Bioinformatic analysis of key pathways and genes shared between endometriosis and ovarian cancer.

The purpose of the study is to identify potential key genes and pathways, using bioinformatics, underlying the potentially common molecular mechanisms between endometriosis (EMS) and ovarian cancer (OC). Two datasets were collected from the Gene Expression Omnibus database, and the limma package identified common differentially expressed genes (DEGs) in the EMS and OC groups compared to controls. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene interaction network, and module analyses identified the enriched pathways associated with DEGs. A protein-protein interaction (PPI) network was then constructed, and the CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity for proteins in the PPI network. A total of 571 overlapping DEGs were identified between EMS and OC (vs. controls). Enriched DEGs were associated with 36 gene ontology terms and 7 Kyoto Encyclopedia of Genes and Genomes pathways, which were mainly associated with deactivation of the p53 signaling pathway. The Kaplan-Meier plotter platform confirmed the expression of the identified hub genes, and survival analysis suggested that CCNB1, CCNB2, BUB1B, CCNA2, KIF2C, and TOP2A are associated with decreased survival and disease-free survival rates of OC. The key pathways identified herein elucidate the possible mechanism by which EMS evolves into OC; further, the identified hub genes may serve as potential biomarkers to predict OC occurrence and prognosis.

Ni L ，Chen Y ，Yang J ，Chen C ... -  《-》

Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer.

Yang D ，He Y ，Wu B ，Deng Y ，Wang N ，Li M ，Liu Y ... -  《Journal of Ovarian Research》

Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis.

The current research was aimed to identify candidate genes associated with development and progression of epithelial ovarian carcinoma using bioinformatics analysis. We screened and validated candidate genes associated with carcinogenesis and development of epithelial ovarian carcinoma via bioinformatic analysis in three microarray datasets (GSE14407, GSE29450, and GSE54388) downloaded from the Gene Expression Omnibus (GEO) database. Our bioinformatic analysis identified 514 differentially expressed genes (DEGs) and nine candidate hub genes (CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, and TTK). Survival analysis using the Kaplan-Meier plotter showed that high expression levels of seven candidate genes (CCNB1, RRM2, BUB1, CCNA2, AURKA, CDK1, and BUB1B) were associated with poor overall survival (OS). Gene Expression Profiling Interactive Analysis (GEPIA) revealed a higher expression level of these seven candidate genes in ovarian carcinoma samples than in normal ovarian samples. Immunostaining results from the Human Protein Atlas (HPA) database suggested that the protein expression levels of CCNB1, CCNA2, AURKA, and CDK1 were increased in ovarian cancer tissues. No difference was observed in RRM2 protein expression level between normal ovarian and ovarian cancer samples. Oncomine analysis revealed an association between the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 and patient clinicopathological information. Finally, six genes, namely CCNB1, CCNA2, AURKA, BUB1, BUB1B, and CDK1, were identified as hub genes and a transcription factor (TF)-gene regulatory network was constructed to identify TFs, including POLR2A, ZBTB11, KLF9, and ELF1, that were implicated in regulating these hub genes. Six significant hub DEGs associated with a poor prognosis in epithelial ovarian cancer were identified. These could be potential biomarkers for ovarian cancer patients.

Qin W ，Yuan Q ，Liu Y ，Zeng Y ，Ke D ，Dai X ，Shuai Y ，Hu J ，Shi H ... -  《-》

Prospective pathway signaling and prognostic values of MicroRNA-9 in ovarian cancer based on gene expression omnibus (GEO): a bioinformatics analysis.

Zuo L ，Li X ，Tan Y ，Zhu H ，Xiao M ... -  《-》

Prognostic values and prospective pathway signaling of MicroRNA-182 in ovarian cancer: a study based on gene expression omnibus (GEO) and bioinformatics analysis.

Li Y ，Li L 《Journal of Ovarian Research》