Analysis of MicroRNAs in Sputum to Improve Computed Tomography for Lung Cancer Diagnosis

Introduction: Computed tomography (CT) plays a central role in lung cancer diagnosis. However, CT has relatively low specificity, presenting a challenge in clinical settings. We previously identified 12 microRNAs (miRNAs) whose expressions in tumor tissues were associated with lung cancer. Methods: Using quantitative reverse transcriptase polymerase chain reaction, we aimed to identify miRNA biomarkers in sputum that could complement CT for diagnosis of lung cancer. Results: In a training set consisting of 66 lung cancer patients and 68 cancer-free smokers, 10 of the 12 miRNAs were differentially expressed between the cases and controls (p 0.01). From the miRNAs, a logistic regression model was built on the basis of miR-31 and miR-210, both of which had the best prediction for lung cancer, producing an area under receiver operating characteristic curve of 0.83. Combined use of the two miRNAs yielded 65.2% sensitivity and 89.7% specificity, CT had 93.9% sensitivity and 83.8% specificity for lung cancer diagnosis. Notably, combined analysis of the miRNA biomarkers and CT produced a higher specificity than does CT used alone (91.2% versus 83.8%; p < 0.05). The diagnostic performance of the biomarkers was confirmed in a testing set comprising 64 lung cancer patients and 73 cancer-free smokers. Conclusion: The sputum miRNA biomarkers might be useful in improving CT for diagnosis of lung cancer, but further independent validation on an external and prospective cohort of patients is required.