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Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer.

ABSTRACT: The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two carefully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10(-11), the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient's response to treatment or be used as a novel target for therapy.

SUBMITTER: Coveney C

PROVIDER: S-EPMC4996375 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer.

Coveney Clare C Boocock David J DJ Rees Robert C RC Deen Suha S Ball Graham R GR

Microarrays (Basel, Switzerland) 20150717 3

The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was appl ...[more]

PMID: 27600227

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Project description:: Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors ) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C) , VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.

Project description:PurposeThe dysregulation of arrestin domain containing 3 (ARRDC3) has an important effect on oncogenesis and tumor progression in many cancers, including renal cell carcinoma and breast cancer. However, the role of ARRDC3 in ovarian cancer (OC) has not been reported.MethodsThe present study explored the diagnostic and prognostic roles of ARRDC3 in ovarian cancer using GEPIA, ONCOMINE, GEO, and Kaplan-Meier Plotter databases for training and validation. Then, we conducted a stratified analysis for clinicopathological factors using Kaplan-Meier Plotter and GEPIA databases. To further explore the mechanisms, we also used the MIST database to visualize the protein-protein interaction network of ARRDC3 associated with OC. The gene-gene interaction network was visualized by GeneMANIA plugin in Cytoscape 3.8.0 software, and the associated co-expression genes of ARRDC3 were analyzed by the cBioPortal database. The 100 top co-expression genes chosen for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used by the DAVID website.ResultsA significant difference in ARRDC3 mRNA expression was found between OC and normal ovary tissues. ARRDC3 could potentially be implicated in the diagnosis of OC. A high ARRDC3 mRNA expression level was associated with poor overall survival and progression-free survival. However, no significance was reported in respect to post progression survival. Except for histology, which had no prognostic value for PPS in stratified analysis, stratified analysis of other factors had prognostic value for OS, PFS, and PPS. Interestingly, we found a positive correlation between ARRDC3 expression and CD8+ T cells, macrophages, neutrophils, and dendritic cells, indicating that ARRDC3 might be associated with immune infiltration of these immune cells. Co-expression genes enrichment analysis found that they were involved in the Renin-angiotensin system pathway.ConclusionDifferentially expressed ARRDC3 might be a potential prognostic and diagnostic marker in Ovarian Cancer.

Dataset Information

Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer.

Publications

Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer.

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