Bioinformatics and Experimental Validation of Diagnostic Marker Genes for Myocardial Infarction and Analysis of Their Immune Cell Infiltration.

IF 1.6 4区生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY

Biochemical Genetics Pub Date : 2025-08-19 DOI:10.1007/s10528-025-11211-2

Shuxing Wu, Ru Wang, Jian Cui, Hongjie Huo, Zhuhua Yao

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引用次数: 0

Abstract

This study aimed to identify diagnostic marker genes for myocardial infarction (MI) and analyzed the key genes pertaining to immune cell infiltration. The MI expression microarrays GSE48060 and GSE66360 were retrieved and downloaded from the GEO database. The merged expression data were subjected to Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, differentially expressed genes (DEGs) were analyzed in MI. Primary rat cardiomyocytes (NRVMs) were isolated for an oxygen-glucose deprivation/reoxygenation (OGD/R) model, in which the effect of ICAM1, NFIL3, TULP2, and ZFP36 on cell phenotype experiments was detected. Gene differential expression analysis identified 96 significant DEGs, and the intersection of these genes with the module genes obtained from WGCNA analysis yielded 81 candidate genes. LASSO regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms identified 7 candidate diagnostic genes. ICAM1, NFIL3, TULP2, and ZFP36 exhibited good diagnostic potential in both experimental and validation datasets, showing significant correlations with immune cells, including Neutrophils. ICAM1, NFIL3, TULP2, and ZFP36 were markedly up-regulated in OGD/R-treated NRVMs, while ICAM1 knockdown suppressed NRVM damage triggered by OGD/R. ICAM1, NFIL3, TULP2, and ZFP36 can serve as candidate diagnostic genes for MI, and ICAM1 silencing can ameliorate OGD/R-elicited myocardial cell damage.

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心肌梗死诊断标记基因的生物信息学、实验验证及其免疫细胞浸润分析。

本研究旨在鉴定心肌梗死（MI）的诊断标记基因，并分析与免疫细胞浸润相关的关键基因。从GEO数据库中检索并下载MI表达芯片GSE48060和GSE66360。合并后的表达数据进行加权基因共表达网络分析（WGCNA）。分离大鼠原代心肌细胞（nrvm），建立氧-糖剥夺/再氧化（OGD/R）模型，检测ICAM1、NFIL3、TULP2和ZFP36对细胞表型的影响。基因差异表达分析鉴定出96个显著的deg，这些基因与WGCNA分析获得的模块基因相交得到81个候选基因。LASSO回归和支持向量机递归特征消除（SVM-RFE）算法确定了7个候选诊断基因。ICAM1、NFIL3、TULP2和ZFP36在实验和验证数据集中都表现出良好的诊断潜力，显示出与免疫细胞（包括中性粒细胞）的显著相关性。在OGD/R处理的NRVM中，ICAM1、NFIL3、TULP2和ZFP36显著上调，而ICAM1敲低抑制OGD/R引发的NRVM损伤。ICAM1、NFIL3、TULP2和ZFP36可以作为心肌梗死的候选诊断基因，ICAM1沉默可以改善OGD/ r诱导的心肌细胞损伤。

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来源期刊

Biochemical Genetics 生物-生化与分子生物学

CiteScore

3.90

自引率

0.00%

发文量

133

审稿时长

4.8 months

期刊介绍： Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses. Although studies focusing on particular regions and target organisms are welcome, it is not the journal’s goal to publish essentially descriptive studies that provide results with narrow applicability, or are based on very small samples or pseudoreplication. Rather, Biochemical Genetics welcomes review articles that go beyond summarizing previous publications and create added value through the systematic analysis and critique of the current state of knowledge or by conducting meta-analyses. Methodological articles are also within the scope of Biological Genetics, particularly when new laboratory techniques or computational approaches are fully described and thoroughly compared with the existing benchmark methods. Biochemical Genetics welcomes articles on the following topics: Genomics; Proteomics; Population genetics; Phylogenetics; Metagenomics; Microbial genetics; Genetics and evolution of wild and cultivated plants; Animal genetics and evolution; Human genetics and evolution; Genetic disorders; Genetic markers of diseases; Gene technology and therapy; Experimental and analytical methods; Statistical and computational methods.