Department of Pharmacy, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China (mainland).
Med Sci Monit. 2020 Jul 11;26:e924334. doi: 10.12659/MSM.924334.
BACKGROUND The underlying mechanism of insulin resistance is complex; bioinformatics analysis is used to explore the mechanism based differential expression genes (DEGs) obtained from omics analysis. However, the expression and role of most DEGs involved in bioinformatics analysis are invalidated. This study aimed to disclose the mechanism of insulin resistance via bioinformatics analysis based on validated insulin resistance-related genes (IRRGs) collected from public disease-gene databases. MATERIAL AND METHODS IRRGs were collected from 4 disease databases including NCBI-Gene, CTD, RGD, and Phenopedia. GO and KEGG analysis of IRRGs were performed by DAVID. Then, the STRING database was employed to construct a protein-protein interaction (PPI) network of IRRGs. The module analysis and hub genes identification were carried out by MCODE and cytoHubba plugin of Cytoscape based on the primary PPI network, respectively. RESULTS A total of 1195 IRRGs were identified. Response to drug, hypoxia, insulin, positive regulation of transcription from RNA polymerase II promoter, cell proliferation, inflammatory response, negative regulation of apoptotic process, glucose homeostasis, cellular response to insulin stimulus, and aging were proposed as the crucial functions related to insulin resistance. Ten insulin resistance-related pathways included the pathways of insulin resistance, pathways in cancer, adipocytokine, prostate cancer, PI3K-Akt, insulin, AMPK, HIF-1, prolactin, and pancreatic cancer signaling pathway were revealed. INS, AKT1, IL-6, TP53, TNF, VEGFA, MAPK3, EGFR, EGF, and SRC were identified as the top 10 hub genes. CONCLUSIONS The current study presented a landscape view of possible underlying mechanism of insulin resistance by bioinformatics analysis based on validated IRRGs.
胰岛素抵抗的潜在机制很复杂；通过生物信息学分析可以探索从组学分析中获得的差异表达基因（DEGs）的机制。然而，大多数涉及生物信息学分析的 DEG 的表达和作用都没有得到验证。本研究旨在通过从公共疾病基因数据库中收集已验证的胰岛素抵抗相关基因（IRRGs），基于生物信息学分析揭示胰岛素抵抗的机制。
从包括 NCBI-Gene、CTD、RGD 和 Phenopedia 在内的 4 个疾病数据库中收集 IRRGs。使用 DAVID 对 IRRGs 进行 GO 和 KEGG 分析。然后，使用 STRING 数据库构建 IRRGs 的蛋白质-蛋白质相互作用（PPI）网络。通过 Cytoscape 中的 MCODE 和 cytoHubba 插件，分别基于主要的 PPI 网络进行模块分析和枢纽基因识别。
共鉴定出 1195 个 IRRGs。与胰岛素抵抗相关的关键功能包括：药物反应、缺氧、胰岛素、RNA 聚合酶 II 启动子的转录正调控、细胞增殖、炎症反应、细胞凋亡过程的负调控、葡萄糖稳态、细胞对胰岛素刺激的反应和衰老。揭示了与胰岛素抵抗相关的 10 条通路，包括胰岛素抵抗通路、癌症通路、脂肪细胞因子、前列腺癌、PI3K-Akt、胰岛素、AMPK、HIF-1、催乳素和胰腺癌信号通路。INS、AKT1、IL-6、TP53、TNF、VEGFA、MAPK3、EGFR、EGF 和 SRC 被鉴定为前 10 个枢纽基因。
本研究通过基于已验证的 IRRGs 的生物信息学分析，呈现了胰岛素抵抗潜在机制的全景图。
