Differential microRNA (miRNA) expression profiling by high-throughput methods has generated a vast amount of information about the complex role of these small regulatory molecules in a broad spectrum of human diseases. However, the results of such studies are often inconsistent, mostly due to the lack of cross-platform standardization, ongoing discovery of novel miRNAs, and small sample size. Therefore, a critical and systematic analysis of all available information is essential for successful identification of the most relevant miRNAs. Meta-analysis approach allows integrating the results from several independent studies in order to achieve greater statistical power and estimate the variability between the studies. Here we describe as an example the use of a robust rank aggregation (RRA) method for identification of miRNA meta-signature in lung cancer. This method analyzes prioritized gene lists and finds commonly overlapping genes, which are ranked consistently better than expected by chance. An RRA approach not only helps to prioritize the putative targets for further experimental studies but also highlights the challenges related with the development of miRNA-based tests and emphasizes the need for rigorous evaluation of the results before proceeding to clinical trials.