Exploring the paradigm of miRNA and cardiac troponin I expression in prolonged post-mortem interval

Abstract Background Accurate post-mortem interval (PMI) determination remains crucial in forensic investigations. Building on our previous findings of miRNA-378, miRNA-206, and miRNA-195 as potential PMI biomarkers, this study evaluates their efficacy in burn and electrocution cases. We incorporate cardiac troponin I (cTnI) to enhance estimation precision. This multi-biomarker approach aims to elucidate the combined potential of miRNA and protein-based indicators for PMI determinations across diverse causes of mortality, addressing the need for more robust estimation methods. These biomarkers were studied in human cardiac tissues of burn and electrocution cases which was maintained at room temperature. Enzyme-linked immunosorbent assay (ELISA) and Real-time, polymerase chain reaction (RT-PCR) was used for cTnI and miRNA analysis, respectively. Result In electrocution cases, a marked decrease in miRNA and cardiac troponin I (cTnI) expression correlates with increasing post-mortem interval (PMI). Specifically, miRNA-195 exhibited a 4.13-fold reduction from 3.1805 ± 1.873 at 12 h to -0.769 ± 2.111 at 196 h, with significant changes noted between 24 and 48 h ( p < 0.05). miRNA-206 displayed a 2.2-fold decline, with variability observed particularly between 12 and 72 h ( p < 0.05). ​cTnI revealed a substantial decrease, reinforcing its utility as an early PMI marker for electrocution fatalities.​ In contrast, burn cases showed an even sharper decline in miRNA-195 (6.84-fold), indicating a higher sensitivity to tissue degradation, while cTnI exhibited a more stable decrease. This comparative analysis illuminates distinct degradation patterns among biomarkers, enhancing their efficacy in PMI identification. Conclusion We combined miRNA expression levels and cTnI concentrations to improve the accuracy of PMI estimation. Thus, relevance of time-dependent variations in miRNAs and cTnI, is investigated and highlighted the approach for optimal design of biomarkers and analytical techniques for PMI estimation.