Prediction model for malignant pulmonary nodules based on cfMeDIP‐seq and machine learning. Issue 9 (21st July 2021)

• Record Type: Journal Article
• Authors: Qi, Jian; Hong, Bo; Tao, Rui; Sun, Ruifang; Zhang, Huanhu; Zhang, Xiaopeng; Ji, Jie; Wang, Shujie; Liu, Yanzhe; Deng, Qingmei; Wang, Hongzhi; Zhao, Dahai; Nie, Jinfu
• Is Part Of: Cancer science. Volume 112:Issue 9(2021)
• Page Start: 3918
• Page End: 3923
• Publication Date: 2021-07-21
• Subjects: cfDNA methylation -- cfMeDIP‐seq -- lung cancer -- machine learning -- pulmonary nodule; Cancer -- Periodicals; Neoplasms -- Periodicals; Research -- Periodicals; Electronic journals; 616.994005
• DOI: 10.1111/cas.15052
• Languages: English
• ISSNs: 1347-9032

Abstract

Abstract: Cell‐free methylated DNA immunoprecipitation and high‐throughput sequencing (cfMeDIP‐seq) is a new bisulfite‐free technique, which can detect the whole‐genome methylation of blood cell‐free DNA (cfDNA). Using this technique, we identified differentially methylated regions (DMR) of cfDNA between lung tumors and normal controls. Based on the top 300 DMR, we built a random forest prediction model, which was able to distinguish malignant lung tumors from normal controls with high sensitivity and specificity of 91.0% and 93.3% (AUROC curve of 0.963). In summary, we reported a non–invasive prediction model that had good ability to distinguish malignant pulmonary nodules. Abstract : This study reported the whole‐genome methylation profiling of plasma cfDNA of patients with pulmonary nodules by cfMeDIP‐seq. A random forest model was used to distinguish pulmonary malignant tumors from normal controls, based on the whole‐genome methylation profiling.