Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease  

基于生物信息学分析糖尿病肾病自噬特征基因及潜在中药预测

作　　者：Yufeng XING Zining PENG Chaoyang YE 邢玉凤;彭紫凝;叶朝阳(上海中医药大学肾脏疾病研究所,上海200120;上海中医药大学附属曙光医院肾内科,上海200120;肝肾疾病教育部重点实验室/上海市中医临床重点实验室,上海200120;云南中医药大学第一临床医学院,云南昆明650500)

机构地区：[1]Institute of Kidney Disease,Shanghai University of Traditional Chinese Medicine,Shanghai 200120,China [2]Department of Nephrology,Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shanghai 200120,China [3]Key Laboratory of Liver and Kidney Diseases,Ministry of Education/Shanghai Key Laboratory of Traditional Chinese Clinical Medicine,Shanghai 200120,China [4]First Clinical Medical College,Yunnan University of Chinese Medicine,Kunming,Yunnan 650500,China

出　　处：《Digital Chinese Medicine》2025年第1期90-99,共10页数字中医药(英文)

基　　金：National Natural Science Foundation of China(82170747),and Shanghai Key Laboratory of Traditional Chinese Clinical Medicine(20DZ2272200).

摘　　要：Objective To predict the autophagy-related pathogenesis and key diagnostic genes of diabetic kidney disease(DKD)through bioinformatics analysis,and to identify related Chinese medicines.Methods Data from sequencing microarrays GSE30528,GSE30529,and GSE1009 in the Gene Expression Omnibus(GEO)were employed.Differentially expressed genes(DEGs)with adjusted P<0.05 from GSE30528 and GSE30529 were identified.Combining these DEGs with the human autophagy gene database,Gene Ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses,and protein-protein interaction(PPI)network analysis were conducted on the obtained DKD autophagy-related genes.Subsequently,the least absolute shrinkage and selection operator(LASSO)regression and support vector machinerecursive feature elimination(SVM-RFE)algorithms were adopted to select autophagy-related genes.The diagnostic capability of these genes was assessed through analysis with the external validation set from microarray GSE1009,and relevant Chinese medicines were inversely predicted using the SymMap database.Results A total of 2014 DEGs were selected from GSE30528 and GSE30529,leading to the identification of 37 DKD autophagy-related genes.GO analysis indicated 681 biological mechanisms,including autophagy regulation and plasma membrane microdomain activity.KEGG enrichment analysis identified 112 related signaling pathways.PPI network analysis showed a marked enrichment of autophagy-related genes in DKD.Through LASSO regression and SVM-RFE,four core diagnostic genes for autophagy in DKD were identified:protein phosphatase 1 regulatory subunit 15A(PPP1R15A),hypoxia inducible factor 1 alpha subunit(HIF1α),deleted in liver cancer 1(DLC1),and ceroid lipofuscinosis neuronal 3(CLN3).The external validation set demonstrated high diagnostic efficiency for these genes.Finally,146 kinds of potential Chinese medicines were predicted using the SymMap database,with heatclearing and detoxifying medicine and blood-activating and stasis-eliminating medicine accounting for the l目的基于生物信息学分析预测糖尿病肾脏疾病(DKD)自噬相关的发病机制和关键诊断基因,并预测相关中药。方法采用基因表达综合库(GEO)中测序芯片GSE30528、GSE30529和GSE1009的数据,从GSE30528和GSE30529芯片中鉴定出校正后P值<0.05的差异表达基因(DEGs),结合人类自噬基因库,对获得的DKD自噬特征基因进行基因本体(GO)、京都基因与基因组百科全书(KEGG)通路富集分析及蛋白质相互作用(PPI)网络分析,再通过最小绝对收缩和选择算子(LASSO)回归和支持向量机-递归特征消除(SVM-RFE)算法获取自噬特征基因。通过使用微阵列GSE1009的外部验证集进行分析来评估这些基因的诊断能力,并使用SymMap数据库反向预测相关中药。结果GSE30528和GSE30529芯片数据共筛选出2014个DEGs,鉴定出37个DKD自噬相关基因,GO分析显示681个包括自噬调节和细胞膜微区域活性在内的生物学机制,KEGG富集分析获得112条相关信号通路,PPI网络分析表明DKD自噬相关基因显著富集。通过LASSO回归和SVM-RFE算法确定了4个DKD自噬的核心诊断基因:蛋白磷酸酶1调控亚基15A(PPP1R15A)、缺氧可诱导因子1α亚基(HIF1α)、肝癌缺失1(DLC1)和蜡样色素脂褐质沉积神经元型3(CLN3),且外部验证集表明这些基因的诊断效率较高。最终通过SymMap数据库预测出146味潜在中药,其中清热解毒药和活血祛瘀药类占比最大,分别为25/146和13/146。结论本研究从生物信息学测序数据库分析和验证,明确DKD自噬的潜在分子机制,预测关键诊断基因、潜在治疗靶点以及相关中药,为临床研究及应用奠定了坚实基础。

关 键 词：BIOINFORMATICS Differentially expressed genes Diabetic kidney disease Autophagy genes Prediction of Chinese medicines 

分 类 号：R285[医药卫生—中药学]