Validating the temporal performance of genetic biomarkers in an animal model of recurrence/ non-recurrence myocardial infarction persuades by bioinformatics tools.

With a global towering prevalence of index acute myocardial infarction (nonrecurrent MI, NR-MI), a high incidence of recurrent MI (R-MI) has emerged in recent decades. Despite the extensive occurrence, the promising predictors of R-MI have been elusive within the cohort of survivors. This study investigates and validates the involvement of distinct gene expressions in R-MI and NR-MI. Bioinformatics tools were used to identify DEGs from the GEO dataset, functional annotation, pathway enrichment analysis, and the PPI network analysis to find hub genes. The validation of proposed genes was conceded by qRT-PCR and Western Blot analysis in experimentally induced NR-MI and R-MI models on a temporal basis. The temporal findings based on RT-PCR consequences reveal a significant and constant upregulation of the UBE2N in the NR-MI model out of the proposed three DEGs (UBE2N, UBB, and TMEM189), while no expression was reported in the R-MI model. Additionally, the proteomics study proposed five DEGs (IL2RB, NKG7, GZMH, CXCR6, and GZMK) for the R-MI model since IL2RB was spotted for significant and persistent downregulation with different time points. Further, Western Blot analysis validated these target genes' expressions temporally. I/R-induced NR-MI and R-MI models were confirmed by the biochemical parameters (CKMB, LDH, cTnI, serum nitrite/nitrate concentration, and inflammatory cytokines) and histological assessments of myocardial tissue. These results underscore the importance of understanding genetic mechanisms underlying MI and highlight the potential of UBE2N and IL2RB as biomarkers for non-recurrent and recurrent MI, respectively. [Display omitted] • Integrate bioinformatic tools to explore biomarkers in non-recurring/recurring MI. • Confirms findings via qRT-PCR and Western Blot analysis, ensuring reliability. • Examines temporal gene expression patterns in experimentally induced MI models. • Supplements gene analysis with proteomics data confirms the involved biomarkers. • Genetic marker validation in endorsed MI models highlights translational potential. [ABSTRACT FROM AUTHOR]