Development of Mathematical Models Using circRNA Combinations (circTulp4, circSlc8a1, and circStrn3) in Mouse Brain Tissue for Postmortem Interval Estimation

The postmortem interval (PMI) is defined as the time interval between physiological death and the examination of the corpse, playing a critical role in forensic investigations. Traditional PMI estimation methods are often influenced by subjective and environmental factors. Circular RNAs (circRNAs), known for their stability, abundance, and conservation in brain tissue, show promise as biomarkers for PMI estimation. However, research on circRNAs in this context remains limited. This study aimed to develop PMI estimation models using circRNAs across multiple temperatures. By employing semi-quantitative reverse transcription-PCR, <i>circTulp4</i>, <i>circSlc8a1</i>, and <i>circStrn3</i> were identified as reliable biomarkers for mouse brain tissue. Mathematical models were constructed using the reference genes <i>28S</i> rRNA, <i>mt-co1</i>, and <i>circCDR1as</i>. At 4 °C, most equations had <i>p</i>-values below 0.05, with the equation using <i>circSlc8a1</i> as a marker exhibiting the highest goodness of fit. Validation results indicated that the equation using <i>circTulp4</i> as the reference gene had the highest accuracy. When applying the combined aforementioned three circRNAs, the equation using <i>circCDR1as</i> as the reference gene showed better accuracy. At 25 °C, all equations had R² values greater than 0.86, but most cubic equations had <i>p</i>-values above 0.05. Validation results demonstrated that the <i>circTulp4</i>/<i>mt-co1</i> equation had the highest accuracy. When applying combined circRNAs, the R² values improved, and long-term PMI estimation was more accurate than short-term PMI estimation. At 35 °C, the linear equations had significantly poorer goodness of fit compared to nonlinear equations, and nonlinear equations exhibited better accuracy than linear equations. When applying the combined aforementioned three circRNAs, the accuracy of the three reference genes was similar, and the accuracy of long-term PMI estimation was consistently higher than that of short-term estimation. For the three-dimensional models, all R² values exceeded 0.75 with <i>p</i>-values significantly below 0.0001. Validation results demonstrated higher accuracy at 25 °C and 35 °C, with superior performance for long-term PMI estimation. In summary, this study constructed PMI estimation models under multiple temperature conditions based on highly expressed circRNAs in mouse brain tissue, highlighting <i>circTulp4</i>, <i>circSlc8a1</i>, and <i>circStrn3</i> as novel biomarkers. These findings offer a complementary tool for PMI estimation, particularly for long-term PMI estimation.