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: Diagnosis; Lung cancer; Plasma miRNAs; Support vector machine.
© The author(s).