PMID- 33691657 OWN - NLM STAT- MEDLINE DCOM- 20210503 LR - 20210503 IS - 1471-2407 (Electronic) IS - 1471-2407 (Linking) VI - 21 IP - 1 DP - 2021 Mar 10 TI - A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules. PG - 263 LID - 10.1186/s12885-021-08002-4 [doi] LID - 263 AB - 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. FAU - Xing, Wenqun AU - Xing W AD - Department of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China. fy20090604@qq.com. FAU - Sun, Haibo AU - Sun H AD - Department of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China. FAU - Yan, Chi AU - Yan C AD - Department of Molecular Pathology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, No 127, Dongming Road, Zhengzhou, 450008, Henan, China. AD - Henan Key Laboratory of Molecular Pathology, Zhengzhou, Henan, China. FAU - Zhao, Chengzhi AU - Zhao C AD - Department of Molecular Pathology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, No 127, Dongming Road, Zhengzhou, 450008, Henan, China. AD - Henan Key Laboratory of Molecular Pathology, Zhengzhou, Henan, China. FAU - Wang, Dongqing AU - Wang D AD - Department of Molecular Pathology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, No 127, Dongming Road, Zhengzhou, 450008, Henan, China. AD - Henan Key Laboratory of Molecular Pathology, Zhengzhou, Henan, China. FAU - Li, Mingming AU - Li M AD - Excellen Medical Technology Co., Ltd., Beijing, China. FAU - Ma, Jie AU - Ma J AUID- ORCID: 0000-0001-5346-8457 AD - Department of Molecular Pathology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, No 127, Dongming Road, Zhengzhou, 450008, Henan, China. majie_fzbl@163.com. AD - Henan Key Laboratory of Molecular Pathology, Zhengzhou, Henan, China. majie_fzbl@163.com. LA - eng GR - 161100311500/Major Science and Technology Projects of Henan/ PT - Journal Article PT - Observational Study DEP - 20210310 PL - England TA - BMC Cancer JT - BMC cancer JID - 100967800 RN - 0 (Biomarkers, Tumor) RN - 0 (Homeodomain Proteins) RN - 0 (PTGER4 protein, human) RN - 0 (RASSF1 protein, human) RN - 0 (Receptors, Prostaglandin E, EP4 Subtype) RN - 0 (SHOX2 protein, human) RN - 0 (Tumor Suppressor Proteins) SB - IM MH - Adult MH - Aged MH - Biomarkers, Tumor/*genetics MH - Biopsy MH - Case-Control Studies MH - *DNA Methylation MH - Diagnosis, Differential MH - Feasibility Studies MH - Female MH - Homeodomain Proteins/blood/genetics MH - Humans MH - Lung/diagnostic imaging/pathology MH - Lung Neoplasms/blood/*diagnosis/genetics/pathology MH - Male MH - Middle Aged MH - Models, Genetic MH - Multiple Pulmonary Nodules/blood/*diagnosis/genetics/pathology MH - ROC Curve MH - Receptors, Prostaglandin E, EP4 Subtype/blood/genetics MH - *Tomography, X-Ray Computed MH - Tumor Suppressor Proteins/blood/genetics PMC - PMC7944594 OTO - NOTNLM OT - Biomarkers OT - CT OT - DNA methylation OT - Lung cancer OT - Pulmonary nodules COIS- ML is an employee of Excellen Medical Technology Co., Ltd. All other authors have no conflicts of interest to declare. EDAT- 2021/03/12 06:00 MHDA- 2021/05/04 06:00 PMCR- 2021/03/10 CRDT- 2021/03/11 05:46 PHST- 2020/09/29 00:00 [received] PHST- 2021/03/02 00:00 [accepted] PHST- 2021/03/11 05:46 [entrez] PHST- 2021/03/12 06:00 [pubmed] PHST- 2021/05/04 06:00 [medline] PHST- 2021/03/10 00:00 [pmc-release] AID - 10.1186/s12885-021-08002-4 [pii] AID - 8002 [pii] AID - 10.1186/s12885-021-08002-4 [doi] PST - epublish SO - BMC Cancer. 2021 Mar 10;21(1):263. doi: 10.1186/s12885-021-08002-4.