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Myocardial infarction (MI) is a heart disease with high morbidity and mortality rates, thus it is critical to identify genes that serve roles during its pathogenesis. The objective of the present study was to identify potentially relevant genes during the progression of the disease. Blood samples from patients with MI and normal controls (n=3/group) were obtained, the RNA was extracted and cDNA libraries were established. RNA sequencing (RNA-seq) was performed on a HiSeq 2500 platform and fragments per kilobase of exon per million fragments mapped was utilized to calculate the gene expression value following preprocessing of the RNA-seq data. Electronic validation of several identified differentially expressed genes (DEGs) was performed on a Gene Expression Omnibus (GEO) dataset GSE59867 (390 cases and 46 healthy controls). Functional enrichment and protein-protein interaction network analysis was conducted for DEGs. A total of 977 DEGs, including 817 upregulated and 160 downregulated genes were identified in patients with MI. These DEGs were significantly enriched for 'positive regulation of the immune system process,' 'inflammatory response,' 'regulation of I-kappaB-kinase/NF-kappaB signaling' and 'TNF signaling pathway'. A protein-protein interaction network of the top 40 DEGs was used to identify high degree genes, including interferon induced protein with tetratricopeptide repeats 3 (IFIT3), MX dynamin like GTPase 1 (MX1), major histocompatibility complex, class II, DQ α1 (HLA-DQA1), RAR related orphan receptor A (RORA), prostaglandin D2 synthase (PTGDS), cysteine rich protein 2 (CRIP2), collagen type VI α 2 chain (COL6A2) and S100 calcium binding protein P (S100P). The results of validation in the GEO dataset were consistent with the sequencing analysis. A total of eight genes, including IFIT3, MX1, HLA-DQA1, RORA, PTGDS, CRIP2, COL6A2 and S100P may therefore be considered as potentially relevant genes in the pathology of MI.

Identification of potentially relevant genes for myocardial infarction using RNA sequencing data analysis