Gene co-expression analysis identifies common modules related to prognosis and drug resistance in cancer cell lines

Identification of Hub Genes Associated with Resistance to Prednisolone in Acute Lymphoblastic Leukemia Based on Weighted Gene Co-expression Network Analysis

Resistance against glucocorticoids which are used to reduce inflammation and treatment of a number of diseases, including leukemia, is known as the first stage of treatment failure in acute lymphoblastic leukemia. Since these drugs are the essential components of chemotherapy regimens for ALL and play an important role in stop of cell growth and induction of apoptosis, it is important to identify genes and the molecular mechanism that may affect glucocorticoid resistance. In this study, we used the GSE66705 dataset and weighted gene co-expression network analysis (WGCNA) to identify modules that correlated more strongly with prednisolone resistance in type B lymphoblastic leukemia patients. The PPI network was built using the DEGs key modules and the STRING database. Finally, we used the overlapping data to identify hub genes. out of a total of 12 identified modules by WGCNA, the blue module was find to have the most statistically significant correlation with prednisolone resistance and Nine genes including SOD1, CD82, FLT3, GART, HPRT1, ITSN1, TIAM1, MRPS6, MYC were recognized as hub genes Whose expression changes can be associated with prednisolone resistance. Enrichment analysis based on the MsigDB repository showed that the altered expressed genes of the blue module were mainly enriched in IL2_STAT5, KRAS, MTORC1, and IL6-JAK-STAT3 pathways, and their expression changes can be related to cell proliferation and survival. The analysis performed by the WGCNA method introduced new genes. The role of some of these genes was previously reported in the resistance to chemotherapy in other diseases. This can be used as clues to detect treatment-resistant (drug-resistant) cases in the early stages of diseases.

Molecular Characteristics and Prognostic Role of MFAP2 in Stomach Adenocarcinoma

Molecular characteristics and prognostic role of MFAP2 were by no means stated. The MFAP2 expression and prognostic prices in this study, with Cox analysis, was employed to develop a predictive fee for MFAP2. To know about coexpression and practical networks associated with MFAP2, LinkedOmics and GEPIA2 have been used. MFAP2 expression has been increased and verified in many unbiased coalitions in TCGA-STAD tumor tissues. In addition, in each TCGA and various cohorts, increased MFAP2 was linked with lower survival. Evaluation by Cox revealed the unbiased danger to average survival, disease-specific survival, and progression-free survival of STAD used to be due to the elevated expression of MFAP2. Active community assessed the MFAP2, through which more than a few cancer-associated kinases and E2F household pathways are regulated, which shows that MFAP2 affects RNA transportation, oocyte meiosis, spliceosome, and ribosome biogenesis. MFAP2 can predict and is linked to the prediction of STAD independently. The closure of the MFAP2 link to the macrophage marker genes is, in particular, the achievable core of immune response.

Gene Network Analysis of Hepatocellular Carcinoma Identifies Modules Associated with Disease Progression, Survival, and Chemo Drug Resistance

Background

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. HCC transcriptome has been extensively studied; however, the progress in disease mechanisms, prognosis, and treatment is still slow.

Methods

A rank-based module-centric workflow was introduced to analyze important modules associated with HCC development, prognosis, and drug resistance. The currently largest HCC cell line RNA-Seq dataset from the LIMORE database was used to construct the reference modules by weighted gene co-expression network analysis.

Results

Thirteen reference modules were identified with validated reproducibility. These modules were all associated with specific biological functions. Differentially expressed module analysis revealed the crucial modules during HCC development. Modules and hub genes are indicative of patient survival. Modules can differentiate patients in different HCC stages. Furthermore, drug resistance was revealed by drug-module association analysis. Based on differentially expressed modules and hub genes, six candidate drugs were screened. The hub genes of those modules merit further investigation.

Conclusion

We proposed a reference module-based analysis of the HCC transcriptome. The identified modules are associated with HCC development, survival, and drug resistance. M3 and M6 may play important roles during HCV to HCC development. M1, M3, M5, and M7 are associated with HCC survival. High M4, high M9, low M1, and low M3 may be associated with dasatinib, doxorubicin, CD532, and simvastatin resistance. Our analysis provides useful information for HCC diagnosis and treatment.

