Identification of key gene pathways and coexpression networks of islets in human type 2 diabetes

Abstract

Purpose

The number of people with type 2 diabetes (T2D) is growing rapidly worldwide. Islet β-cell dysfunction and failure are the main causes of T2D pathological processes. The aim of this study was to elucidate the underlying pathways and coexpression networks in T2D islets.

Materials and methods

We analyzed the differentially expressed genes (DEGs) in the data set GSE41762, which contained 57 nondiabetic and 20 diabetic samples, and developed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein–protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed as well. Moreover, a weighted correlation network analysis (WGCNA) was applied to screen critical gene modules and coexpression networks and explore the biological significance.

Results

We filtered 957 DEGs in T2D islets. Then GO and KEGG analyses identified that key pathways like inflammatory response, type B pancreatic cell differentiation, and calcium ion-dependent exocytosis were involved in human T2D. Three significant modules were filtered from the PPI network. Ribosome biogenesis, extrinsic apoptotic signaling pathway, and membrane depolarization during action potential were associated with the modules, respectively. Furthermore, coexpression network analysis by WGCNA identified 13 distinct gene modules of T2D islets and revealed four modules, which were strongly correlated with T2D and T2D biomarker hemoglobin A1c (HbA1c). Functional annotation showed that these modules mainly enriched KEGG pathways such as NF-kappa B signaling pathway, tumor necrosis factor signaling pathway, cyclic adenosine monophosphate signaling pathway, and peroxisome proliferators-activated receptor signaling pathway.

Conclusion

The results provide potential gene pathways and underlying molecular mechanisms for the prevention, diagnosis, and treatment of T2D.

Keywords:

• type 2 diabetes
• islet β cell
• bioinformatics analysis
• differentially expressed genes
• WGCNA

Acknowledgments

This work was supported by The National Natural Science Foundation of China (No 81703578).

Author contributions

All authors contributed to this work. LL designed the work and performed the data analysis. ZP performed the data analysis and drafted the manuscript. SY performed the data analysis and revised the manuscript. WS analyzed the data and revised the manuscript. YY helped to draft the manuscript. All authors contributed toward data analysis, drafting and critically revising the paper and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

Supplementary material

Figure S1 GO analysis classified the DEGs ([A] upregulated DEGs and [B] downregulated DEGs) into cellular component and molecular function of T2D. Abbreviations: DEG, differentially expressed genes; GO, Gene Ontology; T2D, type 2 diabetes.