Suyu Li , Jing Liu , Wei Xu , Shuyuan Zhang , Mengyao Zhao , Lu Miao , Minxiao Hui , Yuan Wang , Yiping Hou , Bin Cong , Zheng Wang
{"title":"基于 14 种 microRNA 的多类支持向量机分类模型用于法医体液鉴定","authors":"Suyu Li , Jing Liu , Wei Xu , Shuyuan Zhang , Mengyao Zhao , Lu Miao , Minxiao Hui , Yuan Wang , Yiping Hou , Bin Cong , Zheng Wang","doi":"10.1016/j.fsigen.2024.103180","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50435,"journal":{"name":"Forensic Science International-Genetics","volume":"75 ","pages":"Article 103180"},"PeriodicalIF":3.2000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi-class support vector machine classification model based on 14 microRNAs for forensic body fluid identification\",\"authors\":\"Suyu Li , Jing Liu , Wei Xu , Shuyuan Zhang , Mengyao Zhao , Lu Miao , Minxiao Hui , Yuan Wang , Yiping Hou , Bin Cong , Zheng Wang\",\"doi\":\"10.1016/j.fsigen.2024.103180\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":50435,\"journal\":{\"name\":\"Forensic Science International-Genetics\",\"volume\":\"75 \",\"pages\":\"Article 103180\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Forensic Science International-Genetics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1872497324001765\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forensic Science International-Genetics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1872497324001765","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
A multi-class support vector machine classification model based on 14 microRNAs for forensic body fluid identification
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.
期刊介绍:
Forensic Science International: Genetics is the premier journal in the field of Forensic Genetics. This branch of Forensic Science can be defined as the application of genetics to human and non-human material (in the sense of a science with the purpose of studying inherited characteristics for the analysis of inter- and intra-specific variations in populations) for the resolution of legal conflicts.
The scope of the journal includes:
Forensic applications of human polymorphism.
Testing of paternity and other family relationships, immigration cases, typing of biological stains and tissues from criminal casework, identification of human remains by DNA testing methodologies.
Description of human polymorphisms of forensic interest, with special interest in DNA polymorphisms.
Autosomal DNA polymorphisms, mini- and microsatellites (or short tandem repeats, STRs), single nucleotide polymorphisms (SNPs), X and Y chromosome polymorphisms, mtDNA polymorphisms, and any other type of DNA variation with potential forensic applications.
Non-human DNA polymorphisms for crime scene investigation.
Population genetics of human polymorphisms of forensic interest.
Population data, especially from DNA polymorphisms of interest for the solution of forensic problems.
DNA typing methodologies and strategies.
Biostatistical methods in forensic genetics.
Evaluation of DNA evidence in forensic problems (such as paternity or immigration cases, criminal casework, identification), classical and new statistical approaches.
Standards in forensic genetics.
Recommendations of regulatory bodies concerning methods, markers, interpretation or strategies or proposals for procedural or technical standards.
Quality control.
Quality control and quality assurance strategies, proficiency testing for DNA typing methodologies.
Criminal DNA databases.
Technical, legal and statistical issues.
General ethical and legal issues related to forensic genetics.