Integrative microRNA and gene profiling data analysis reveals novel biomarkers and mechanisms for lung cancer

BACKGROUND: Studies on the accuracy of microRNAs (miRNAs) in diagnosing non-small cell lung cancer (NSCLC) have still controversial. Therefore, we conduct to systematically identify miRNAs related to NSCLC, and their target genes expression changes using microarray data sets. METHODS: We screened out five miRNAs and six genes microarray data sets that contained miRNAs and genes expression in NSCLC from Gene Expression Omnibus. RESULTS: Our analysis results indicated that fourteen miRNAs were significantly dysregulated in NSCLC. Five of them were up-regulated (miR-9, miR-708, miR-296-3p, miR-892b, miR-140-5P) while nine were down-regulated (miR-584, miR-218, miR-30b, miR-522, miR486-5P, miR-34c-3p, miR-34b, miR-516b, miR-592). The integrating diagnosis sensitivity (SE) and specificity (SP) were 82.6% and 89.9%, respectively. We also found that 4 target genes (p < 0.05, fold change > 2.0) were significant correlation with the 14 discovered miRNAs, and the classifiers we built from one training set predicted the validation set with higher accuracy (SE = 0.987, SP = 0.824). CONCLUSIONS: Our results demonstrate that integrating miRNAs and target genes are valuable for identifying promising biomarkers, and provided a new insight on underlying mechanism of NSCLC. Further, our well-designed validation studies surely warrant the investigation of the role of target genes related to these 14 miRNAs in the prediction and development of NSCLC.