid biopsy” approach for non-invasive cancer screening or monitoring. The rapid development of sequencing technologies has made cfDNA a promising source to study cancer development and progression. Specific genetic and epigenetic alterations have been found in plasma, serum, and urine cfDNA and could potentially be used as diagnostic or prognostic biomarkers in various cancer types. In this review, we will discuss the molecular characteristics of cancer cfDNA and major bioinformatics approaches involved in the analysis of cfDNA sequencing data for detecting genetic mutation, copy number alteration, methylation change, and nucleosome positioning variation. We highlight specific challenges in sensitivity to detect genetic aberrations and robustness of statistical analysis. Finally, we provide perspectives regarding the standard and continuing development of bioinformatics analysis to move this promising screening tool into clinical practice. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Review
Insights into Telomerase/hTERT Alternative Splicing Regulation Using Bioinformatics and Network Analysis in Cancer
Cancers 2019, 11(5), 666; https://doi.org/10.3390/cancers11050666 - 14 May 2019
Cited by 17 | Viewed by 2461
Abstract
The reactivation of telomerase in cancer cells remains incompletely understood. The catalytic component of telomerase, hTERT, is thought to be the limiting component in cancer cells for the formation of active enzymes. hTERT gene expression is regulated at several levels including chromatin, [...] Read more.
The reactivation of telomerase in cancer cells remains incompletely understood. The catalytic component of telomerase, hTERT, is thought to be the limiting component in cancer cells for the formation of active enzymes. hTERT gene expression is regulated at several levels including chromatin, DNA methylation, transcription factors, and RNA processing events. Of these regulatory events, RNA processing has received little attention until recently. RNA processing and alternative splicing regulation have been explored to understand how hTERT is regulated in cancer cells. The cis- and trans-acting factors that regulate the alternative splicing choice of hTERT in the reverse transcriptase domain have been investigated. Further, it was discovered that the splicing factors that promote the production of full-length hTERT were also involved in cancer cell growth and survival. The goals are to review telomerase regulation via alternative splicing and the function of hTERT splicing variants and to point out how bioinformatics approaches are leading the way in elucidating the networks that regulate hTERT splicing choice and ultimately cancer growth. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Review
A Review on a Deep Learning Perspective in Brain Cancer Classification
Cancers 2019, 11(1), 111; https://doi.org/10.3390/cancers11010111 - 18 Jan 2019
Cited by 110 | Viewed by 7246
Abstract
A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many [...] Read more.
A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Other

Brief Report
Breast Cancer Prognosis Using a Machine Learning Approach
Cancers 2019, 11(3), 328; https://doi.org/10.3390/cancers11030328 - 07 Mar 2019
Cited by 55 | Viewed by 4642
Abstract
Machine learning (ML) has been recently introduced to develop prognostic classification models that can be used to predict outcomes in individual cancer patients. Here, we report the significance of an ML-based decision support system (DSS), combined with random optimization (RO), to extract prognostic [...] Read more.
Machine learning (ML) has been recently introduced to develop prognostic classification models that can be used to predict outcomes in individual cancer patients. Here, we report the significance of an ML-based decision support system (DSS), combined with random optimization (RO), to extract prognostic information from routinely collected demographic, clinical and biochemical data of breast cancer (BC) patients. A DSS model was developed in a training set (n = 318), whose performance analysis in the testing set (n = 136) resulted in a C-index for progression-free survival of 0.84, with an accuracy of 86%. Furthermore, the model was capable of stratifying the testing set into two groups of patients with low- or high-risk of progression with a hazard ratio (HR) of 10.9 (p < 0.0001). Validation in multicenter prospective studies and appropriate management of privacy issues in relation to digital electronic health records (EHR) data are presently needed. Nonetheless, we may conclude that the implementation of ML algorithms and RO models into EHR data might help to achieve prognostic information, and has the potential to revolutionize the practice of personalized medicine. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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