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Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning.

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.

SUBMITTER: Qi J 

PROVIDER: S-EPMC8409309 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning.

Qi Jian J   Hong Bo B   Tao Rui R   Sun Ruifang R   Zhang Huanhu H   Zhang Xiaopeng X   Ji Jie J   Wang Shujie S   Liu Yanzhe Y   Deng Qingmei Q   Wang Hongzhi H   Zhao Dahai D   Nie Jinfu J  

Cancer science 20210721 9

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 spe  ...[more]

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