基于人DNA甲基化的宫颈癌及癌前病变机器学习预测模型构建与初步验证  

Construction and preliminary validation of machine learning predictive models for cervical cancer screening based on human DNA methylation

作　　者：杨源 周航 王亚坤 戴钰 皮若及 章华 黄紫玥 吴婷[1] 杨敬红 陈汶 Yang Yuan;Zhou Hang;Wang Yakun;Dai Yu;Pi Ruoji;Zhang Hua;Huang Ziyue;Wu Ting;Yang Jinghong;Chen Wen(National Institute of Diagnostics and Vaccine Development in Infectious Diseases,School of Public Health,Xiamen University,Xiamen 361102,China;Department of Epidemiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100021,China;Department of Disease Control,Beijing Traditional Chinese Medicine Hospital Affiliated to Capital Medical University,Beijing 100010,China;Department of Maternal and Child Health Management,Mianyang Maternal and Child Health Care Hospital,Mianyang 621000,China)

机构地区：[1]厦门大学公共卫生学院、国家传染病诊断试剂与疫苗工程技术研究中心,厦门361102 [2]国家癌症中心、国家肿瘤临床医学研究中心、中国医学科学院北京协和医学院肿瘤医院流行病学研究室,北京100021 [3]首都医科大学附属北京中医医院疾控处,北京100010 [4]绵阳市妇幼保健院妇幼健康管理科,绵阳621000

出　　处：《中华肿瘤杂志》2025年第2期193-200,共8页Chinese Journal of Oncology

基　　金：国家自然科学基金(81973136);中国医学科学院医学与健康科技创新工程(2021-I2M-1-004);四川省科技计划应用基础研究项目(21YYJC3520)。

摘　　要：目的利用人基因的甲基化特征,构建预测宫颈癌及癌前病变的机器学习预测模型。方法对2014年4月至2015年3月来自中国医学科学院肿瘤医院、天津市中心妇产科医院、河南省新密妇幼保健院、四川大学华西第二附属医院和山西长治医学院附属和平医院的224例宫颈脱落细胞标本进行人DNA甲基化检测,通过CpG高密度、高关联、高甲基化基因片段筛选和LASSO回归算法,筛选出与宫颈癌病变相关的高甲基化基因片段。以宫颈上皮内瘤变2级(CIN2)及以上病变为研究结局,以144例门诊患者标本为训练集,构建随机森林(RF)、朴素贝叶斯(NB)和支持向量机(SVM)3种机器学习预测模型,以80例参与宫颈癌筛查项目女性的宫颈脱落细胞标本为验证集对预测模型进行验证。以组织学诊断结果为金标准,比较3种机器学习预测模型与HPV检测和细胞学诊断对CIN2及以上病变的检出效能。结果训练集144例中,HPV阳性34例,阳性率为23.61%。细胞学诊断为无上皮内病变或恶性细胞(NILM)37例,不能明确意义的非典型鳞状上皮细胞(ASC-US)及以上病变107例。组织学诊断为未见宫颈上皮内病变或宫颈良性病变28例,CIN131例,CIN218例,CIN331例,鳞癌36例。从45个基因中筛选出7个高甲基化基因片段,构建了RF、NB和SVM机器学习预测模型。验证集80例中,HPV阳性28例,阳性率为35.00%。细胞学诊断为NILM 65例,ASC-US及以上病变15例。组织学诊断为未见宫颈上皮内病变或宫颈良性病变39例,CIN110例,CIN210例,CIN311例,鳞癌10例。在验证集中,RF模型、NB模型、SVM模型、HPV检测和细胞学诊断CIN2及以上病变的受试者工作特征曲线下面积(AUC)分别为0.90、0.88、0.82、0.68和0.45。DeLong检验显示,RF模型、NB模型和SVM模型的AUC差异无统计学意义(两两比较均P>0.05),RF模型、NB模型的AUC高于HPV检测(均P<0.01),RF模型、NB模型、SVM模型的AUC高于细胞学诊断(均P<0.01)�Objective Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences,Tianjin Central Hospital of Gynecology Obstetrics,Xinmi Maternal and Child Health Hospital of Henan Province,West China Second Affiliated Hospital of Sichuan University,and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015.The hypermethylated gene fragments related to cervical cancer were selected by high-density,high-association,and hypermethylated gene fragment screening and the LASSO regression algorithm.Taking cervical intraepithelial neoplasia grade 2(CIN2)or more severe lesions as the research outcome,machine learning predictive models based on the random forest(RF),naive Bayes(NB),and support vector machine(SVM)algorithm,respectively,were constructed.A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models.Using histological diagnosis results as the gold standard,the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus(HPV)detection and cytological diagnosis.Results In the training set of 144 cases,there were 34 cases of HPV positivity,with a positive rate of 23.61%.Cytologically,there were 37 cases diagnosed as no intraepithelial lesion or malignancy(NILM),and 107 cases diagnosed as atypical squamous cells of undetermined significance(ASC-US)or above.Histologically,there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions,31 cases of CIN1,18 cases of CIN2,31 cases of CIN3,and 36 cases of squamous cell carcinoma.Seven hypermethylated gene

关 键 词：子宫颈肿瘤 甲基化 甲基化单倍型负荷 机器学习 朴素贝叶斯模型 

分 类 号：R73[医药卫生—肿瘤]