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Meta-analysis of human lung cancer microRNA expression profiling studies comparing cancer tissues with normal tissues
Journal of Experimental & Clinical Cancer Research volume 31, Article number: 54 (2012)
Lung cancer is the major cause of cancer death globally, it is often diagnosed at an advanced stage and has one of the lowest survival rates of any type of cancer. The common interest in the field of lung cancer research is the identification of biomarkers for early diagnosis and accurate prognosis. There is increasing evidence to suggest that microRNAs play important and complex roles in lung cancer.
A meta-analysis was conducted to review the published microRNA expression profiling studies that compared the microRNAs expression profiles in lung cancer tissues with those in normal lung tissues. A 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 was employed.
A total of 184 differentially expressed microRNAs were reported in the fourteen microRNA expression profiling studies that compared lung cancer tissues with normal tissues, with 61 microRNAs were reported in at least two studies. In the panel of consistently reported up-regulated microRNAs, miR-210 was reported in nine studies and miR-21 was reported in seven studies. In the consistently reported down-regulated microRNAs, miR-126 was reported in ten studies and miR-30a was reported in eight studies. Four up-regulated microRNAs (miR-210, miR-21, miR-31 and miR-182) and two down-regulated mcroiRNAs (miR-126 and miR-145) were consistently reported both in squamous carcinoma and adenocarcinoma-based subgroup analysis, with the other 14 microRNAs solely reported in one or the other subset.
In conclusion, the top two most consistently reported up-regulated microRNAs were miR-210 and miR-21. The results of this meta-analysis of human lung cancer microRNA expression profiling studies might provide some clues of the potential biomarkers in lung cancer. Further mechanistic and external validation studies are needed for their clinical significance and role in the development of lung cancer.
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.
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, 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.
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).
Each included studies comparing miRNA expression between lung cancer tissues and neighbouring noncancerous or normal lung tissues provided a list of differentially expressed miRNAs. Then, the following vote-counting strategy based method of ranking potential molecular biomarkers, by Griffith  and Chan , was adopted in the meta-analysis. The differentially expressed miRNAs reported by each study were ranked according to the following order of importance (i), number of the studies that consistently reported the miRNA as differentially expressed and with a consistent direction of change; (ii), total number of samples for comparison in agreement; (iii), average fold change reported by the studies in agreement (only based on the subset of studies with available fold change information). All the comparisons were stepwise made with the online bioinformatics tool (http://jura.wi.mit.edu/bioc/tools/compare.php), and the ranking was performed by Statistical Product and Service Solutions (SPSS 12.0 for windows, SPSS Inc., Chicago, IL, USA).
Results and discussion
Included independent studies
A total of 137 relevant publications were indexed in PubMed. According to the inclusion criteria and identification of duplicate publication, only 14 independent studies [19–32] were included in the analysis. The characteristics of these studies are listed in Table 1 in alphabetical order of the first author. Among the fourteen included studies, four studies focused on lung squamous cell carcinoma, three studies focused on lung adenocarcinoma, six studies were about non-small cell lung cancer, and one study based on non-specified lung cancer patients (Table 1). Reference 30 also provided the differentially expression miRNAs by histological type, and the miRNA profiles in lung squamous cell carcinoma of reference 21 was described in a separate publication , which made it possible to further explore and compare the deregulated miRNAs in different histological type of lung cancer. Different platforms and various statistical and bio-computational analyses have been utilized in the collected profiling studies. The number of differential miRNAs ranges from 1 to 60, with the median 20. There is one study  that only provided the top ten miRNAs of the identified 56 significantly differentially expressed miRNAs. Ten of the fourteen eligible studies provided fold change (FC) information of differentially expressed miRNAs. As one environmental well-known risk factor of lung cancer is tobacco smoking, six studies provided the information of patients’ smoking status. Among them, all the lung cancer patients in reference 19 were current or former heavy smokers and all the lung cancer patients in reference 22 and 25 were never smokers.
Differentially expressed miRNAs
A total of 184 differentially expressed miRNAs were reported in the fourteen miRNA expression profiling studies that compared lung cancer tissues with normal tissues, with 61 miRNAs (33.2%) were reported in at least two studies. Among the 61 differentially expressed miRNAs, 54 miRNAs (88.5%) were with a consistent direction, 26 were reported to be up-gulated (Table 2) and 28 down-regulated (Table 3). The seven inconsistently reported miRNAs are listed in Table 4.
In the panel of consistently reported up-regulated miRNAs, miR-210 was reported in nine studies (average FC: 2.65) and miR-21 was reported in seven studies (average FC: 4.39). In the consistently reported down-regulated miRNAs, miR-126 was reported in ten studies (average FC: 0.33), and miR-30a was reported in eight studies (average FC: 0.36).