Survival-Based Biomarker Module Identification Associated with Oral Squamous Cell Carcinoma (OSCC)

Simple Summary

In this study, four OSCC-specific hub genes were identified using high-throughput RNA-Seq data from TCGA cohort. The significant genes within tumor and normal samples were used for weighted PPI network construction based on survival of patients along with their expression profiles. The analysis revealed the most significant module in the training and test datasets. The genes from this module were used for pathway enrichment analysis followed by hub gene selection. These novel biomarkers might have clinical utility for diagnosis and prognosis prediction in OSCC, providing diagnosis at a very early stage. Moreover, a combination of all these biomarkers might distinguish the OSCC patients with low risk and high risk for cancer progression and recurrence, which will provide useful guidance for personalized and precision therapy. However, the results in the present study were obtained by integrative theoretical analysis, and the findings remain to be confirmed by further experimental validations.

Abstract

Head and neck squamous cell carcinoma (HNSC) is one of the most common malignant tumors worldwide with a high rate of morbidity and mortality, with 90% of predilections occurring for oral squamous cell carcinoma (OSCC). Cancers of the mouth account for 40% of head and neck cancers, including squamous cell carcinomas of the tongue, floor of the mouth, buccal mucosa, lips, hard and soft palate, and gingival. OSCC is the most devastating and commonly occurring oral malignancy, with a mortality rate of 500,000 deaths per year. This has imposed a strong necessity to discover driver genes responsible for its progression and malignancy. In the present study we filtered oral squamous cell carcinoma tissue samples from TCGA-HNSC cohort, which we followed by constructing a weighted PPI network based on the survival of patients and the expression profiles of samples collected from them. We found a total of 46 modules, with 18 modules having more than five edges. The KM and ME analyses revealed a single module (with 12 genes) as significant in the training and test datasets. The genes from this significant module were subjected to pathway enrichment analysis for identification of significant pathways and involved genes. Finally, the overlapping genes between gene sets ranked on the basis of weighted PPI module centralities (i.e., degree and eigenvector), significant pathway genes, and DEGs from a microarray OSCC dataset were considered as OSCC-specific hub genes. These hub genes were clinically validated using the IHC images available from the Human Protein Atlas (HPA) database.

Reference Module-Based Analysis of Ovarian Cancer Transcriptome Identifies Important Modules and Potential Drugs

Ovarian cancer (OVC) is often diagnosed at the advanced stage resulting in a poor overall outcome for the patient. The disease mechanisms, prognosis, and treatment require imperative elucidation. A rank-based module-centric framework was proposed to analyze the key modules related to the development, prognosis, and treatment of OVC. The ovarian cancer cell line microarray dataset GSE43765 from the Gene Expression Omnibus database was used to construct the reference modules by weighted gene correlation network analysis. Twenty-three reference modules were tested for stability and functionally annotated. Furthermore, to demonstrate the utility of reference modules, two more OVC datasets were collected, and their gene expression profiles were projected to the reference modules to generate a module-level expression. An epithelial-mesenchymal transition module was activated in OVC compared to the normal epithelium, and a pluripotency module was activated in ovarian cancer stroma compared to ovarian cancer epithelium. Seven differentially expressed modules were identified in OVC compared to the normal ovarian epithelium, with five up-regulated, and two down-regulated. One module was identified to be predictive of patient overall survival. Four modules were enriched with SNP signals. Based on differentially expressed modules and hub genes, five candidate drugs were screened. The hub genes of those modules merit further investigation. We firstly propose the reference module-based analysis of OVC. The utility of the analysis framework can be extended to transcriptome data of other kinds of diseases.

