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

Abstract

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 mechanisms of these signatures were explored and potential drug molecules associated with PCOS and EC were screened out.

Identification and analysis of biomarkers associated with oxidative stress and ferroptosis in recurrent miscarriage

“…We speculated that the up-regulated expression of this gene may play a different regulatory role in the development of RM, and this new specific mechanism requires further verification and attention. CPT1A was mainly enriched in the “structural constituent of ribosome” and “Cell cycle.” In order to investigate and analyze the relationship between endometrial cancer and polycystic ovary syndrome, this article uses bioinformatics to analyze the common gene features, and uses KEGG pathway to clarify the high correlation between “ribosomal subunits” and “mitochondrial translation extension.” [46]…”

“…In order to investigate and analyze the relationship between endometrial cancer and polycystic ovary syndrome, this article uses bioinformatics to analyze the common gene features, and uses KEGG pathway to clarify the high correlation between "ribosomal subunits" and "mitochondrial translation extension." [46] By using IPA pathway analysis, it can be determined that multiple pathways can be related to ferroptosis oxidation, such as the "S100 family signaling pathway," "neurovascular coupling signaling pathway" and "IL-10 signaling." For S100 protein, it can play many roles in biological organisms, not only controlling intracellular functions, but also controlling extracellular functions, such as cell migration and proliferation processes.…”

Identification of Key Genes Associated with Polycystic Ovarian Syndrome and Endometrial and Ovarian Cancer through Bioinformatics

“…Our analysis adopts a more comprehensive approach than Smith and Johnson’s, who concentrated exclusively on PCOS and OC, and Brown and White, who similarly focussed on identifying shared gene signatures between PCOS and EC. [ 11 12 ]…”

Deciphering novel common gene signatures for rheumatoid arthritis and systemic lupus erythematosus by integrative analysis of transcriptomic profiles

“…The treatments are very similar for autoimmune disorders, except for cases involving organ damage or where the features of one disease dominate over the other. Thus, elucidating these shared genetic determinants would eventually contribute to identifying biomarkers [11,12] and developing novel therapeutic strategies for combined diagnosis and prognosis of RA and SLE. The gene expression patterns analysis can provide valuable details for better understanding of molecular mechanisms in the diseases.…”