Subgroup analysis on histological type was conducted for further comparison. In the six studies based on the tissues from lung squamous carcinoma patients [24,26,29-31,33], nineteen deregulated miRNAs were consistently reported in at least two studies (8 up-regulated and 11 down-regulated) with miR-210 as the most frequent reported up-regulated miRNA (Table 5). In the subset of four studies about lung adenocarcinoma [20, 22, 30, 32], seven miRNAs were consistently reported, with miR-210 as the most frequent reported up-regulated miRNA (Table 6). Four up-regulated miRNAs (miR-210, miR-21. miR-31 and miR-182) and two down-regulated miRNAs (miR-126 and miR-145) were consistently reported both in squamous carcinoma and adenocarcinoma-based analysis, with the other 14 miRNAs solely reported in one subset or the other (Tables 5 and 6).
Factors to consider for miRNAs as biomarkers
To our knowledge, no meta-analysis of miRNA profiling studies has investigated lung cancer specially. This kind of systematic review has been proved to be useful in exploring candidate miRNA biomarkers in human colorectal cancer . The present study suggested several promising miRNAs that have been consistently reported with average more than 2-fold change. Their potential targets may provide a clue to the role of miRNAs in tumorigenesis and the underlying mechanisms.
There are several factors needed to be considered when choosing miRNAs as candidate clinical biomarkers of lung cancer. First, the biological complexities should be well understood. A single miRNA may have many targets, and also, a specific mRNA may be regulated by multiple different miRNAs . More understanding of molecular mechanisms that can mediate miRNA dysregulations and the targets of the miRNAs would advance their use in clinical settings.
Second, there should be sufficient information about their pattern of expression in different kinds of specimens in target populations. The release mechanism of miRNAs can be via tumor-derived microvesicles or exosomes [36, 37]. It has been indicated that circulating miRNAs in plasma could be more tissue-specific than tumor-specific [8, 38], thus our study focused on the profiling studies that compared miRNA profiles in lung cancer tissues with those in normal lung tissues. Boeri and colleagues found that miRNAs deregulated in tissue specimens were rarely detected in plasma samples, which further strengthened the high tissue-specificity of miRNAs and suggested the predictive role of plasma miRNAs independent from tissue specimens . In the context of the inconsistent profiles between tissue-based and plasma-based result, however, some consistently reported miRNAs in tissue-based profiling studies, for example, a panel of miR-21, miR-210 and miR-486-5p, have been validated in plasma-based studies to confirm their diagnostic value in the diagnosis of lung cancer with solitary pulmonary nodules . Future studies that based on parallel plasma and tissue samples may provide more solid evidence. For the included profiling studies in which adjacent corresponding normal lung tissue served as an expression baseline, we need to know that adjacent appearing morphologically normal tissue may contain molecular changes associated with cancer [40, 41].
Third, rigorous validation and demonstration of reproducibility in an independent population are necessary to confirm the predictive value of miRNAs. One of the most frequently investigated miRNAs is miR-21, it ranks second among consistently reported up-regulated miRNAs in this meta-analysis, it has been also reported to be associated with prognosis in several kinds of cancer [42–44]. From the prognostic point of view, over expression of miR-21 has been reported to be independently associated with reduced survival of pancreatic ductal adenocarcinoma . High miR-21 expression was also associated with poor survival of colon adenocarcinoma in both the training cohort (US test cohort of 84 patients with incident colon adenocarcinoma, recruited between 1993 and 2002) and validation cohort (independent Chinese cohort of 113 patients recruited between 1991 and 2000) . However, when expression of miR-21, miR-29b, miR-34a/b/c, miR-155, and let-7a was determined by quantitative real-time PCR in formalin-fixed paraffin-embedded tumor specimens from 639 patients who participated in the International Adjuvant Lung Cancer Trial (IALT), there was a deleterious borderline prognostic effect of lowered miR-21 expression .
In conclusion, the top two most consistently reported up-regulated miRNAs were miR-210 and miR-21. The results of this meta-analysis of human lung cancer miRNA expression profiling studies might provide some clues of the potential biomarkers in lung cancer. Further mechanistic and external validation studies are needed for their clinical significance and role in the development of lung cancer.
False discovery rate
Non-small cell lung cancer
Squamous cell carcinoma.
Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D: Global cancer statistics. CA Cancer J Clin. 2011, 61: 69-90. 10.3322/caac.20107.
Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM: Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010, 127: 2893-2917. 10.1002/ijc.25516.
Goldstraw P, Ball D, Jett JR, Le Chevalier T, Lim E, Nicholson AG, Shepherd FA: Non-small-cell lung cancer. Lancet. 2011, 378: 1727-1740. 10.1016/S0140-6736(10)62101-0.
van Meerbeeck JP, Fennell DA, De Ruysscher DK: Small-cell lung cancer. Lancet. 2011, 378: 1741-1755. 10.1016/S0140-6736(11)60165-7.