A network-based algorithm for the identification of moonlighting noncoding RNAs and its application in sepsis

Moonlighting proteins provide more options for cells to execute multiple functions without increasing the genome and transcriptome complexity. Although there have long been calls for computational methods for the prediction of moonlighting proteins, no method has been designed for determining moonlighting long noncoding ribonucleicacidz (RNAs) (mlncRNAs). Previously, we developed an algorithm MoonFinder for the identification of mlncRNAs at the genome level based on the functional annotation and interactome data of lncRNAs and proteins. Here, we update MoonFinder to MoonFinder v2.0 by providing an extensive framework for the detection of protein modules and the establishment of RNA-module associations in human. A novel measure, moonlighting coefficient, was also proposed to assess the confidence of an ncRNA acting in a moonlighting manner. Moreover, we explored the expression characteristics of mlncRNAs in sepsis, in which we found that mlncRNAs tend to be upregulated and differentially expressed. Interestingly, the mlncRNAs are mutually exclusive in terms of coexpression when compared to the other lncRNAs. Overall, MoonFinder v2.0 is dedicated to the prediction of human mlncRNAs and thus bears great promise to serve as a valuable R package for worldwide research communities (https://cran.r-project.org/web/packages/MoonFinder/index.html). Also, our analyses provide the first attempt to characterize mlncRNA expression and coexpression properties in adult sepsis patients, which will facilitate the understanding of the interaction and expression patterns of mlncRNAs.

Integrated profiling identifies SLC5A6 and MFAP2 as novel diagnostic and prognostic biomarkers in gastric cancer patients

Gastric cancer (GC) is one of the leading causes of malignancy‑associated mortality worldwide. However, the underlying molecular mechanisms of GC are unclear and the prognosis of GC is poor. Therefore, it is important and urgent to explore the underlying mechanisms and screen for novel diagnostic and prognostic biomarkers, as well as therapeutic targets. In the current study, scale‑free gene co‑expression networks were constructed using weighted gene co‑expression network analysis, the potential associations between gene sets and clinical features were investigated, and the hub genes were identified. The gene expression profiles of GSE38749 were downloaded from the Gene Expression Omnibus database. RNA‑seq and clinical data for GC from The Cancer Genome Atlas were utilized for verification. Furthermore, the expression of candidate biomarkers in gastric tissues was investigated. Survival analysis was performed using Kaplan‑Meier and log‑rank test. The predictive role of candidate biomarkers in GC was evaluated using a receiver operator characteristic (ROC) curve. Gene Ontology, gene set enrichment analysis and gene set variation analysis methods were used to interpret the function of candidate biomarkers in GC. A total of 29 modules were identified via the average linkage hierarchical clustering. A significant module consisting of 48 genes associated with clinical traits was found; three genes with high connectivity in the clinical significant module were identified as hub genes. Among them, SLC5A6 and microfibril‑associated protein 2 (MFAP2) were negatively associated with the overall survival, and their expression was elevated in GC compared with non‑tumor tissues. Additionally, ROC curves indicated that SLC5A6 and MFAP2 showed a good diagnostic power in discriminating cancerous from normal tissues. SLC5A6 and MFAP2 were identified as novel diagnostic and prognostic biomarkers in GC patients; both of these genes were first reported here in connection with GC and deserved further research.

Long noncoding RNA and mRNA profiling in cetuximab-resistant colorectal cancer cells by RNA sequencing analysis

To gain an insight into the molecular mechanisms of cetuximab resistance in colorectal cancer, we generated a cetuximab-resistant cell line (H508/CR) and performed RNA sequencing to identify the differential expression patterns of noncoding RNAs (ncRNAs) and mRNAs between cetuximab-sensitive and resistant cells. A total of 278 ncRNA transcripts and 1,059 mRNA transcripts were dysregulated in the cetuximab-resistant cells. The expression levels of nine selected long noncoding RNAs (lncRNAs) were validated using quantitative real-time PCR. Functional analysis revealed that several groups of lncRNAs might be involved in pathways associated with cetuximab resistance. Increased glucose consumption and lactate secretion in cetuximab-resistant cells suggested that glucose metabolism might be involved in cetuximab resistance. In addition, lncRNA LINC00973 was upregulated in the H508/CR cell line and cells transfected with a LINC00973 short interfering RNA exhibited reduced cell viability, increased apoptosis, and decreased glucose consumption and lactate secretion. Our results provide essential data regarding differentially expressed lncRNAs and mRNAs in cetuximab-resistant cells, which may provide new potential candidates for cetuximab therapy.

