Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes

Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body’s inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D.

1型糖尿病（Type 1 diabetes, T1D）是一种复杂疾病，由自身免疫破坏分泌胰岛素的胰腺β细胞所致，最终致使机体无法合成胰岛素。尽管科研人员已投入大量精力探究与该病病因学相关的遗传与环境因素，但其确切的分子机制仍未完全明晰。1型糖尿病属于异质性疾病，既往该领域的研究多聚焦于单基因分析或传统基因表达谱分析，而此类方法通常无法揭示与复杂疾病相关基因的功能背景。然而，基于网络的分析能够纳入糖尿病特异性基因或蛋白质间的相互作用，助力学界获取疾病模块相关的全新认知，进而可用于筛选1型糖尿病潜在的新型生物标志物。本研究采用整合了基于网络的加权基因共表达网络分析（Weighted Gene Co-Expression Network Analysis, WGCNA）与功能富集分析的系统生物学方法，对1型糖尿病患者与健康对照的公共微阵列数据进行分析。研究阐明了与1型糖尿病相关的新型共表达基因网络模块，为筛选可能参与1型糖尿病发病过程的潜在通路及生物标志物基因提供了理论依据。