Bioinformatics and system biology approaches to identify molecular pathogenesis of polycystic ovarian syndrome, type 2 diabetes, obesity, and cardiovascular disease that are linked to the progression of female infertility

Hossain, Md. Sanower; Amin, Md. Al; Hasan, Md. Imran; Sohel, Md; Ahammed, Md Akash; Mahmud, Sakib; Rahman, Rezanur; Rahman, Habibur

doi:10.1016/j.imu.2022.100960

Cited by 7 publications

(1 citation statement)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, some researchers found key genes and pathways in T2DM by integrated bioinformatics analysis. [18][19][20][21][22][23] However, the comparative analysis of DEGs across a range of independent investigation might yield only a relatively limited amount of useful data with regard to T2DM advancement. The disadvantages of these single investigations might be overcome by NGS analysis, as this approach would make it possible to analyze the signaling pathways and interaction networks linked with the identified DEGs.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus

Alur

Raju

Vastrad³

et al. 2023

Clin Med Insights Endocrinol Diabetes

View full text Add to dashboard Cite

Background: Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder. The aim of the present investigation was to identify gene signature specific to T2DM. Methods: The next generation sequencing (NGS) dataset GSE81608 was retrieved from the gene expression omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs) between T2DM and normal controls. Then, Gene Ontology (GO) and pathway enrichment analysis, protein-protein interaction (PPI) network, modules, miRNA (micro RNA)-hub gene regulatory network construction and TF (transcription factor)-hub gene regulatory network construction, and topological analysis were performed. Receiver operating characteristic curve (ROC) analysis was also performed to verify the prognostic value of hub genes. Results: A total of 927 DEGs (461 were up regulated and 466 down regulated genes) were identified in T2DM. GO and REACTOME results showed that DEGs mainly enriched in protein metabolic process, establishment of localization, metabolism of proteins, and metabolism. The top centrality hub genes APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1 were screened out as the critical genes. ROC analysis provides prognostic value of hub genes. Conclusion: The potential crucial genes, especially APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1, might be linked with risk of T2DM. Our study provided novel insights of T2DM into genetics, molecular pathogenesis, and novel therapeutic targets.

show abstract