Weighted gene co-expression network analysis identifies important pancreatic islet molecules related to type 2 diabetes / 中华内分泌代谢杂志

ABSTRACT

Objective To identify potential molecule targets of type 2 diabetes using weighted gene co-expression network analysis. Methods Microarray data of type 2 diabetes (GSE38642) were downloaded from Gene Expression Omnibus of NCBI, including 9 type 2 diabetic patients, 9 pre-diabetic patients ( 6%≤HbA1C<6.5%), and 31 normal controls (HbA1C<6%). Using weighted gene co-expression network analysis (WGCNA) package in R, the weighted gene co-expression network was built and significant modules related to clinical traits were identified. Then, functional and pathway enrichment analysis were conducted for genes in the most significant modules using GeneAnswers package in R. Upstream transcription factor enrichment analysis were conducted using TRANSFAC database. The hub genes and upstream transcription factors were selected as potential molecule targets of type 2 diabetes. Results 34 modules were identified in the co-expression network. Green module was positive correlated with HbA1C(R＝0.47, P＝1×10－4). The enriched functions were cell adhesion, extracellular matrix disassembly, etc. The enriched KEGG pathways were Pancreatic secretion, Focal adhesion, etc. ITGA6, ZAK, and YBX3 are hub genes of Green module. Brown module was negative correlated with HbA1C(R＝0.46, P＝1×10－4). The enriched functions were synapse, transmembrane transporter activity, etc. The enriched KEGG pathways were Insulin secretion, Dopaminergic synapse, etc. The upstream transcription factors PAX6, REST, and PDX1 of Brown module might play important roles. 30 hub genes, including SLC4A10, ELAVL4, and SYT14, were identified in Brown module. The relationships between these genes and type 2 diabetes were confirmed by previously published studies. Conclusion Important genes related to type 2 diabetes can be filtered out from transcriptome profiles using gene co-expression analysis. Our finding might provide a novel insight into the underlying molecular mechanism of type 2 diabetes.