摘要Objective:DNA methylation alterations are early events in carcinogenesis and immune signalling in lung cancer.This study aimed to develop a model based on short stature homeobox 2 gene(SHOX2)/prostaglandin E receptor 4 gene(PTGER4)DNA methylation in plasma,appearance subtype of pulmonary nodules(PNs)and low-dose computed tomography(LDCT)images to distinguish early-stage lung cancers.Methods:We developed a multimodal prediction model with a training set of 257 individuals.The performance of the multimodal prediction model was further validated in an independent validation set of 42 subjects.In addition,we explored the association between SHOX2/PTGER4 DNA methylation and driver gene mutations in lung cancer based on data from The Cancer Genome Atlas(TCGA)portal.Results:There were significant differences between the early-stage lung cancers and benign groups in the methylation levels.The area under a receiver operator characteristic curve(AUC)of SHOX2 in patients with solid nodules,mixed ground-glass opacity nodules and pure ground-glass opacity nodules were 0.693,0.497 and 0.864,respectively,while the AUCs of PTGER4 were 0.559,0.739 and 0.619,respectively.With the highest AUC of 0.894,the novel multimodal prediction model outperformed the Mayo Clinic model(0.519)and LDCT-based deep learning model(0.842)in the independent validation set.Database analysis demonstrated that patients with SHOX2/PTGER4 DNA hypermethylation were enriched in TP53 mutations.Conclusions:The present multimodal prediction model could more efficiently distinguish early-stage lung cancer from benign PNs.A prognostic index based on DNA methylation and lung cancer driver gene alterations may separate the patients into groups with good or poor prognosis.