A novel multimodal prediction model based on DNA methylation biomarkers and low-dose computed tomography images for identifying early-stage lung cancer
Jing Zhang, Haohua Yao, Chunliu Lai, Xue Sun, Xiujuan Yang, Shurong Li, Yubiao Guo, Junhang Luo, Zhihua Wen, Kejing Tang
Abstract
<sec><b>Objective</b>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 (<i>SHOX2</i>)/prostaglandin E receptor 4 gene (<i>PTGER4</i>) DNA methylation in plasma, appearance subtype of pulmonary nodules (PNs) and low-dose computed tomography (LDCT) images to distinguish early-stage lung cancers.</sec><sec><b>Methods</b>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 <i>SHOX2</i>/<i>PTGER4</i> DNA methylation and driver gene mutations in lung cancer based on data from The Cancer Genome Atlas (TCGA) portal.</sec><sec><b>Results</b>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 <i>SHOX2</i> 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 <i>PTGER4</i> 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 <i>SHOX2</i>/<i>PTGER4</i> DNA hypermethylation were enriched in <i>TP53</i> mutations.</sec><sec><b>Conclusions</b>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.</sec>