O’Byrne KJ, Gatzemeier U, Bondarenko I, Barrios C, Eschbach C, Martens UM, Hotko Y, Kortsik C, Paz-Ares L, Pereira JR, von Pawel J, Ramlau R, Roh JK, Yu CT, Stroh C, Celik I, Schueler A, Pirker R: Molecular biomarkers in non-small-cell lung cancer: a retrospective analysis of data from the phase 3 FLEX study. Lancet Oncol. 2011, 12: 795-805. 10.1016/S1470-2045(11)70189-9.
Mok TS: Personalized medicine in lung cancer: what we need to know. Nat Rev Clin Oncol. 2011, 8: 661-668. 10.1038/nrclinonc.2011.126.
Subramanian J, Simon R: Gene expression-based prognostic signatures in lung cancer: ready for clinical use?. J Natl Cancer Inst. 2010, 102: 464-474. 10.1093/jnci/djq025.
Liu CG, Calin GA, Meloon B, Gamliel N, Sevignani C, Ferracin M, Dumitru CD, Shimizu M, Zupo S, Dono M, Alder H, Bullrich F, Negrini M, Croce CM: An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues. Proc Natl Acad Sci USA. 2004, 101: 9740-9744. 10.1073/pnas.0403293101.
Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, Lanza G, Scarpa A, Vecchione A, Negrini M, Harris CC, Croce CM: A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA. 2006, 103: 2257-2261. 10.1073/pnas.0510565103.
Hui A, How C, Ito E, Liu FF: Micro-RNAs as diagnostic or prognostic markers in human epithelial malignancies. BMC Cancer. 2011, 11: 500-10.1186/1471-2407-11-500.
Carrington JC, Ambros V: Role of microRNAs in plant and animal development. Science. 2003, 301: 336-338. 10.1126/science.1085242.
Suárez Y, Sessa WC: MicroRNAs as novel regulators of angiogenesis. Circ Res. 2009, 104: 442-454. 10.1161/CIRCRESAHA.108.191270.
Hatley ME, Patrick DM, Garcia MR, Richardson JA, Bassel-Duby R, van Rooij E, Olson EN: Modulation of K-Ras-dependent lung tumorigenesis by MicroRNA-21. Cancer Cell. 2010, 18: 282-293. 10.1016/j.ccr.2010.08.013.
Heller G, Weinzierl M, Noll C, Babinsky V, Ziegler B, Altenberger C, Minichsdorfer C, Lang G, Döme B, End-Pfützenreuter A, Arns BM, Grin Y, Klepetko W, Zielinski CC, Zöchbauer-Müller S: Genome-Wide miRNA Expression Profiling Identifies miR-9-3 and miR-193a as Targets for DNA Methylation in Non-Small Cell Lung Cancers. Clin Cancer Res. 2012, 18: 1619-1629. 10.1158/1078-0432.CCR-11-2450.
Foss KM, Sima C, Ugolini D, Neri M, Allen KE, Weiss GJ: miR-1254 and miR-574-5p: serum-based microRNA biomarkers for early-stage non-small cell lung cancer. J Thorac Oncol. 2011, 6: 482-488. 10.1097/JTO.0b013e318208c785.
Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA. 2004, 101: 9309-9314. 10.1073/pnas.0401994101.
Griffith OL, Melck A, Jones SJ, Wiseman SM: Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers. J Clin Oncol. 2006, 24: 5043-5051. 10.1200/JCO.2006.06.7330.
Chan SK, Griffith OL, Tai IT, Jones SJ: Meta-analysis of colorectal cancer gene expression profiling studies identifies consistently reported candidate biomarkers. Cancer Epidemiol Biomarkers Prev. 2008, 17: 543-552. 10.1158/1055-9965.EPI-07-2615.
Boeri M, Verri C, Conte D, Roz L, Modena P, Facchinetti F, Calabrò E, Croce CM, Pastorino U, Sozzi G: MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer. Proc Natl Acad Sci USA. 2011, 108: 3713-3718. 10.1073/pnas.1100048108.
Dacic S, Kelly L, Shuai Y, Nikiforova MN: miRNA expression profiling of lung adenocarcinomas: correlation with mutational status. Mod Pathol. 2010, 23: 1577-1582. 10.1038/modpathol.2010.152.
Gao W, Yu Y, Cao H, Shen H, Li X, Pan S, Shu Y: Deregulated expression of miR-21, miR-143 and miR-181a in non small cell lung cancer is related to clinicopathologic characteristics or patient prognosis. Biomed Pharmacother. 2010, 64: 399-408. 10.1016/j.biopha.2010.01.018.
