中国药理学通报2026，Vol.42Issue(5)：930-937,8.DOI:10.12360/CPB202506028
阿尔茨海默病失巢凋亡相关基因挖掘与验证
Mining and verification of anoikis-related genes in Alzheimer's disease
摘要
Abstract
Aim To identify the expression character-istics of anoikis-related genes in Alzheimer's disease(AD)using machine learning algorithms and evaluate their diagnostic value.Methods The GEO dataset GSE44770 was used as the training set to screen for anoikis-related differentially expressed genes(DEGs).Consensus clustering was applied to classify AD patients into C1 and C2 subtypes.Weighted gene co-expression network analysis(WGCNA)was em-ployed to identify AD-related feature genes.Diagnos-tic models were constructed using Random Forest,Support Vector Machine,eXtreme Gradient Boosting(XGBoost),and Generalized Linear Model.The opti-mal model was selected,and key genes most corre-lated with AD clinical traits were extracted.Correla-tion analysis between these key genes and anoikis-related DEGs was conducted to identify crucial anoikis-related DEGs.Experimental validation was performed using APP/PSI transgenic AD model mice through HE staining,Western blot,and RT-qPCR.Results A total of 20 anoikis-related DEGs were identified.AD patients were classified into C1 and C2 subtypes,with the C2 subtype exhibiting enhanced glycolysis,acti-vated inflammation,and suppressed mitochondrial function.WGCNA identified 243 AD-related genes.The XGBoost model demonstrated the best perfor-mance,and five key genes(including OLFM1 and ITPKB)significantly associated with clinical traits were identified.Systematic correlation analysis be-tween these key genes and anoikis-related DEGs ulti-mately screened UCHL1,TNFRSF10B,and ITGB5 as crucial anoikis-related DEGs.Animal experiments re-vealed significant hippocampal neuronal damage,ab-normal p-Tau protein accumulation,and consistent ex-pression trends of the key anoikis-related DEGs with bioinformatic predictions.Conclusion Anoikis plays an important role in the pathogenesis of AD,and its key genes may serve as potential diagnostic biomarkers and therapeutic targets.关键词
阿尔茨海默病/失巢凋亡/生物信息学/机器学习/实验验证/差异基因Key words
Alzheimer's disease/anoikis/bioinfor-matics/machine learning/experimental validation/differentially expressed genes分类
医药卫生引用本文复制引用
黄颖睿,吴林,朱小敏,符钰岚,张颖,卓桂锋,郝二伟,陈炜..阿尔茨海默病失巢凋亡相关基因挖掘与验证[J].中国药理学通报,2026,42(5):930-937,8.基金项目
国家自然科学基金资助项目(No 82460906,82060844) （No 82460906,82060844）
广西中医药大学"岐黄工程"高层次人才团队(No 202410) （No 202410）
广西中医脑病临床研究中心(桂科AD20238028) （桂科AD20238028）
国家中医药管理局高水平重点学科-中医内科学(No ZYYZDXK-2023166) （No ZYYZDXK-2023166）
广西中医药重点学科建设项目(No GZXK-Z-20-13) （No GZXK-Z-20-13）