Revisiting Connectivity Map from a gene co-expression network analysis

The Connectivity Map (CMap) is a tool that has been extensively utilized to study drug repositioning and side-effect prediction. However, most of these analyses rely on signature genes, ignoring the pathways by which those genes are regulated, as well as the functional overlap of redundant genes. The present study utilized a systems biology approach referred to as Weighted Gene Co-expression Network Analysis (WGCNA) to dissect the transcriptional profiles of CMap and reveal these hidden factors. Seven common modules associated with protein binding, extracellular matrix organization and translation were identified. Furthermore, drugs were clustered based on module expression to infer their mechanism of action (MoA) based on common activity profiles. As an extension of this, an example of disease-based module projection to identify novel drugs was provided. The analysis developed in the present study may provide a novel framework for drug repositioning or discovering MoAs.

Role of COL6A3 in colorectal cancer

Public transcriptome databases provide a valuable resource for genome-wide co-expression network analysis and investigation of the molecular mechanisms that underlie pathogenesis. To discover genes that may affect patient survival, a large-scale analysis of human colorectal cancer (CRC) datasets that were retrieved from the NCBI Gene Expression Omnibus was performed. A gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA). A total of 18 co-expressed gene modules were identified, of which two genes corresponded to cell migration and the cell cycle, two genes were involved in immune responses, two genes corresponded to mitochondrial function, and one gene corresponded to RNA splicing. A total of eight hub genes in the cell migration/extracellular matrix module were associated with poor prognosis in CRC, and the P-value for collagen type VI α3 chain (COL6A3) was the lowest. In silico analysis of cell type-specific gene expression and COL6A3 knockout experiments indicated the clinical relevance of COL6A3 in the development of CRC. In summary, the present analysis provides a basis for understanding the molecular characterization of CRC at the transcription level. COL6A3 may be a promising biomarker or target for the prognosis and treatment of CRC.

Oxidative phosphorylation activation is an important characteristic of DOX resistance in hepatocellular carcinoma cells

Background

Despite the implications for tumor growth and cancer drug resistance, the mechanisms underlying differences in energy metabolism among cells remain unclear.

Methods

To analyze differences between cell types, cell viability, ATP and α-ketoglutaric acid levels, the oxygen consumption rate and extracellular acidification rate, and the expression of key enzymes involved in α-KG metabolism and transfer were examined. Additionally, UPLC-MS/MS was used to determine the doxorubicin (DOX) content in SMMC-7721 and SMMC-7721/DOX cells.

Results

We found that energy metabolism in SMMC-7721 cells is mainly dependent on the glycolysis pathway, whereas SMMC-7721/DOX cells depend more heavily on the oxidative phosphorylation pathway. Cell viability and intracellular ATP levels in SMMC-7721/DOX cells were significantly reduced by rotenone and oligomycin, inhibitors of oxidative phosphorylation. However, SMMC-7721 cell properties were more strongly influenced by an inhibitor of glycolysis, 2-deoxy-d-glucose. Furthermore, the suppressive effect of α-KG on ATP synthase plays an important role in the low levels of oxidative phosphorylation in SMMC-7721 cells; this effect could be strengthened by the metabolic poison methotrexate and reversed by l-(−)-malic acid, an accelerator of the malate-aspartate cycle.

Conclusions

The inhibitory effect of α-KG on ATP synthase was uncoupled with the tricarboxylic acid cycle and oxidative phosphorylation in SMMC-7721 cells; accordingly, energy metabolism was mainly determined by glycolysis. In drug-resistant cells, a remarkable reduction in the inhibitory effects of α-KG on ATP synthase resulted in better coordination among the TCA cycle, oxidative phosphorylation, and glycolysis, providing novel potential strategies for clinical treatment of liver cancer resistance.

Transcriptome-derived stromal and immune scores infer clinical outcomes of patients with cancer

The stromal and immune cells that form the tumor microenvironment serve a key role in the aggressiveness of tumors. Current tumor-centric interpretations of cancer transcriptome data ignore the roles of stromal and immune cells. The aim of the present study was to investigate the clinical utility of stromal and immune cells in tissue-based transcriptome data. The 'Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data' (ESTIMATE) algorithm was used to probe diverse cancer datasets and the fraction of stromal and immune cells in tumor tissues was scored. The association between the ESTIMATE scores and patient survival data was asessed; it was indicated that the two scores have implications for patient survival, metastasis and recurrence. Analysis of a colorectal cancer progression dataset revealed that decreased levels immune cells could serve an important role in cancer progression. The results of the present study indicated that trasncriptome-derived stromal and immune scores may be a useful indicator of cancer prognosis.

