A Triage Model for HR-HPV Positive Women Based on Methylation of Human and HPV Genes.

The aim of this study is to develop a machine learning triage model for HR-HPV positive women based on methylation patterns of human and HPV genes to predict the risk of CIN2 +, and to compare its performance with traditional detection techniques. A total of 512 eligible women participated in the study, with exfoliated cervical cell specimens collected for methylation analysis of both human and HPV genes. Highly methylated gene fragments related to cervical cancer were identified through high density, high association, high MHL (Methylation Haplotype Load) screening and Logistic regression. The samples were divided into a training set and a validation set at a ratio of 7:3, and a machine learning triage model was constructed using CIN2+ as the outcome. Among 512 HR-HPV positive women, 174 (34.0%) had CIN2 +. To construct the random forest model, 10 high methylation human gene fragments were identified from a pool of 45 genes, along with HPV 16, 18, and 52 L2 genes. In the validation set, the random forest model identified 45 samples as positive (38 were true positives) and 112 samples as negative (96 were true negatives). The model achieved an AUC of 0.9, and DeLong test confirmed its performance was significantly superior (p < 0.001) to HPV 16/18 genotyping and cytology testing. The random forest model constructed based on methylation patterns of human and HPV genes demonstrated strong predictive efficacy for CIN2+ in HR-HPV positive women, and may optimize the triage method in cervical cancer screening.