Unveiling Immune Infiltration Characterizing Genes in Hypertrophic Cardiomyopathy Through Transcriptomics and Bioinformatics

Jianmin Gong,1,2,* Bo Shi,1,3,* Ping Yang,1 Adeel Khan,4 Tao Xiong,2 Zhiyang Li1 1Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210000, People’s Republic of China; 2College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China; 3Department of Clinical Laboratory, Nanjing Jiangning Hospital of Chinese Medicine (CM), Nanjing, 211100, People’s Republic of China; 4Department of Biotechnology, University of Science and Technology Bannu, Bannu, 28100, Islamic Republic of Pakistan*These authors contributed equally to this workCorrespondence: Tao Xiong, College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China, Tel +8613872387410, Email xiongtao@yangtzeu.edu.cn Zhiyang Li, Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, People’s Republic of China, Tel +8618951703765, Email lizhiyang@nju.edu.cnBackground: Hypertrophic cardiomyopathy (HCM) is a dominantly inherited disease associated with sudden immune cell associations that remain unclear. The aim of this study was to comprehensively screen candidate markers associated with HCM and immune cells and explore potential pathogenic pathways.Methods: First, download the GSE32453 dataset to identify differentially expressed genes (DEGs) and perform Gene Ontology and pathway enrichment analysis using DAVID and GSEA. Next, construct protein-protein interaction (PPI) networks using String and Cytoscape to identify hub genes. Afterward, use CIBERSORT to determine the proportion of immune cells attributed to key genes in HCM and conduct ROC analysis based on the external dataset GSE36961 to evaluate their diagnostic value. Finally, validate the expression of key genes in the hypertrophic cardiomyocyte model through qRT-PCR using data from the HPA database.Results: Comprehensive analysis revealed that there were 254 upregulated genes and 181 downregulated genes in HCM. The enrichment study underscored pathways of inflammatory signaling, including MAPK and PI3K-Akt pathways. Pathways abundant in genes associated with HCM encompassed myocardial contraction and NADH dehydrogenase activity. Additionally, the analysis of immune infiltration revealed a notable increase in macrophages, NK cells, and monocytes in the HCM group, showing statistically significant variances in CD4 memory resting T cell infiltration when compared to the healthy control group. Within the validation dataset GSE36961, the Area Under the Curve (AUC) scores for eight crucial genes (FOS, CD86, CD68, BDNF, PIK3R1, PLEK, RAC2, CCL2) each exceeded 0.8. The HPA database revealed the positioning traits and paths of these eight crucial genes in smooth muscle cells, myocardial cells, and fibroblasts. The outcomes of the qRT-PCR were aligned with the sequencing findings.Conclusion: Bioinformatics analysis unveiled pivotal genes, pathways, and immune involvement, illuminating the molecular underpinnings of HCM. These findings suggest promising therapeutic targets for clinical applications.Keywords: hypertrophic cardiomyopathy, bioinformatic, immune cells infiltration, biomarker