A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules
Abstract Background Lung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is...
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doaj-d9895775f7914550b57448575f79dd742021-03-11T12:51:31ZengBMCBMC Cancer1471-24072021-03-0121111110.1186/s12885-021-08002-4A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodulesWenqun Xing0Haibo Sun1Chi Yan2Chengzhi Zhao3Dongqing Wang4Mingming Li5Jie Ma6Department of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer HospitalDepartment of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer HospitalDepartment of Molecular Pathology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer HospitalDepartment of Molecular Pathology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer HospitalDepartment of Molecular Pathology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer HospitalExcellen Medical Technology Co., Ltd.Department of Molecular Pathology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer HospitalAbstract Background Lung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is a clinical challenge. This study aimed to develop a prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant pulmonary nodules from benign PNs. Methods We assessed three DNA methylation biomarkers (PTGER4, RASSF1A, and SHOX2) and clinically-relevant variables in a training cohort of 110 individuals with PNs. Four machine-learning-based prediction models were established and compared, including the K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithms. Variables of the best-performing algorithm (LR) were selected through stepwise use of Akaike’s information criterion (AIC). The constructed prediction model was compared with the methylation biomarkers and the Mayo Clinic model using the non-parametric approach of DeLong et al. with the area under a receiver operator characteristic curve (AUC) analysis. Results A prediction model was finally constructed based on three DNA methylation biomarkers and one radiological characteristic for identifying malignant from benign PNs. The developed prediction model achieved an AUC value of 0.951 in malignant PNs diagnosis, significantly higher than the three DNA methylation biomarkers (0.912, 95% CI:0.843–0.958, p = 0.013) or Mayo Clinic model (0.823, 95% CI:0.739–0.890, p = 0.001). Validation of the prediction model in the testing cohort of 100 subjects with PNs confirmed the diagnostic value. Conclusion We have shown that integrating DNA methylation biomarkers and radiological characteristics could more accurately identify lung cancer in subjects with CT-found PNs. The prediction model developed in our study may provide clinical utility in combination with LDCT to improve the over-all diagnosis of lung cancer.https://doi.org/10.1186/s12885-021-08002-4CTDNA methylationBiomarkersLung cancerPulmonary nodules |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wenqun Xing Haibo Sun Chi Yan Chengzhi Zhao Dongqing Wang Mingming Li Jie Ma |
spellingShingle |
Wenqun Xing Haibo Sun Chi Yan Chengzhi Zhao Dongqing Wang Mingming Li Jie Ma A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules BMC Cancer CT DNA methylation Biomarkers Lung cancer Pulmonary nodules |
author_facet |
Wenqun Xing Haibo Sun Chi Yan Chengzhi Zhao Dongqing Wang Mingming Li Jie Ma |
author_sort |
Wenqun Xing |
title |
A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules |
title_short |
A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules |
title_full |
A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules |
title_fullStr |
A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules |
title_full_unstemmed |
A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules |
title_sort |
prediction model based on dna methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules |
publisher |
BMC |
series |
BMC Cancer |
issn |
1471-2407 |
publishDate |
2021-03-01 |
description |
Abstract Background Lung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is a clinical challenge. This study aimed to develop a prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant pulmonary nodules from benign PNs. Methods We assessed three DNA methylation biomarkers (PTGER4, RASSF1A, and SHOX2) and clinically-relevant variables in a training cohort of 110 individuals with PNs. Four machine-learning-based prediction models were established and compared, including the K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithms. Variables of the best-performing algorithm (LR) were selected through stepwise use of Akaike’s information criterion (AIC). The constructed prediction model was compared with the methylation biomarkers and the Mayo Clinic model using the non-parametric approach of DeLong et al. with the area under a receiver operator characteristic curve (AUC) analysis. Results A prediction model was finally constructed based on three DNA methylation biomarkers and one radiological characteristic for identifying malignant from benign PNs. The developed prediction model achieved an AUC value of 0.951 in malignant PNs diagnosis, significantly higher than the three DNA methylation biomarkers (0.912, 95% CI:0.843–0.958, p = 0.013) or Mayo Clinic model (0.823, 95% CI:0.739–0.890, p = 0.001). Validation of the prediction model in the testing cohort of 100 subjects with PNs confirmed the diagnostic value. Conclusion We have shown that integrating DNA methylation biomarkers and radiological characteristics could more accurately identify lung cancer in subjects with CT-found PNs. The prediction model developed in our study may provide clinical utility in combination with LDCT to improve the over-all diagnosis of lung cancer. |
topic |
CT DNA methylation Biomarkers Lung cancer Pulmonary nodules |
url |
https://doi.org/10.1186/s12885-021-08002-4 |
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