Bioinformatics Analysis of Weighted Genes in Diabetic Retinopathy

Abstract

Purpose: Intricate signaling networks and transcriptional regulators translate pathogen recognition into defense responses. The aim of this study was to identify the weighted genes involved in diabetic retinopathy (DR) in different rodent models of diabetes. Methods: We performed a gene coexpression analysis of publicly available microarray data, namely, the GSE19122 dataset from the Gene Expression Omnibus database. We conducted gene coexpression analysis on the microarray data to identify modules of functionally related coexpressed genes that are differentially expressed in different rodent models. We leveraged a richly curated expression dataset and used weighted gene coexpression network analysis to construct an undirected network. We screened 30 genes in the most closely related module. A protein-protein interaction network was constructed for the genes in the most related module using the Search Tool for the Retrieval of Interacting Genes. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed for the 30 genes. Results: Five visual perception-related genes (Pde6g, Guca1a, Rho, Sag, and Prph2) were significantly upregulated. Based on the competing endogenous RNA hypothesis, a link between the long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and visual perception-related mRNAs was constructed using bioinformatics tools. Six potential microRNAs (miR-155-5p, miR-1a-3p, miR-122-5p, miR-223-3p, miR-125b-5p, and miR-124-3p) were also screened. Conclusions: MALAT1 might play important roles in DR by regulating Sag and Guca1a through miR-124-3p and regulating Pde6g through miR-125b-5p.