A Predictive Model Using Six Genes DNA Methylation Markers to Identify Individuals With High Risks of High-Grade Squamous Intraepithelial Lesions and Cervical Cancer

利用六个基因DNA甲基化标记构建预测模型,以识别罹患高级别鳞状上皮内病变和宫颈癌的高风险人群

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Abstract

BACKGROUND: Cervical cancer is preceded by low-grade squamous intraepithelial lesions (LSIL) and high-grade squamous intraepithelial lesions (HSIL). Human papillomavirus (HPV) test is a sensitive method for cervical cancer screening, but it is less specific compared with cytological examination, leading to overtreatment and reduced patient compliance. Therefore, new detection methods that can improve the accuracy of cervical cancer screening are needed. METHODS: In the present study, cervical exfoliated cell samples were collected from 228 Chinese individuals, including 114 healthy control individuals, 46 patients with LSIL, 21 patients with HSIL and 47 patients with cervical cancer. The DNA methylation levels of 12 cervical cancer-related genes were detected using quantitative multiplex methylation-specific PCR. All individuals were divided into high- or low-risk groups. Patients with HSIL and cervical cancer were assigned to the high-risk group, whereas healthy controls and patients with LSIL were assigned to the low-risk group. The ability to predict cancer risks was evaluated using ROC curves and a predictive model for cancer risk was constructed by linear regression analysis. RESULTS: The methylation levels were significantly higher for all 12 genes in individuals with cervical cancer or HSIL, compared with those in LSIL or normal group. Family with sequence similarity 19 member A4 (FAM19A4), phosphatase and actin regulator 3 (PHACTR3), somatostatin (SST), Zic family member 1 (ZIC1), paired box 1 (PAX1) and zinc finger protein 671 (ZNF671) were used to construct a predictive model for cancer risk prediction, with a specificity of 89.6% and a sensitivity of 95.0%. CONCLUSION: The present study demonstrated the methylation levels of 12 cervical cancer-related genes were higher in Chinese patients with HSIL or cervical cancer. Also, a predictive model was constructed to distinguish cervical cancer or HSCL from individuals with low risk.

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