Research Of Body Fluid Identification Based On MiRNA High-throughput Sequencing And Bioinformatics

Objective:Accurate identification of the types of body fluids left at a crime scene is an indispensable part of improving the judicial chain of evidence.Micro RNAs(miRNAs)have become recognized as ideal molecular markers for the identification of body fluids in forensic science due to their short length,stability and strong tissue specificity.This study aims to conduct a systematic study on the screening and verification of five common body fluids miRNA markers in forensic medicine by combining high-throughput sequencing with bioinformatics methods,and further establishes an effective analytical method that can accurately distinguish five common body fluids in forensic science.Methods:1、In this study,BGISEQ-500 sequencing platform was used to sequence small RNAs in five types of common body fluids(including peripheral blood,menstrual blood,semen,saliva,vaginal secretions)in forensic science;2、The specific miRNA markers of five common body fluids in forensic medicine were screened by bioinformatics methods such as differential analysis and significant enrichment analysis;3、The candidate miRNAs markers were screened for large-sample validation using RT-q PCR4、The miRNA molecules with better discrimination effect were combined with the other four miRNA molecules(miR-451a,miR-144-5p,miR-203a-3p,miR-891a-5p)screened and verified by our laboratory in the early stage.Furthermore,the expression data of the above markers in 100 samples of five types of body fluids were analyzed based on seven machine learning algorithms(KNN,NN,SVM,LDA,RF,LOG,KDE)and the body fluid prediction model with the best discrimination effect was finally selected.Results:A total of 20 samples of five types of body fluids were sequenced successfully.The total sequencing reads for all samples were between 2.37×10~7and 4.46×10~7,and the Q20 were above 98%.A total of 63 differentially expressed miRNAs were screened by differential expression analysis of sequencing data;GO and KEGG biological function enrichment analysis was performed on the target genes of differentially expressed miRNAs,and 4 body fluid-specific miRNA markers with verification significance were further screened from 63 differentially expressed miRNAs;Four candidate miRNA markers were verified by real-time fluorescence quantitative PCR,and miR-223-3p was finally selected as a saliva-specific marker.Among the 169 samples of the five types of body fluids(22 peripheral blood,23menstrual blood,25 semen,47 saliva and 52 vaginal secretions),the expression level of miR-223-3p in saliva was the highest.Nonparametric t-test results showed that the expression of miR-223-3p in saliva was significantly different from that of any of the other four body fluids,and the p values were all less than 0.0001.The appropriate classification model was the kernel density estimation for all the seven algorithmic ones.The analysis strategy for the identification of body fluids based on five miRNA markers(miR-223-3p,miR-451a,miR-144-5p,miR-203a-3p,miR-891a-5p)and the kernel density estimation model could distinguish five body fluids with 100%accuracy.Conclusion:In this study,small RNA sequencing was performed on five types of body fluids(including peripheral blood,menstrual blood,semen,saliva and vaginal secretions)by using the BGISEQ-500 sequencing platform,and the specific miRNA markers in body fluids were screened by bioinformatics methods such as differential analysis and significant enrichment analysis.Through RT-q PCR validation of 169 samples,we confirmed that miR-223-3p can be used as a saliva-specific marker.In addition,this study combined miR-223-3p with other four miRNA molecules(miR-451a,miR-144-5p,miR-203a-3p,miR-891a-5p)that had been previously screened and verified in our laboratory,and seven body fluid prediction models based on machine learning algorithm were constructed and verified.Finally,we established an analytical strategy for body fluid identification using KDE with the five miRNA molecules,yielding an accuracy of 100%.This study not only made a major breakthrough in solving the problem of identifying saliva and vaginal secretions,but also further improved the laboratory’s work on distinguishing five common forensic body fluids.