Diagnosis value of aberrantly expressed microRNA profiles in lung squamous cell carcinoma: a study based on the Cancer Genome Atlas

Abstract

Background Lung cancer is considered as one of the most frequent and deadly cancers with high mortality all around the world. It is critical to find new biomarkers for early diagnosis of lung cancer, especially lung squamous cell carcinoma (LUSC). The Cancer Genome Atlas (TCGA) is a database which provides both cancer and clinical information. This study is a comprehensive analysis of a novel diagnostic biomarker for LUSC, based on TCGA. Methods and Results The present study investigated LUSC-specific key microRNAs (miRNAs) from large-scale samples in TCGA. According to exclusion criteria and inclusion criteria, the expression profiles of miRNAs with related clinical information of 332 LUSC patients were obtained. Most aberrantly expressed miRNAs were identified between tumor and normal samples. Forty-two LUSC-specific intersection miRNAs (fold change >2, p < 0.05) were obtained by an integrative computational method, among them six miRNAs were found to be aberrantly expressed concerning characteristics of patients (gender, lymphatic metastasis, patient outcome assessment) through Student t-test. Five miRNAs correlated with overall survival (log-rank p < 0.05) were obtained through the univariate Cox proportional hazards regression model and Mantel–Haenszel test. Then, five miRNAs were randomly selected to validate the expression in 47 LUSC patient tissues using quantitative real-time polymerase chain reaction. The results showed that the test findings were consistent with the TCGA findings. Also, the diagnostic value of the specific key miRNAs was determined by areas under receiver operating characteristic curves. Finally, 577 interaction mRNAs as the targets of 42 LUSC-specific intersection miRNAs were selected for further bioinformatics analysis. Conclusion This study indicates that this novel microRNA expression signature may be a useful biomarker of the diagnosis for LUSC patients, based on bioinformatics analysis.