基于生物信息学预测冠心病心肌梗死的诊断性生物标志物及靶向铜死亡相关基因的中药  

Prediction of Diagnostic Biomarkers and TCM Targeting Cuprotosis-Related Genes for Myocardial Infarction Based on Bioinformatics

作　　者：祁祥 曹珊[1] 段凯旋 张艺嘉 QI Xiang;CAO Shan;DUAN Kaixuan;ZHANG Yijia(Henan University of Chinese Medicine,Zhengzhou 450046 Henan,China)

机构地区：[1]河南中医药大学,河南郑州450046

出　　处：《中药新药与临床药理》2024年第5期694-705,共12页Traditional Chinese Drug Research and Clinical Pharmacology

基　　金：河南省自然科学基金项目(242300421295);河南省科技攻关项目(232102310434);河南省中医药科学研究重大专项课题(2022ZYZD20);崔应民全国名老中医药专家传承工作室建设项目(国中医药人教函[2022]75号);河南省中医药科学研究重点课题(2023ZY1031)。

摘　　要：目的冠心病(coronary heart disease,CHD)是目前全球主要的致死性疾病之一,对生物标志物的检测是目前评估冠心病进展的重要无创方法,对冠心病的诊断和二级预防有着重要意义。本研究旨在筛选冠心病心肌梗死发病进程中的诊断性生物标志物,分析该病发展过程中的铜死亡相关基因,进一步预测能调控铜死亡相关基因的中药。方法检索GEO数据库获得冠心病心肌梗死芯片数据,分析差异表达基因(Differentially expressed genes,DEGs),对差异基因进行富集分析,基于最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)与随机森林(Random Forest,RF)方法筛选关键基因,构建诊断性模型并进行验证。对差异基因进行免疫细胞浸润分析,结果进一步结合加权基因共表达网络分析获得差异表达的免疫相关基因,与铜死亡基因取交集获得铜死亡免疫相关的核心(Hub)基因,分析铜死亡相关基因与诊断性基因的相关性。对铜死亡相关基因进行基因集富集分析(Gene Set Enrichment Analysis,GSEA),进一步预测调控铜死亡相关基因的中药。结果差异分析获得115个DEGs,DEGs主要富集于淋巴细胞介导的免疫,线粒体呼吸链复合体Ⅳ等生物学过程和C型凝集素受体信号通路,趋化因子信号通路。机器学习方法筛选出SNORA20、SNORA19、H4C3、SNORD81、COX7B五个诊断性基因。免疫浸润分析发现树突状细胞,巨噬细胞M2,单核细胞,中性粒细胞,自然杀伤细胞,CD4+T细胞,CD8+T细胞,γδT细胞,这也表明以上8种免疫细胞对冠心病心肌梗死的发病发挥着一定作用。加权基因共表达网络分析(Weighted correlation networkanalysis,WGCNA)结合免疫浸润分析获得358个关键模块基因,与铜死亡基因取交集获得3个铜死亡与免疫特征基因。5个诊断性基因与Hub基因的相关性分析结果显示SLC31A1与SNORA20,LIAS与SNORA19、SNORD81,MTF1与H4C3、SNORA20、SNORA19、SNORD81Objective Coronary heart disease(CHD)is one of the major lethal diseases in the world at present.The detection of biomarkers is an important non-invasive method to evaluate the progression of CHD,which is of great significance for the diagnosis and secondary prevention of CHD.This study aims to screen diagnostic biomarkers in the pathogenesis of myocardial infarction,analyze cuprotosis-related genes in the development of this disease,and further predict the traditional Chinese medicine of regulating cuprotosis-related genes.Methods The GEO database was searched to obtain chip data of myocardial infarction,differentially expressed genes(DEGs)were analyzed.Then,DEGs enrichment analysis was performed,and key genes were screened based on least absolute shrinkage and selection operator(LASSO)and random forest(RF)methods.Diagnostic model was constructed and verified.After immune cell infiltration analysis was performed on differential genes,the results were further combined with weighted gene co-expression network analysis to obtain differentially expressed immune-related genes,which were intersected with cuproptosis genes to obtain cuproptosis immune-related Hub genes.The correlation between cuproptosis-related genes and diagnostic genes were analyzed.Gene set enrichment analysis(GSEA)was performed on cuproptosis-related genes to further predict the traditional Chinese medicines of regulating the genes related to cuproptosis.Results A total of 115 DEGs,which were mainly enriched in the biological processes and pathways related to lymphocyte-mediated immunity,mitochondrial respiratory chain complexⅣ,C-type lectin receptor signaling pathway,and chemokine signaling pathway,were obtained by differential analysis.Five diagnostic genes,SNORA20,SNORA19,H4C3,SNORD81,and COX7B,were screened out by machine learning methods.Immune infiltration analysis found dendritic cells,macrophages M2,monocytes,neutrophils,natural killer cells,CD4+T cells,CD8+T cells,andγδT cells.It was indicated the above eight immune cells play a certa

关 键 词：冠心病 心肌梗死 机器学习 生物信息学 铜死亡 生物标志物 活血化瘀药 行气止痛药 

分 类 号：R285.5[医药卫生—中药学]