Identification of 4 autophagy-related genes in heart failure by bioinformatics analysis and machine learning

• DOI: 10.3389/fcvm.2024.1247079
• Publication Date: 2024-01-29T04:40:29Z
• Authors: Xiwei Deng; Ziqi Yang; Tongzheng Li; Yang Wang; Qinchuan Yang; Rui An; Jian Xu
• Keywords: KEGG

Introduction Autophagy refers to the process of breaking down and recycling damaged or unnecessary components within a cell maintain cellular homeostasis. Heart failure (HF) is severe medical condition that poses serious threat patient's life. known play pivotal role in pathogenesis HF. However, our understanding specific mechanisms involved remains incomplete. Here, we identify autophagy-related genes (ARGs) associated with HF, which believe will contribute further comprehending Methods By searching GEO (Gene Expression Omnibus) database, found GSE57338 dataset, was related ARGs were obtained from HADb HAMdb databases. Annotation GO enrichment analysis KEGG pathway carried out on differentially expressed (AR-DEGs). We employed machine learning algorithms conduct thorough screening significant validated these by analyzing external dataset GSE76701 conducting mouse models experimentation. At last, immune infiltration conducted, target drugs screened TF regulatory network constructed. Results Through processing R language, total 442 DEGs. Additionally, retrieved 803 database. The intersection two sets resulted 15 AR-DEGs. Upon performing functional analysis, it discovered exhibited domains “regulation growth”, “icosatetraenoic acid binding”, “IL-17 signaling pathway”. After verification, ultimately identified 4 key genes. Finally, an illustrated discrepancies 16 distinct types cells between HF control group up 194 potential TFs based Discussion In this study, TPCN1, MAP2K1, S100A9, CD38 considered as With relevant data, exploration molecular autophagy can be out.