Circular RNA as a Potential Biomarker for Forensic Age Prediction

Junyan Wang; Chunyan Wang; Yangyan Wei; Yanhao Zhao; Can Wang; Chaolong Lu; Jin Feng; Shujin Li; Bin Cong

doi:10.3389/fgene.2022.825443

Circular RNA as a Potential Biomarker for Forensic Age Prediction

Front Genet. 2022 Feb 7:13:825443. doi: 10.3389/fgene.2022.825443. eCollection 2022.

Authors

Junyan Wang¹, Chunyan Wang², Yangyan Wei¹, Yanhao Zhao¹, Can Wang¹, Chaolong Lu¹, Jin Feng², Shujin Li¹, Bin Cong¹

Affiliations

¹ Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, College of Forensic Medicine, Chinese Academy of Medical Sciences, Hebei Medical University, Shijiazhuang, China.
² Physical Examination Center of Shijiazhuang First Hospital, Shijiazhuang, China.

Abstract

In forensic science, accurate estimation of the age of a victim or suspect can facilitate the investigators to narrow a search and aid in solving a crime. Aging is a complex process associated with various molecular regulations on DNA or RNA levels. Recent studies have shown that circular RNAs (circRNAs) upregulate globally during aging in multiple organisms such as mice and C.elegans because of their ability to resist degradation by exoribonucleases. In the current study, we attempted to investigate circRNAs' potential capability of age prediction. Here, we identified more than 40,000 circRNAs in the blood of thirteen Chinese unrelated healthy individuals with ages of 20-62 years according to their circRNA-seq profiles. Three methods were applied to select age-related circRNA candidates including the false discovery rate, lasso regression, and support vector machine. The analysis uncovered a strong bias for circRNA upregulation during aging in human blood. A total of 28 circRNAs were chosen for further validation in 30 healthy unrelated subjects by RT-qPCR, and finally, 5 age-related circRNAs were chosen for final age prediction models using 100 samples of 19-73 years old. Several different algorithms including multivariate linear regression (MLR), regression tree, bagging regression, random forest regression (RFR), and support vector regression (SVR) were compared based on root mean square error (RMSE) and mean average error (MAE) values. Among five modeling methods, regression tree and RFR performed better than the others with MAE values of 8.767 years (S.rho = 0.6983) and 9.126 years (S.rho = 0.660), respectively. Sex effect analysis showed age prediction models significantly yielded smaller prediction MAE values for males than females (MAE = 6.133 years for males, while 10.923 years for females in the regression tree model). In the current study, we first used circRNAs as additional novel age-related biomarkers for developing forensic age estimation models. We propose that the use of circRNAs to obtain additional clues for forensic investigations and serve as aging indicators for age prediction would become a promising field of interest.

Keywords: age prediction; biomarkers; circular RNA; forensic genetics; machine learning.