ABSTRACT
OBJECTIVE: Body fluid mixtures are complex biological samples that frequently occur in crime scenes, and can provide important clues for criminal case analysis. DNA methylation assay has been applied in the identification of human body fluids, and has exhibited excellent performance in predicting single-source body fluids. The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification, and accurately predict the mixture samples. In addition, the value of DNA methylation in the prediction of body fluid mixtures was further explored. METHODS: In the present study, 420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system. Each kind of body fluid sample presented the specific methylation profiles of the 10 markers. RESULTS: Significant differences in methylation levels were observed between the mixtures and single body fluids. For all kinds of mixtures, the Spearman's correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions (1:20, 1:10, 1:5, 1:1, 5:1, 10:1 and 20:1). Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components, based on the methylation levels of 10 markers. For the mixture prediction, Model-1 presented outstanding prediction accuracy, which reached up to 99.3% in 427 training samples, and had a remarkable accuracy of 100% in 243 independent test samples. For the mixture proportion prediction, Model-2 demonstrated an excellent accuracy of 98.8% in 252 training samples, and 98.2% in 168 independent test samples. The total prediction accuracy reached 99.3% for body fluid mixtures and 98.6% for the mixture proportions. CONCLUSION: These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.
ABSTRACT
OBJECTIVE: To construct and verificate an RNA-binding protein (RBP)-associated prognostic model for gliomas using integrated bioinformatics analysis. METHODS: RNA-sequencing and clinic pathological data of glioma patients from The Cancer Genome Atlas (TCGA) database and the Chinese Glioma Genome Atlas database (CGGA) were downloaded. The aberrantly expressed RBPs were investigated between gliomas and normal samples in TCGA database. We then identified prognosis related hub genes and constructed a prognostic model. This model was further validated in the CGGA-693 and CGGA-325 cohorts. RESULTS: Totally 174 differently expressed genes-encoded RBPs were identified, containing 85 down-regulated and 89 up-regulated genes. We identified five genes-encoded RBPs (ERI1, RPS2, BRCA1, NXT1, and TRIM21) as prognosis related key genes and constructed a prognostic model. Overall survival (OS) analysis revealed that the patients in the high-risk subgroup based on the model were worse than those in the low-risk subgroup. The area under the receiver operator characteristic curve (AUC) of the prognostic model was 0.836 in the TCGA dataset and 0.708 in the CGGA-693 dataset, demonstrating a favorable prognostic model. Survival analyses of the five RBPs in the CGGA-325 cohort validated the findings. A nomogram was constructed based on the five genes and validated in the TCGA cohort, confirming a promising discriminating ability for gliomas. CONCLUSION: The prognostic model of the five RBPs might serve as an independent prognostic algorithm for gliomas.
