Qualitative serum microRNA signatures for lung cancer screening

Background: Lung cancer is the leading cause of morbidity and mortality among cancers worldwide. The early detection of lung cancer can effectively reduce the mortality rates among patients. Therefore, this study aims to construct signatures for the screening of lung cancer. Methods: The serum miRNA expression profiles of 5078 non-cancer, 1951 lung cancer and 3504 other cancer samples from four datasets were used. Subsequently, the samples were classified into one training dataset and two validation datasets. Results: In this study, we firstly demonstrate the differential expression pattern of serum miRNAs between lung cancer and non-cancer in four datasets, respectively. Subsequently, two qualitative serum microRNA signatures were established. The first signature, designated as LC-MPS2, was employed to distinguish lung cancer from non-cancer samples. The sensitivity and specificity of the signature were all over 99.0% in both the training and validation datasets. However, LC-MPS2 was unable to distinguish lung cancer from other types of cancer. Thus, an additional signature, comprising of six miRNA pairs designated as LC-MPS6, was constructed. This signature demonstrated 90.9% sensitivity and 91.1% specificity in the training datasets for discriminating lung cancer from multiple other types of cancers. In an independent validation dataset, LC-MPS6 achieved 80.2% sensitivity and 93.9% specificity. Conclusions: This study demonstrates that the qualitative serum microRNA signatures can accurately identify lung cancer from multiple cancer and non-cancer samples.