Blood-borne miRNA profile-based diagnostic classifier for lung adenocarcinoma

Abstract

Accumulated evidence indicates that various types of miRNA are aberrantly expressed in lung cancer and secreted into the bloodstream. For this study, we constructed a serum diagnostic classifier based on detailed bioinformatics analysis of miRNA profiles from a training cohort of 143 lung adenocarcinoma patients and 49 healthy subjects, resulting in a 20 miRNA-based classifier. Validation performed with an independent cohort of samples from lung adenocarcinoma patients (n = 110), healthy subjects (n = 52), and benign pulmonary disease patients (n = 47) showed a sensitivity of 89.1% and specificity of 94.9%, with an area under the curve value of 0.958. Notably, 90.8% of Stage I lung adenocarcinoma cases were correctly diagnosed. Interestingly, this classifier also detected squamous and large cell lung carcinoma cases at relatively high rates (70.4% and 70.0%, respectively), which appears to be consistent with organ site-dependent miRNA expression in cancer tissues. In contrast, we observed significantly lower rates (0-35%) using samples from 96 cases of cancer in other major organs, with breast cancer the lowest. These findings warrant a future study to realize its clinical application as a part of diagnostic procedures for lung cancers, for which early detection and surgical removal is presently the only hope for eventual cure.