Integrated Strategies of Diverse Feature Selection Methods Identify Aging-Based Reliable Gene Signatures for Ischemic Cardiomyopathy

Ischemic cardiomyopathy (ICM) is a major cardiovascular state associated with prominently increased morbidity and mortality. Our purpose was to detect reliable gene signatures for ICM through integrated feature selection strategies. Transcriptome profiles of ICM were curated from the GEO project. Classification models, including least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest, were adopted for identifying candidate ICM-specific genes for ICM. Immune cell infiltrates were estimated using the CIBERSORT method. Expressions of candidate genes were verified in ICM and healthy myocardial tissues Western blotting. JC-1 staining, flow cytometry, and TUNEL staining were presented in hypoxia/reoxygenation (H/R)-stimulated H9C2 cells with TRMT5 deficiency. Following the integration of three feature selection methods, we identified seven candidate ICM-specific genes including ASPN, TRMT5, LUM, FCN3, CNN1, PCNT, and HOPX. ROC curves confirmed the excellent diagnostic efficacy of this combination of previous candidate genes in ICM. Most of them presented prominent interactions with immune cell infiltrates. Their deregulations were confirmed in ICM than healthy myocardial tissues. TRMT5 expressions were remarkedly upregulated in H/R-stimulated H9C2 cells. TRMT5 deficiency enhanced mitochondrial membrane potential and reduced apoptosis in H/R-exposed H9C2 cells. Collectively, our findings identified reliable gene signatures through combination strategies of diverse feature selection methods, which facilitated the early detection of ICM and revealed the underlying mechanisms.