Lung cancer is the leading cause of cancer death in males and the second leading cause of cancer death among females in 2008 globally [1, 2]. Lung cancer is often diagnosed at an advanced stage and has one of the lowest survival rates of any type of cancer [3, 4]. The common interest in the field of lung cancer research is the identification of biomarkers for early diagnosis and accurate prognosis [5, 6], and the general starting point is to compare the gene expression profiles between lung cancer tissues and noncancerous/normal lung tissues. Although many efforts to develop a robust genomic model have been made in this area, controversy exists for their clinical application .
Recently, there is increasing evidence to suggest that microRNAs (miRNAs) play important and complex roles in human cancers, including lung cancer [8–10]. miRNAs are a class of small, noncoding, highly stable RNAs that regulate mRNA and protein expression. Several studies have indicated that miRNAs have been involved in regulating various biological processes, such as cellular differentiation, proliferation, angiogenesis, metabolism and cancer development [11–13]. Microarray-based miRNA profiling assays attracted more attention because they constitute the efficient methodology to screen in parallel for the expression of hundreds of miRNAs through extensive sample collections. With the aim at identifying new biomarkers of lung cancer, many investigators have carried out miRNAs expression profiling studies in cell lines, tissue samples or serum samples [9, 14, 15]. Typically, dozens of miRNAs are identified to be differentially expressed, miRNAs can be either over- or under-expressed, depending on their target downstream genes. Given the fact of a large number of candidate signatures, a logical approach to identify important expression signatures is to search for the intersection of those signatures identified in multiple independent studies . The challenges are how to collate the results of those miRNAs expression profiling studies, when they employed different profiling platforms, and made use of different methods to ascertain differential expression, for example, normalization or significance thresholds. To address these challenges, Griffith and Chan proposed a vote-counting strategy to identify consistent markers when raw data are unavailable [17, 18], which gave us insights into the meta-analysis of lung cancer miRNA expression profiling studies.
The starting point of this meta-analysis is to collect those published miRNAs expression profiling studies that compared the miRNAs expression profiles in lung cancer tissues with those in noncancerous/normal lung tissues. Then, the above mentioned vote-counting strategy that considers the total number of studies reporting its differential expression, the total number of tissue samples used in the studies and the average fold change will be employed. The consistently reported differentially miRNAs will be presented and we will also rank the differentially expressed up-regulated and down-regulated miRNAs.