Cao Zhaoming, Du Yage, Xu Guangyi, Zhu He, Ma Yinchao, Wang Ziyuan, Wang Shaoying, Lu Yanhui
School of Nursing, Peking University, Beijing 100191, China.
School of Stomatology, Peking University, Beijing 100191, China.
Brain Sci. 2024 Oct 18;14(10):1035. doi: 10.3390/brainsci14101035.
To provide a basis for further research on the molecular mechanisms underlying type 2 diabetes-associated mild cognitive impairment (DCI) using two bioinformatics methods to screen key genes involved in the progression of mild cognitive impairment (MCI) and type 2 diabetes.
RNA sequencing data of MCI and normal cognition groups, as well as expression profile and sample information data of clinical characteristic data of GSE63060, which contains 160 MCI samples and 104 normal samples, were downloaded from the GEO database. Hub genes were identified using weighted gene co-expression network analysis (WGCNA). Protein-protein interaction (PPI) analysis, combined with least absolute shrinkage and selection operator (LASSO) and receiver operating characteristic (ROC) curve analyses, was used to verify the genes. Moreover, RNA sequencing and clinical characteristic data for GSE166502 of 13 type 2 diabetes samples and 13 normal controls were downloaded from the GEO database, and the correlation between the screened genes and type 2 diabetes was verified by difference and ROC curve analyses. In addition, we collected clinical biopsies to validate the results.
Based on WGCNA, 10 modules were integrated, and six were correlated with MCI. Six hub genes associated with MCI (TOMM7, SNRPG, COX7C, UQCRQ, RPL31, and RPS24) were identified using the LASSO algorithm. The ROC curve was screened by integrating the GEO database, and revealed COX7C, SNRPG, TOMM7, and RPS24 as key genes in the progression of type 2 diabetes.
COX7C, SNRPG, TOMM7, and RPS24 are involved in MCI and type 2 diabetes progression. Therefore, the molecular mechanisms of these four genes in the development of type 2 diabetes-associated MCI should be studied.
运用两种生物信息学方法筛选参与轻度认知障碍（MCI）和2型糖尿病进展的关键基因，为进一步研究2型糖尿病相关轻度认知障碍（DCI）的分子机制提供依据。
从GEO数据库下载MCI组与正常认知组的RNA测序数据，以及包含160个MCI样本和104个正常样本的GSE63060临床特征数据的表达谱和样本信息数据。采用加权基因共表达网络分析（WGCNA）鉴定枢纽基因。运用蛋白质-蛋白质相互作用（PPI）分析，并结合最小绝对收缩和选择算子（LASSO）及受试者工作特征（ROC）曲线分析来验证这些基因。此外，从GEO数据库下载13个2型糖尿病样本和13个正常对照的GSE166502的RNA测序及临床特征数据，通过差异分析和ROC曲线分析验证筛选出的基因与2型糖尿病之间的相关性。另外，我们收集临床活检样本以验证结果。
基于WGCNA整合了10个模块，其中6个与MCI相关。使用LASSO算法鉴定出6个与MCI相关的枢纽基因（TOMM7、SNRPG、COX7C、UQCRQ、RPL31和RPS24）。通过整合GEO数据库筛选ROC曲线，结果显示COX7C、SNRPG、TOMM7和RPS24是2型糖尿病进展中的关键基因。
COX7C、SNRPG、TOMM7和RPS24参与MCI和2型糖尿病的进展。因此，应研究这四个基因在2型糖尿病相关MCI发生发展中的分子机制。
