Key genes and co‐expression network analysis in the livers of type 2 diabetes patients

Abstract

Aims/Introduction The incidence of type 2 diabetes is increasing worldwide. Hepatic insulin resistance and liver lipid accumulation contributes to type 2 diabetes development. The aim of the present study was to investigate the key gene pathways and co‐expression networks in the livers of type 2 diabetes patients. Materials and Methods Dataset GSE 15653 containing nine healthy individuals and nine type 2 diabetes patients was downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database. Differentially expressed genes were obtained from the livers of type 2 diabetes patients, annotated pathway enrichment and protein–protein interaction network analysis. Next, functional modules and transcription factor networks were constructed. Gene co‐expression networks were analyzed by weighted correlation network analysis to identify key modules related to clinical traits, and the candidate key genes were validated in hepatic insulin resistance models in vitro . Results A total of 778 differentially expressed genes were filtered in the livers of type 2 diabetes patients, pathway enrichment analysis identified ke y pathways, such as the mitogen‐activated protein kinase signaling pathway, Hippo signaling pathway and hypoxia‐inducible factor ‐1 signaling pathway, that were associated with type 2 diabetes. Several transcription factors of three functional modules identified from protein–protein interaction networks are likely to be implicated in type 2 diabetes. Furthermore, weighted correlation network analysis identified five modules that were shown to be highly correlated with type 2 diabetes and other clinical traits. Functional annotation showed that these modules were mainly enriched in pathways such as metabolic pathways, phosphoinositide 3‐kinase‐protein kinase B signaling pathway and natural killer cell‐mediated cytotoxicity. UBE 2M and GPER were upregulated in L02 and HepG2 models, whereas P2 RY 11 only upregulated in L02 model, and UBE 2N only downregulated in HepG2 model at a significant level. Conclusions These results would offer new insights into hepatic insulin resistance, type 2 diabetes pathogenesis, development and drug discovery.

Novel susceptibility loci identified in a genome-wide association study of type 2 diabetes complications in population of Latvia

“…Although the rs2477088 variant has not been previously linked to any T2DM manifestations, PDE4DIP , also known as myomegalin or cardiomyopathy-associated protein 2, is a well-known contributor of the microtubule control process [ 36 ] and some previous evidence exist indicating on the potential role of the gene in macrovascular diseases and T2DM. The exome sequencing has revealed a rare variant of PDE4DIP , which significantly increases the risk of ischemic stroke [ 37 ], moreover, CpG island methylation in leukocytes annotated to PDE4DIP contributes to the epigenetic fingerprint of myocardial infarction [ 38 ], and finally, the gene is also significantly downregulated in liver of T2DM patients [ 39 ]. Another risk allele (rs4852954) identified in the analysis of macrovascular complications is located near the N-Acetyltransferase 8 coding gene ( NAT8 ) and has been previously associated with systolic blood pressure and renal function in the Estonian population [ 40 ] which is genetically close to the Latvian population [ 41 ].…”

Circulating miRNAs as a Predictive Biomarker of the Progression from Prediabetes to Diabetes: Outcomes of a 5-Year Prospective Observational Study

“…TRIM41 targets nucleoproteins for ubiquitination and protein degradation [ 90 ]. Little is known about the association between ANAPC13 and T2DM; however, it has been suggested to be one of the key differentially expressed genes in the livers of T2DM patients [ 91 ]. UBE2C overexpression is involved in the mis-segregation of chromosomes and alters the cell cycle process, facilitating cell proliferation.…”

Regulatory Master Genes Identification and Drug Repositioning by Integrative mRNA-miRNA Network Analysis for Acute Type A Aortic Dissection

“…The weighted correlations between ATAAD and all genes in GSE52093 or GSE98770 were analyzed by WGCNA package respectively as previously described ( Li et al, 2018 ). The soft threshold power was calculated automatically.…”