Optimizing prognosis-related key miRNA-target interactions responsible for cancer metastasis

Increasing evidence suggests that the abnormality of microRNAs (miRNAs) and their downstream targets is frequently implicated in the pathogenesis of human cancers, however, the clinical benefit of causal miRNA-target interactions has been seldom studied. Here, we proposed a computational method to optimize prognosis-related key miRNA-target interactions by combining transcriptome and clinical data from thousands of TCGA tumors across 16 cancer types. We obtained a total of 1,956 prognosis-related key miRNA-target interactions between 112 miRNAs and 1,443 their targets. Interestingly, these key target genes are specifically involved in tumor progression-related functions, such as ‘cell adhesion’ and ‘cell migration’. Furthermore, they are most significantly correlated with ‘tissue invasion and metastasis’, a hallmark of metastasis, in ten distinct types of cancer through the hallmark analysis. These results implicated that the prognosis-related key miRNA-target interactions were highly associated with cancer metastasis. Finally, we observed that the combination of these key miRNA-target interactions allowed to distinguish patients with good prognosis from those with poor prognosis both in most TCGA cancer types and independent validation sets, highlighting their roles in cancer metastasis. We provided a user-friendly database named miRNATarget (freely available at http://biocc.hrbmu.edu.cn/miRNATar/), which provides an overview of the prognosis-related key miRNA-target interactions across 16 cancer types.

Coexpression analysis identifies nuclear reprogramming barriers of somatic cell nuclear transfer embryos

The success of cloned animal "Dolly Sheep" demonstrated the somatic cell nuclear transfer (SCNT) technique holds huge potentials for mammalian asexual reproduction. However, the extremely poor development of SCNT embryos indicates their molecular mechanism remain largely unexplored. Deciphering the spatiotemporal patterns of gene expression in SCNT embryos is a crucial step toward understanding the mechanisms associated with nuclear reprogramming. In this study, a valuable transcriptome recourse of SCNT embryos was firstly established, which derived from different inter-/intra donor cells. The gene co-expression analysis identified 26 cell-specific modules, and a series of regulatory pathways related to reprogramming barriers were further enriched. Compared to the intra-SCNT embryos, the inter-SCNT embryos underwent only complete partially reprogramming. As master genome trigger genes, the transcripts related to TFIID subunit, RNA polymerase and mediators were incomplete activated in inter-SCNT embryos. The inter-SCNT embryos only wasted the stored maternal mRNA of master regulators, but failed to activate their self-sustained pathway of RNA polymerases. The KDM family of epigenetic regulator also seriously delayed in inter-SCNT embryo reprogramming process. Our study provided new insight into understanding of the mechanisms of nuclear reprogramming.

From Saccharomyces cerevisiae to human: The important gene co-expression modules

Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Yeast has long been a popular model organism for biomedical research. In the current study, a weighted gene co-expression network analysis algorithm was applied to construct a gene co-expression network in Saccharomyces cerevisiae. Seventeen stable gene co-expression modules were detected from 2,814 S. cerevisiae microarray data. Further characterization of these modules with the Database for Annotation, Visualization and Integrated Discovery tool indicated that these modules were associated with certain biological processes, such as heat response, cell cycle, translational regulation, mitochondrion oxidative phosphorylation, amino acid metabolism and autophagy. Hub genes were also screened by intra-modular connectivity. Finally, the module conservation was evaluated in a human disease microarray dataset. Functional modules were identified in budding yeast, some of which are associated with patient survival. The current study provided a paradigm for single cell microorganisms and potentially other organisms.

