The prognostic and diagnostic value of microRNA (miRNA) expression aberrations in lung cancer has been studied intensely in recent years. However, due to the application of different technological platforms and small sample size, the miRNA expression profiling efforts have led to inconsistent results between the studies. We performed a comprehensive meta-analysis of 20 published miRNA expression studies in lung cancer, including a total of 598 tumor and 528 non-cancerous control samples. Using a recently published robust rank aggregation method, we identified a statistically significant miRNA meta-signature of seven upregulated (miR-21, miR-210, miR-182, miR-31, miR-200b, miR-205 and miR-183) and eight downregulated (miR-126-3p, miR-30a, miR-30d, miR-486-5p, miR-451a, miR-126-5p, miR-143 and miR-145) miRNAs. We conducted a gene set enrichment analysis to identify pathways that are most strongly affected by altered expression of these miRNAs. We found that meta-signature miRNAs cooperatively target functionally related and biologically relevant genes in signaling and developmental pathways. We have shown that such meta-analysis approach is suitable and effective solution for identification of statistically significant miRNA meta-signature by combining several miRNA expression studies. This method allows the analysis of data produced by different technological platforms that cannot be otherwise directly compared or in the case when raw data are unavailable.
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