Selection and validation of a novel set of specific differential methylation markers and construction of a random forest prediction model for the accurate tissue origin identifications of body fluids involving young and middle-aged group of Chinese Han population

The identification of tissue origin of body fluid is helpful to the determination of the case nature and the reproduction of the case process. It has been confirmed that tissue-specific differential methylation markers could be used to identify the tissue origins of different body fluids. To select suitable tissue-specific differential methylation markers and establish the efficient typing system which could be applied to the identifications of body fluids in forensic cases involving Chinese Han individuals of young and middle-aged group, a total of 125 body fluids (venous blood, semen, vaginal fluid, saliva, and menstrual blood) were collected from healthy Chinese Han volunteers aged 20-45 years old. After genome-wide explorations of DNA methylation patterns in these five kinds of body fluids based on the Illumina Infinium Methylation EPIC BeadChip, 15 novel body fluid-specific differential CpGs were selected and verified based on the pyrosequencing method. And these identification efficiencies for target body fluids were verified by ROC curves. The pyrosequencing results indicated that the average methylation rates of nine CpGs were consistent with those of DNA methylation chip detection results, and the other five CpGs (except for cg12152558) were still helpful for the tissue origin identifications of target body fluids. Finally, a random forest classification prediction model based on these 14 CpGs was constructed to successfully identify five kinds of body fluids, and the tested accuracy rates all reached 100%.

Automated summary

The identification of tissue origin in body fluids is crucial in determining the nature and progression of forensic cases. Through genome-wide exploration of DNA methylation patterns in various body fluids, 15 novel methylation markers were identified and validated using pyrosequencing. These markers showed high efficiency in identifying the tissue origins of target body fluids, leading to the construction of a random forest classification model with 100% accuracy in identifying the five types of body fluids.