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<p>Determining the deposition time of a biological stain can clarify the time of the case and strengthen the association between the biological evidence and the timeline of the criminal event. This study evaluated several RNA molecules with diurnal expression patterns to estimate the deposition time at 3-hour intervals within one day. The relative expression of 11 microRNAs (miRNAs) and 5 messenger RNAs (mRNAs) that have been reported to exhibit diurnal oscillating expression was detected by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). Four RNA biomarkers exhibited significant day/night differential expressions. Time estimation models were developed based on these rhythmic RNA markers, using miRNAs, mRNAs, or both to predict the categories of deposition time. Binary logistic regression models with leave-one-out cross-validation were built to infer the day/night time categories. The model, including two miRNAs (hsa-miR-150-5p and hsa-miR-192a-5p) exhibited better predictive performance (area under the curve [AUC] = 0.796) compared to the mRNA-only model (AUC = 0.706) and combined model (AUC = 0.776). A Support Vector Machine (SVM) model was used to predict three-time categories. The results of a combined model using 14 RNA biomarkers exhibited an overall classification accuracy of 0.67, with a 95% confidence interval of 0.575–0.759 for three fold cross-validation. The stability assessment indicated that miRNAs were highly stable, while mRNAs were slightly less stable. This study is the first to demonstrate the potential of diurnal rhythmic miRNAs, combined with mRNA biomarkers, for estimating blood deposition time in forensic applications.</p>

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Estimating time of day from fingertip blood samples using RNA molecules with diurnal oscillating expression: a proof-of-principle study

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Junyan Wang,
  • Yanhao Zhao,
  • Tianyi Song,
  • Li Yang,
  • Qinmin Chen,
  • Yusi Zhu,
  • Bin Cong,
  • Shujin Li

摘要

Determining the deposition time of a biological stain can clarify the time of the case and strengthen the association between the biological evidence and the timeline of the criminal event. This study evaluated several RNA molecules with diurnal expression patterns to estimate the deposition time at 3-hour intervals within one day. The relative expression of 11 microRNAs (miRNAs) and 5 messenger RNAs (mRNAs) that have been reported to exhibit diurnal oscillating expression was detected by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). Four RNA biomarkers exhibited significant day/night differential expressions. Time estimation models were developed based on these rhythmic RNA markers, using miRNAs, mRNAs, or both to predict the categories of deposition time. Binary logistic regression models with leave-one-out cross-validation were built to infer the day/night time categories. The model, including two miRNAs (hsa-miR-150-5p and hsa-miR-192a-5p) exhibited better predictive performance (area under the curve [AUC] = 0.796) compared to the mRNA-only model (AUC = 0.706) and combined model (AUC = 0.776). A Support Vector Machine (SVM) model was used to predict three-time categories. The results of a combined model using 14 RNA biomarkers exhibited an overall classification accuracy of 0.67, with a 95% confidence interval of 0.575–0.759 for three fold cross-validation. The stability assessment indicated that miRNAs were highly stable, while mRNAs were slightly less stable. This study is the first to demonstrate the potential of diurnal rhythmic miRNAs, combined with mRNA biomarkers, for estimating blood deposition time in forensic applications.