Temporal RT-qPCR-Based Porcine Cardiac Molecular Profiling for Post-Mortem Interval Estimation: Predictive Modeling.
V, C., C, M., FJ, D., T, Z., K, G., W, R., M, Č., D, S., P, G., & A, A. (2026). Temporal RT-qPCR-Based Porcine Cardiac Molecular Profiling for Post-Mortem Interval Estimation: Predictive Modeling.. International journal of molecular sciences. https://doi.org/10.3390/ijms27114856
V C, C M, FJ D, T Z, K G, W R, et al. Temporal RT-qPCR-Based Porcine Cardiac Molecular Profiling for Post-Mortem Interval Estimation: Predictive Modeling.. International journal of molecular sciences. 2026; doi: 10.3390/ijms27114856
V C, C M, FJ D, et al. Temporal RT-qPCR-Based Porcine Cardiac Molecular Profiling for Post-Mortem Interval Estimation: Predictive Modeling.[J]. International journal of molecular sciences. 2026. DOI: 10.3390/ijms27114856.
@article{v2026,
author = {Cianci V and Mondello C and Diaz FJ and Zinger T and Giese K and Ryan W and Češpivová M and Sapienza D and Gualniera P and Asmundo A},
title = {Temporal RT-qPCR-Based Porcine Cardiac Molecular Profiling for Post-Mortem Interval Estimation: Predictive Modeling.},
journal = {International journal of molecular sciences},
year = {2026},
doi = {10.3390/ijms27114856},
note = {PMID: 42278385},
}
TY - JOUR AU - Cianci V AU - Mondello C AU - Diaz FJ AU - Zinger T AU - Giese K AU - Ryan W AU - Češpivová M AU - Sapienza D AU - Gualniera P AU - Asmundo A TI - Temporal RT-qPCR-Based Porcine Cardiac Molecular Profiling for Post-Mortem Interval Estimation: Predictive Modeling. T2 - International journal of molecular sciences PY - 2026 DO - 10.3390/ijms27114856 AN - PMID:42278385 ER -
Estimating the post-mortem interval (PMI) remains a major challenge in forensic pathology, particularly beyond the earliest postmortem phases. RNA-based markers measured by RT-qPCR have been proposed as potential tools for PMI estimation, but their reliability depends on technical robustness, reference gene stability, and data representation. Technical reproducibility was overall satisfactory. However, the candidate reference genes showed different temporal behaviors: ACTB remained relatively stable, whereas RPL4 displayed significant time-dependent drift, resulting in partial instability of the composite reference signal. All target genes were associated with PMI, with HPRT1 and HMOX1 emerging as the most informative markers. Several targets also showed evidence of non-linear temporal dynamics. In predictive analyses, models based on raw Ct values consistently outperformed ΔCt-based models. A parsimonious model based on HMOX1 and HPRT1 showed the most favorable trade-off between interpretability and predictive performance within this exploratory dataset, although prediction error remained non-negligible. These findings suggest that postmortem cardiac transcriptional profiles may contain temporal information useful for PMI-oriented modeling but also show that predictive performance remains limited by reference gene behavior, analytical strategy, and non-negligible estimation error. Nine porcine hearts were stored at 4 °C and sampled at 0, 12, 24, 48, 72, 96, and 120 h. RT-qPCR was performed in technical triplicate for selected target genes (BAX, CASP3, HIF1A, HMOX1) together with additional quantified transcripts (ACTB, RPL4, GAPDH, HPRT1). Technical reproducibility, reference gene stability, temporal trends and predictive performance were assessed using mixed-effects models and predictive models evaluated by leave-one-heart-out cross-validation. Comparative analyses were performed using raw Ct, ΔCt, and ΔΔCt data. Overall, postmortem cardiac RT-qPCR profiling should be regarded as a proof-of-concept framework developed under specific controlled refrigerated conditions. Therefore, further external validation under heterogeneous real-world forensic scenarios and methodological standardization are required before a real-life forensic application.