Network-based expression analysis reveals key genes related to glucocorticoid resistance in infant acute lymphoblastic leukemia

PURPOSE

Despite vast improvements that have been made in the treatment of children with acute lymphoblastic leukemia (ALL), the majority of infant ALL patients (~80 %, < 1 year of age) that carry a chromosomal translocation involving the mixed lineage leukemia (MLL) gene shows a poor response to chemotherapeutic drugs, especially glucocorticoids (GCs), which are essential components of all current treatment regimens. Although addressed in several studies, the mechanism(s) underlying this phenomenon have remained largely unknown. A major drawback of most previous studies is their primary focus on individual genes, thereby neglecting the putative significance of inter-gene correlations. Here, we aimed at studying GC resistance in MLL-rearranged infant ALL patients by inferring an associated module of genes using co-expression network analysis. The implications of newly identified candidate genes with associations to other well-known relevant genes from the same module, or with associations to known transcription factor or microRNA interactions, were substantiated using literature data.

METHODS

A weighted gene co-expression network was constructed to identify gene modules associated with GC resistance in MLL-rearranged infant ALL patients. Significant gene ontology (GO) terms and signaling pathways enriched in relevant modules were used to provide guidance towards which module(s) consisted of promising candidates suitable for further analysis.

RESULTS

Through gene co-expression network analysis a novel set of genes (module) related to GC-resistance was identified. The presence in this module of the S100 and ANXA genes, both well-known biomarkers for GC resistance in MLL-rearranged infant ALL, supports its validity. Subsequent gene set net correlation analyses of the novel module provided further support for its validity by showing that the S100 and ANXA genes act as 'hub' genes with potentially major regulatory roles in GC sensitivity, but having lost this role in the GC resistant phenotype. The detected module implicates new genes as being candidates for further analysis through associations with known GC resistance-related genes.

CONCLUSIONS

From our data we conclude that available systems biology approaches can be employed to detect new candidate genes that may provide further insights into drug resistance of MLL-rearranged infant ALL cases. Such approaches complement conventional gene-wise approaches by taking putative functional interactions between genes into account.

Quantitative Identification of Compound-Dependent On-Modules and Differential Allosteric Modules From Homologous Ischemic Networks

Module‐based methods have made much progress in deconstructing biological networks. However, it is a great challenge to quantitatively compare the topological structural variations of modules (allosteric modules [AMs]) under different situations. A total of 23, 42, and 15 coexpression modules were identified in baicalin (BA), jasminoidin (JA), and ursodeoxycholic acid (UA) in a global anti‐ischemic mice network, respectively. Then, we integrated the methods of module‐based consensus ratio (MCR) and modified Z_summary module statistic to validate 12 BA, 22 JA, and 8 UA on‐modules based on comparing with vehicle. The MCRs for pairwise comparisons were 1.55% (BA vs. JA), 1.45% (BA vs. UA), and 1.27% (JA vs. UA), respectively. Five conserved allosteric modules (CAMs) and 17 unique allosteric modules (UAMs) were identified among these groups. In conclusion, module‐centric analysis may provide us a unique approach to understand multiple pharmacological mechanisms associated with differential phenotypes in the era of modular pharmacology.

An integrative and comparative study of pan-cancer transcriptomes reveals distinct cancer common and specific signatures

To investigate the commonalities and specificities across tumor lineages, we perform a systematic pan-cancer transcriptomic study across 6744 specimens. We find six pan-cancer subnetwork signatures which relate to cell cycle, immune response, Sp1 regulation, collagen, muscle system and angiogenesis. Moreover, four pan-cancer subnetwork signatures demonstrate strong prognostic potential. We also characterize 16 cancer type-specific subnetwork signatures which show diverse implications to somatic mutations, somatic copy number aberrations, DNA methylation alterations and clinical outcomes. Furthermore, some of them are strongly correlated with histological or molecular subtypes, indicating their implications with tumor heterogeneity. In summary, we systematically explore the pan-cancer common and cancer type-specific gene subnetwork signatures across multiple cancers, and reveal distinct commonalities and specificities among cancers at transcriptomic level.

Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer

Ovarian cancer remains a dismal disease with diagnosing in the late, metastatic stages, therefore, there is a growing realization of the critical need to develop effective biomarkers for understanding underlying mechanisms. Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored. Moreover, ovarian cancer diagnosis and treatment still exist a large gap that need to be bridged. In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma. Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes. More importantly, these overlapping genes tightly clustered together pointing to the module, deciphering the crosstalk between network-based survival-associated module and cell death in ovarian cancer.