Identification of the shared gene signatures and pathways between polycystic ovary syndrome and endometrial cancer: An omics data based combined approach

作者：Chenyun Miao, Yun Chen, Xiaojie Fang, Ying Zhao, Ruye Wang, Qin Zhang · 发表于：PLoS ONE · 年份：2022 · DOI：10.1371/journal.pone.0271380 · 被引用次数：26 · 研究领域：Bioinformatics and Genomic Networks、Ferroptosis and cancer prognosis、Ovarian function and disorders

OBJECTIVE: Polycystic ovary syndrome (PCOS) is a common endocrine disorder with high incidence. Recently it has been implicated as a significant risk factor for endometrial cancer (EC). Our study aims to detect shared gene signatures and biological mechanism between PCOS and EC by bioinformatics analysis. METHODS: Bioinformatics analysis based on GEO database consisted of data integration, network construction and functional enrichment analysis was applied. In addition, the pharmacological methodology and molecular docking was also performed. RESULTS: Totally 10 hub common genes, MRPL16, MRPL22, MRPS11, RPL26L1, ESR1, JUN, UBE2I, MRPL17, RPL37A, GTF2H3, were considered as shared gene signatures for EC and PCOS. The GO and KEGG pathway analysis of these hub genes showed that "mitochondrial translational elongation", "ribosomal subunit", "structural constituent of ribosome" and "ribosome" were highly correlated. Besides, associated transcription factors (TFs) and miRNAs network were constructed. We identified candidate drug molecules including fenofibrate, cinnarizine, propanil, fenthion, clindamycin, chloramphenicol, demeclocycline, hydrochloride, azacitidine, chrysene and artenimol according to these hub genes. Molecular docking analysis verified a good binding interaction of fenofibrate against available targets (JUN, ESR1, UBE2I). CONCLUSION: Gene signatures and regulatory biological pathways were identified through bioinformatics analysis. Moreover, the molecular mechanism...