Identification of Peripheral Blood and Menstrual Blood Based on the Expression Level of MicroRNAs and Discriminant Analysis.
10.12116/j.issn.1004-5619.2020.04.016
- Author:
Hong Xia HE
1
;
An Quan JI
2
;
Na HAN
3
;
Yi Xia ZHAO
2
;
Sheng HU
2
;
Qing Lan KONG
4
;
Yao LIU
1
;
Qi Fan SUN
2
Author Information
1. School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China.
2. Key Laboratory of Forensic Genetics, Ministry of Public Security, National Engineering Laboratory for Forensic Science, Institute of Forensic Science, Beijing 100038, China.
3. State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
4. Faculty of Mathematics and Statistics, Zaozhuang University, Zaozhuang 277160, Shandong Province, China.
- Publication Type:Journal Article
- Keywords:
forensic genetics;
microRNAs;
models, statistical;
body fluids;
menstrual blood;
peripheral blood;
identification
- MeSH:
Body Fluids;
Discriminant Analysis;
Female;
Forensic Genetics;
MicroRNAs/genetics*;
Semen
- From:
Journal of Forensic Medicine
2020;36(4):514-518
- CountryChina
- Language:English
-
Abstract:
Objective To construct a discriminant analysis model based on the differential expression of multiple microRNAs （miRNAs） in two kinds of blood samples （peripheral blood and menstrual blood） and three non-blood samples （saliva, semen and vaginal secretion）, to form an identification solution for peripheral blood and menstrual blood. Methods Six kinds of miRNA （miR-451a, miR-144-3p, miR-144-5p, miR-214-3p, miR-203-3p and miR-205-5p） were selected from literature, the samples of five kinds of body fluids commonly seen in forensic practice （peripheral blood, menstrual blood, saliva, semen, vaginal secretion） were collected, then the samples were divided into training set and testing set and detected by SYBR Green real-time qPCR. A discriminant analysis model was set up based on the expression data of training set and the expression data of testing set was used to examine the accuracy of the model. Results A discriminant analysis statistical model that could distinguish blood samples from non-blood samples and distinguish peripheral blood samples from menstrual blood samples at the same time was successfully constructed. The identification accuracy of the model was over 99%. Conclusion This study provides a scientific and accurate identification strategy for forensic fluid identification of peripheral blood and menstrual blood samples and could be used in forensic practice.
