Quantitative survey of multiple cpgs from 5 genes identifies cpg methylation panel discriminating between high- and low-grade cervical intraepithelial neoplasia

Abstract

Background: Studies of methylation biomarkers for cervical cancer often involved only few randomly selected CpGs per candidate gene analyzed by methylation-specific PCR-based methods, with often inconsistent results from different laboratories. We evaluated the role of different CpGs from multiple genes as methylation biomarkers for high-grade cervical intraepithelial neoplasia (CIN). Results: We applied a mass spectrometry-based platform to survey the quantitative methylation levels of 34 CpG units from SOX1, PAX1, NKX6-1, LMX1A, and ONECUT1 genes in 100 cervical formalin-fixed paraffin-embedded (FFPE) tissues. We then used nonparametric statistics and Random Forest algorithm to rank significant CpG methylations and support vector machine with 10-fold cross validation and 200 times bootstrap resampling to build a predictive model separating CIN II/III from CIN I/normal subjects. We found only select CpG units showed significant differences in methylation between CIN II/III and CIN I/normal groups, while mean methylation levels per gene were similar between the two groups for each gene except PAX1. An optimal classification model involving five CpG units from SOX1, PAX1, NKX6-1, and LMX1A achieved 81.2% specificity, 80.4% sensitivity, and 80.8% accuracy. Conclusions: Our study suggested that during CIN development, the methylation of CpGs within CpG islands is not uniform, with varying degrees of significance as biomarkers. Our study emphasizes the importance of not only methylated marker genes but also specific CpGs for identifying high-grade CINs. The 5-CpG classification model provides a promising biomarker panel for the early detection of cervical cancer.