A multi-class support vector machine classification model based on 14 microRNAs for forensic body fluid identification.

Journal: Forensic science international. Genetics
PMID:

Abstract

MicroRNAs (miRNAs) are promising biomarkers for forensic body fluid identification owing to their small size, stability against degradation, and differential expression patterns. However, the expression of most body fluid-miRNAs is relative (differentially expressed in certain body fluids) rather than absolute (exclusively expressed in a specific body fluid). Moreover, different body fluids contain heterogeneous cell types, complicating their identification. Therefore, appropriate normalization strategies to eliminate non-biological variations and robust models to interpret expression levels accurately are necessary prerequisites for applying miRNAs in body fluid identification. In this study, the expression stability of six candidate reference genes (RGs) across five body fluids was validated using geNorm, NormFinder, BestKeeper and RankAggreg, and the most suitable combination of RGs (hsa-miR-484 and hsa-miR-191-5p) was identified under our experimental conditions. Subsequently, we systematically evaluated the expression patterns of the 28 most promising body fluid-specific miRNA markers using TaqMan RT-qPCR and selected the optimal combination of markers (12 miRNAs) to establish a multi-class support vector machine (MSVM) classification model. An independent test set (60 samples) was used to validate the accuracy of the proposed classification model, while an additional 30 casework samples were used to assess its robustness. The MSVM model accurately predicted the body fluid origin for almost all (59/60) single-source samples. Moreover, this model demonstrated the capability to identify aged forensic samples and to predict the primary components of mixed stains to a certain extent. In summary, this study presented a miRNA-based MSVM classification model for forensic body fluid identification using the qPCR platform. However, extensive validation, especially inter-laboratory collaborative exercises, is necessary before miRNA can be routinely applied in forensic identification practice.

Authors

  • Suyu Li
    Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China.
  • Jing Liu
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Wei Xu
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023 China.
  • Shuyuan Zhang
  • Mengyao Zhao
    Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China.
  • Lu Miao
    Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China; Criminal Investigation Detachment of Huainan Public Security Bureau, Huainan 232000, China.
  • Minxiao Hui
    Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China.
  • Yuan Wang
    State Key Laboratory of Soil and Sustainable Agriculture, Changshu National Agro-Ecosystem Observation and Research Station, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China.
  • Yiping Hou
    Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China.
  • Bin Cong
    College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Shijiazhuang 050017, China. Electronic address: hbydbincong@126.com.
  • Zheng Wang
    Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan 430060, China.