Developing a Novel Forensic Age Assessment Strategy for Aged Blood Samples by Combining PiRNA and MiRNA Markers

Abstract

In forensic investigations, age estimation is vital in determining whether the suspect is under or over the legally defined age and narrowing the scope of searching for a suspect. With recent breakthroughs in high-throughput RNA sequencing analysis technology, small noncoding RNAs provide new ways to solve age estimation puzzles in trace or age samples due to their small molecular weight and better stability. MiRNAs have already been applied in the age prediction of bloodstains in our previous study and further improvement of the existing models is needed. PiRNAs (Piwi-interactingRNAs) are 24-32 nt single-stranded noncoding small RNA molecules involved in the PIWI-piRNA pathway and play an important role in the aging process. In this study, we attempted to explore the possibility of the simultaneous analysis of piRNAs and miRNAs in age estimation. Five age-related piRNAs were obtained from blood samples stored for eight years based on massive parallel sequencing. After further studied by real-time PCR, two piRNAs, piR-000753 and piR-020548, showed a relative higher efficiency in age estimiation. In addition to these two piRNAs, two age-related miRNAs, miR-324-3p and miR-330-5p, were also applied to build the prediction models. A novel age predication model for aged blood samples was established using multiple machine learning algorithms. In all the algorithms, AdaBoost showed a minimum MAE of 4.4. The error of the model is less than 5 years for 61.67% of the samples and less than 10 years for 88.33%. Our results suggested that the combined use of piRNA and miRNA markers may increase the accuracy of the age estimation model and have great potential for application in forensic case work.

Note:
Funding Information: This work was supported by National Natural Science Foundation of China (No. 82002006 and 82030058), Organization Department of Beijing Talents Project (2018400685627G339), the Open project of Shanghai Key Laboratory of Forensic Medicine, Key lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science)(KF202206) and Qingchuang Talents Induction Program of Shandong Higher Education Institution (2021 Forensic medicine innovation team).

Conflict of Interests: The authors report no conflicts of interest.

Ethical Approval: All experiments were carried out in accordance with the guidelines and regulations of the Ethical Committee of Shandong First Medical University. The committee assessed and approved this study protocol.

Keywords: age estimation, PiRNA, MiRNA, Blood, Machine Learning