Identification of key genes and molecular pathways in type 2 diabetes mellitus and polycystic ovary syndrome via bioinformatics analyses

OBJECTIVE

Type 2 diabetes mellitus (T2DM) and polycystic ovary syndrome (PCOS) are highly prevalent endocrine system diseases. However, studies on the molecular mechanisms of T2DM and PCOS at the transcriptomic level are still few. Thus, we aimed to reveal the potential common genetic and molecular pathways between T2DM and PCOS via bioinformatics analyses.

MATERIALS AND METHODS

We downloaded the GSE10946 and GSE18732 datasets for T2DM and PCOS, respectively, from the National Center for Biotechnology Information's Gene Expression Omnibus (GEO) database. These datasets were subjected to integrated differential and weighted gene co-expression network analyses (WGCNA) to screen common genes. Thereafter, functional enrichment and disease gene association analyses were performed, transcription factor (TF)-gene and TF-miRNA-gene regulatory networks were constructed, and finally, the relevant target drugs were identified.

RESULTS

We identified common genes (BIRC3, DEPTOR, TNNL3, ADRA2A) in T2DM and PCOS. Pathway enrichment analysis depicted that the common genes were enriched in smooth muscle contraction, channel inhibitor activity, apoptosis, and tumor necrosis factor (TNF) signaling pathways. TFs such as SP7, KLF8, HCFC1, IRF1, and MLLT1 played key roles in TF regulatory networks. Orlistat was indicated to be an important gene-targeting drug.

CONCLUSIONS

This study is the first study to explore four diagnostic biomarkers and gene regulatory networks for T2DM and PCOS. The findings of our study provide novel insights into the diagnosis and treatment of T2DM and PCOS.

An approach combining bioinformatics and machine learning to identify eight autophagy-related biomarkers and construct molecular mechanisms underlying COVID-19 and major depressive disorders

OBJECTIVE

A lack of objective biomarkers is preventing the screening and diagnosis of COVID-19 combined with major depression disorder (COVID-19-MDD). The purpose of this study was to identify diagnostic biomarkers and gene regulatory mechanisms associated with autophagy; a crucial process significantly involved in the pathogenesis of COVID-19-MDD.

MATERIALS AND METHODS

In this study, differentially expressed genes (DEGs) were screened using GSE98793 from the GEO2R analysis (GEO) database, and intersected with the COVID-19-related gene (CRGs) and autophagy-related genes (ARGs) to obtain common genes involved in. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these common genes were performed. Subsequently, the transcription factor (TF)-gene regulatory network and comorbidity network were constructed. In addition, 10 drug candidates were screened using the DSigDB database. To identify diagnostic markers, we used LASSO regression.

RESULTS

In total, 13 common genes were screened, which were primarily enriched in lysosomes, endoplasmic reticulum membranes, and other endomembrane systems also associated with autophagy. Additionally, these genes were involved in neurological cell signaling and have a functional role in pathways related to vascular endothelial growth factor, tyrosine kinase, autophagy, inflammation, immunity, and carcinogenesis. Tumors and psychiatric disorders were the most highly linked diseases to COVID-19. Finally, ten drug candidates and eight diagnostic markers (STX17, NRG1, RRAGD, XPO1, HERC1, HSP90AB1, EPHB2, and S1PR3) were screened.

CONCLUSIONS

This is the first study to screen eight diagnostic markers and construct a gene regulatory network for COVID-19-MDD from the perspective of autophagy. The findings of our study provide novel insights into the diagnosis and treatment of COVID-19-MDD.