Integrated bioinformatics and machine learning algorithms reveal the critical cellular senescence-associated genes and immune infiltration in heart failure due to ischemic cardiomyopathy

Abstract

Heart failure (HF) is the final stage of many cardiovascular illnesses and the leading cause of death worldwide. At the same time, ischemic cardiomyopathy has replaced valvular heart disease and hypertension as the primary causes of heart failure. Cellular senescence in heart failure is currently receiving more attention. In this paper, we investigated the correlation between the immunological properties of myocardial tissue and the pathological mechanisms of cellular senescence during ischemic cardiomyopathy leading to heart failure (ICM-HF) using bioinformatics and machine learning methodologies. Our goals were to clarify the pathogenic causes of heart failure and find new treatment options. First, after obtaining GSE5406 from the Gene Expression Omnibus (GEO) database and doing limma analysis, differential genes (DEGs) among the ICM-HF and control groups were identified. We intersected these differential genes with cellular senescence-associated genes (CSAG) via the CellAge database to obtain 39 cellular senescence-associated DEGs (CSA-DEGs). Then, a functional enrichment analysis was performed to elucidate the precise biological processes by which the hub genes control cellular senescence and immunological pathways. Then, the respective key genes were identified by Random Forest (RF) method, LASSO (Least Absolute Shrinkage and Selection Operator) algorithms, and Cytoscape's MCODE plug-in. Three sets of key genes were taken to intersect to obtain three CSA-signature genes (including MYC, MAP2K1, and STAT3), and these three CSA-signature genes were validated in the test gene set (GSE57345), and Nomogram analysis was done. In addition, we assessed the relationship between these three CSA- signature genes and the immunological landscape of heart failure encompassing immunological infiltration expression profiles. This work implies that cellular senescence may have a crucial role in the pathogenesis of ICM-HF, which may be closely tied to its effect on the immune microenvironment. Exploring the molecular underpinnings of cellular senescence during ICM-HF is anticipated to yield significant advances in the disease's diagnosis and therapy.

Keywords: bioinformatics; cellular senescence; gene; heart failure; machine learning.