Zhang Jing, Yao Haohua, Lai Chunliu, Sun Xue, Yang Xiujuan, Li Shurong, Guo Yubiao, Luo Junhang, Wen Zhihua, Tang Kejing
Division of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
Department of Urology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Chin J Cancer Res. 2023 Oct 30;35(5):511-525. doi: 10.21147/j.issn.1000-9604.2023.05.08.
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 ()/prostaglandin E receptor 4 gene () DNA methylation in plasma, appearance subtype of pulmonary nodules (PNs) and low-dose computed tomography (LDCT) images to distinguish early-stage lung cancers.
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 / DNA methylation and driver gene mutations in lung cancer based on data from The Cancer Genome Atlas (TCGA) portal.
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 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 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 / DNA hypermethylation were enriched in mutations.
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.
DNA甲基化改变是肺癌发生和免疫信号传导的早期事件。本研究旨在基于血浆中矮小同源盒2基因（）/前列腺素E受体4基因（）的DNA甲基化、肺结节（PNs）的外观亚型和低剂量计算机断层扫描（LDCT）图像开发一种模型，以区分早期肺癌。
我们开发了一种多模态预测模型，训练集包含257名个体。该多模态预测模型的性能在42名受试者的独立验证集中得到进一步验证。此外，我们基于癌症基因组图谱（TCGA）门户的数据，探索了/ DNA甲基化与肺癌驱动基因突变之间的关联。
早期肺癌与良性组之间的甲基化水平存在显著差异。实性结节、混合性磨玻璃密度结节和纯磨玻璃密度结节患者的曲线下面积（AUC）分别为0.693、0.497和0.864，而的AUC分别为0.559、0.739和0.619。新的多模态预测模型在独立验证集中的AUC最高，为0.894，优于梅奥诊所模型（0.519）和基于LDCT的深度学习模型（0.842）。数据库分析表明，/ DNA高甲基化的患者富集于突变。
目前的多模态预测模型可以更有效地将早期肺癌与良性PNs区分开来。基于DNA甲基化和肺癌驱动基因改变的预后指数可能会将患者分为预后良好或不良的组。
