Data Sheet 1_Identification of biomarkers between coronary artery disease and non-alcoholic steatohepatitis: a combination of bioinformatics and machine learning.pdf

BackgroundNon-alcoholic steatohepatitis (NASH) commonly complicates coronary artery disease (CAD), yet the interaction mechanism remains unclear. Our research seeks to investigate the common mechanisms and key signature genes between CAD and NASH.
MethodsRNA sequence information for CAD and NASH was screened from the GEO database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene analysis identified key genes, followed by functional enrichment analysis of these shared genes. Three machine learning methods—LASSO, random forest, and SVM-RFE—were used to identify signature genes. Gene set enrichment analysis (GSEA) was then performed to explore potential mechanisms associated with the signature genes. In addition, single-sample gene set enrichment analysis (ssGSEA) evaluated immune infiltration in CAD and NASH and its correlation with the signature genes.
ResultsWGCNA has revealed two key modules for CAD and NASH. The intersection of the CAD modules and their differential genes narrowed the key genes down to 2,808 shared genes. Finally, 44 shared genes were selected for both CAD and NASH. Kyoto Encyclopedia of Genes and Genomes analysis showed that these genes were primarily enriched in insulin resistance and inflammation pathways. Machine learning identified the signature genes BATF3, SOCS2, and GPER, all with ROC values above 0.7, validated in external datasets. GSEA revealed that these genes act through common mechanisms in CAD and NASH, regulating metabolic, inflammatory, and cardiovascular pathways. In addition, ssGSEA suggested their involvement in immune cell infiltration.
ConclusionBATF3, SOCS2, and GPER have emerged as promising gene candidates that may serve as biomarkers or potential therapeutic targets for CAD combined with NASH, linked to the regulation of metabolic, inflammatory, and cardiovascular pathways. We also identified insulin resistance and inflammation pathways as common mechanisms underlying both diseases.

背景：非酒精性脂肪性肝炎（Non-alcoholic steatohepatitis, NASH）常并发冠状动脉疾病（Coronary artery disease, CAD），但其相互作用机制仍未阐明。本研究旨在探讨CAD与NASH之间的共同发病机制及关键特征基因。
方法：从基因表达综合数据库（Gene Expression Omnibus, GEO）中筛选CAD与NASH的RNA序列信息。采用加权基因共表达网络分析（Weighted gene co-expression network analysis, WGCNA）与差异表达基因分析筛选关键基因，随后对这些共享基因开展功能富集分析。通过LASSO、随机森林（Random Forest）与支持向量机-递归特征消除（Support Vector Machine-Recursive Feature Elimination, SVM-RFE）三种机器学习方法识别特征基因。随后进行基因集富集分析（Gene Set Enrichment Analysis, GSEA），以探究特征基因相关的潜在发病机制。此外，采用单样本基因集富集分析（Single-Sample Gene Set Enrichment Analysis, ssGSEA）评估CAD与NASH中的免疫浸润情况及其与特征基因的相关性。
结果：WGCNA筛选出CAD与NASH的两个关键模块。将CAD模块与其差异基因取交集，将关键基因缩减至2808个共享基因，最终筛选出CAD与NASH共有的44个基因。京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）分析显示，这些基因主要富集于胰岛素抵抗与炎症通路。机器学习方法识别出特征基因BATF3、SOCS2与GPER，其受试者工作特征（Receiver Operating Characteristic, ROC）曲线下面积均高于0.7，并在外部数据集得到验证。GSEA结果显示，这些基因通过共同机制在CAD与NASH中发挥作用，调控代谢、炎症与心血管通路。此外，ssGSEA结果提示其参与免疫细胞浸润过程。
结论：BATF3、SOCS2与GPER有望成为CAD合并NASH的潜在生物标志物或治疗靶点，其作用与代谢、炎症及心血管通路的调控密切相关。本研究同时明确了胰岛素抵抗与炎症通路作为两种疾病共有的潜在发病机制。