The challenges are how to collate the results of those miRNAs exp

The challenges are how to collate the results of those miRNAs expression Trichostatin A 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 Lazertinib nmr 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. Methods Study selection PubMed was used to search for lung cancer miRNA expression profiling studies published from January 2003 and May 2012 (last accessed on 15 May 2012), by means of the MeSH terms: ‘lung neoplasms’

and ‘microRNAs’ in combination with the keyword ‘profiling’ and ‘humans’. Eligible studies

had to meet the following criteria: (i), they were miRNA expression profiling studies in lung cancer patients; (ii), they used tissue samples obtained from surgically resected lung tumor and corresponding noncancerous or normal tissues for comparison; (iii), use of miRNA microarray methods; (iv), reporting of cut-off criteria of differentially expressed miRNAs, and (v), validation method and validation sample set reported. Therefore, Avelestat (AZD9668) the miRNA profiling studies using the serum, or sputum samples of lung cancer patients or lung cancer cell lines, or using different miRNA technologies were excluded. Review articles and the studies comparing miRNA expression profiles in lung squamous cell carcinoma from those in lung adenocarcinoma were also excluded. Data abstraction Two investigators (PG and ZY) independently evaluated and extracted the data with the standard protocol and with all the discrepancies resolved by a third investigator (BZ). From the full text and corresponding supplement information, the following eligibility items were collected and recorded for each study: author, journal and year of publication, location of study, selection and characteristics of recruited lung cancer patients, platform of miRNA expression profiling, author defined cut-off criteria of statistically differentially expressed miRNAs and the list of up- and down-regulated miRNA features, and their corresponding fold change (if available).

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