摘要
Non-small-cell lung cancer （NSCLC）, the most common type of lung cancer accounting for 85% of the cases, is often diagnosed at advanced stages owing to the lack of efficient early diagnostic tools. 5-Hydroxymetbylcytosine （ShmC） signatures in circulating cell-free DNA （cfDNA） that carries the cancer-specific epigenetic patterns may represent the valuable biomarkers for discriminat- ing tumor and healthy individuals, and thus could be potentially useful for NSCLC diagnosis. Here, we employed a sensitive and reliable method to map genome-wide 5hmC in the cfDNA of Chinese NSCLC patients and detected a significant 5hmC gain in both the gene bodies and promoter regions in the blood samples from tumor patients compared with healthy controls. Specifically, we identi- fied six potential biomarkers from 66 patients and 67 healthy controls （mean decrease accuracy 〉 3.2, P 〈 3.68E-19） using machine-learning-based tumor classifiers with high accuracy. Thus, the unique signature of 5hmC in tumor patient＇s cfDNA identified in our study may provide valuable information in facilitating the development of new diagnostic and therapeutic modalities for NSCLC.
Non-small-cell lung cancer （NSCLC）, the most common type of lung cancer accounting for 85% of the cases, is often diagnosed at advanced stages owing to the lack of efficient early diagnostic tools. 5-Hydroxymetbylcytosine （ShmC） signatures in circulating cell-free DNA （cfDNA） that carries the cancer-specific epigenetic patterns may represent the valuable biomarkers for discriminat- ing tumor and healthy individuals, and thus could be potentially useful for NSCLC diagnosis. Here, we employed a sensitive and reliable method to map genome-wide 5hmC in the cfDNA of Chinese NSCLC patients and detected a significant 5hmC gain in both the gene bodies and promoter regions in the blood samples from tumor patients compared with healthy controls. Specifically, we identi- fied six potential biomarkers from 66 patients and 67 healthy controls （mean decrease accuracy 〉 3.2, P 〈 3.68E-19） using machine-learning-based tumor classifiers with high accuracy. Thus, the unique signature of 5hmC in tumor patient＇s cfDNA identified in our study may provide valuable information in facilitating the development of new diagnostic and therapeutic modalities for NSCLC.
基金
supported by grants from the Ministry of Science and Technology of China (Grant No. 2016YFC0900300)
National Natural Science Foundation of China (Grant Nos. 31670895, U1504831, 31430022, 31670824, 31400672, and 81703598)
Major Science and Technology Project of Henan Province (Grant No. 161100310100)
Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB14030300)
Foundation for University Young Key Teacher of Henan Province (Grant No. 2015GGJS-162)
Science and Technology Project of Henan Province (Grant No.162102310199), China
