J Cancer 2019; 10(21):5090-5098. doi:10.7150/jca.30528 This issue Cite
1. Department of Occupational Health and Occupational Disease, College of Public Health, Zhengzhou University, Zhengzhou, China
2. The Key Laboratory of Nanomedicine and Health Inspection of Zhengzhou, Zhengzhou, China
3. Department of Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, China
4. Department of Thoracic Surgery, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, China
5. Department of Respiratory Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
6. Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
7. Department of Sanitary Chemistry, College of Public Health, Zhengzhou University, Zhengzhou, China
8. Department of Health Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, China
9. Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
Aim: Small single-stranded non-coding RNAs (miRNAs) play an important role in carcinogenesis through degrading target mRNAs. However, the diagnostic value of miRNAs was not explored in lung cancers. In this study, a support-vector-machine (SVM) model for diagnosis of lung cancer was established based on plasma miRNAs biomarkers, clinical symptoms and epidemiology material.
Methods: The expressions of plasma miRNA were examined with SYBR Green-based quantitative real-time PCR.
Results: We identified that the expressions of 10 plasma miRNAs (miR-21, miR-20a, miR-210, miR-145, miR-126, miR-223, miR-197, miR-30a, miR-30d, miR-25), smoking status, fever, cough, chest pain or tightness, bloody phlegm, haemoptysis, were significantly different between lung cancer and control groups (P<0.05). The accuracies of the combined SVM, miRNAs SVM, symptom SVM, combined Fisher, miRNAs Fisher and symptom Fisher were 96.34%, 80.49%, 84.15%, 84.15%, 75.61%, and 80.49%, respectively; AUC of these six model were 0.976, 0.841, 0.838, 0.865, 0.750, and 0.801, respectively. The accuracy and AUC of combined SVM were higher than the other 5 models (P<0.05).
Conclusions: Our findings indicate that SVM model based on plasma miRNAs biomarkers may serve as a novel, accurate, noninvasive method for auxiliary diagnosis of lung cancer.
Keywords: Lung cancer, Plasma miRNAs, Support vector machine, Diagnosis