Jang J, Jeon HS, Sun Z, Aubry MC, Tang H, Park CH, Rakhshan F, Schultz DA, Kolbert CP, Lupu R, Park JY, Harris CC, Yang P, Jin J: Increased miR-708 Expression in NSCLC and Its Association with Poor Survival in Lung Adenocarcinoma from Never Smokers. Clin Cancer Res. 2012, in press
Ma L, Huang Y, Zhu W, Zhou S, Zhou J, Zeng F, Liu X, Zhang Y, Yu J: An integrated analysis of miRNA and mRNA expressions in non-small cell lung cancers. PLoS One. 2011, 6: e26502-10.1371/journal.pone.0026502.
Raponi M, Dossey L, Jatkoe T, Wu X, Chen G, Fan H, Beer DG: MicroRNA classifiers for predicting prognosis of squamous cell lung cancer. Cancer Res. 2009, 69: 5776-5783. 10.1158/0008-5472.CAN-09-0587.
Seike M, Goto A, Okano T, Bowman ED, Schetter AJ, Horikawa I, Mathe EA, Jen J, Yang P, Sugimura H, Gemma A, Kudoh S, Croce CM, Harris CC: MiR-21 is an EGFR-regulated anti-apoptotic factor in lung cancer in never-smokers. Proc Natl Acad Sci USA. 2009, 106: 12085-12090. 10.1073/pnas.0905234106.
Tan X, Qin W, Zhang L, Hang J, Li B, Zhang C, Wan J, Zhou F, Shao K, Sun Y, Wu J, Zhang X, Qiu B, Li N, Shi S, Feng X, Zhao S, Wang Z, Zhao X, Chen Z, Mitchelson K, Cheng J, Guo Y, He J: A 5-microRNA signature for lung squamous cell carcinoma diagnosis and hsa-miR-31 for prognosis. Clin Cancer Res. 2011, 17: 6802-6811. 10.1158/1078-0432.CCR-11-0419.
Võsa U, Vooder T, Kolde R, Fischer K, Välk K, Tõnisson N, Roosipuu R, Vilo J, Metspalu A, Annilo T: Identification of miR-374a as a prognostic marker for survival in patients with early-stage nonsmall cell lung cancer. Genes Chromosomes Cancer. 2011, 50: 812-822. 10.1002/gcc.20902.
Wang R, Wang ZX, Yang JS, Pan X, De W, Chen LB: MicroRNA-451 functions as a tumor suppressor in human non-small cell lung cancer by targeting ras-related protein 14 (RAB14). Oncogene. 2011, 30: 2644-2658. 10.1038/onc.2010.642.
Xing L, Todd NW, Yu L, Fang H, Jiang F: Early detection of squamous cell lung cancer in sputum by a panel of microRNA markers. Mod Pathol. 2010, 23: 1157-1164. 10.1038/modpathol.2010.111.
Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, Stephens RM, Okamoto A, Yokota J, Tanaka T, Calin GA, Liu CG, Croce CM, Harris CC: Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 2006, 9: 189-198. 10.1016/j.ccr.2006.01.025.
Yang Y, Li X, Yang Q, Wang X, Zhou Y, Jiang T, Ma Q, Wang YJ: The role of microRNA in human lung squamous cell carcinoma. Cancer Genet Cytogenet. 2010, 200: 127-133. 10.1016/j.cancergencyto.2010.03.014.
Yu L, Todd NW, Xing L, Xie Y, Zhang H, Liu Z, Fang H, Zhang J, Katz RL, Jiang F: Early detection of lung adenocarcinoma in sputum by a panel of microRNA markers. Int J Cancer. 2010, 127: 2870-2878. 10.1002/ijc.25289.
Gao W, Shen H, Liu L, Xu J, Xu J, Shu Y: MiR-21 overexpression in human primary squamous cell lung carcinoma is associated with poor patient prognosis. J Cancer Res Clin Oncol. 2011, 137: 557-566. 10.1007/s00432-010-0918-4.
Ma Y, Zhang P, Yang J, Liu Z, Yang Z, Qin H: Candidate microRNA biomarkers in human colorectal cancer: systematic review profiling studies and experimental validation. Int J Cancer. 2012, 130: 2077-2087. 10.1002/ijc.26232.
Cherni I, Weiss GJ: miRNAs in lung cancer: large roles for small players. Future Oncol. 2011, 7: 1045-1055. 10.2217/fon.11.74.
Skog J, Würdinger T, van Rijn S, Meijer DH, Gainche L, Sena-Esteves M, Curry WT, Carter BS, Krichevsky AM, Breakefield XO: Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol. 2008, 10: 1470-1476. 10.1038/ncb1800.
Valadi H, Ekström K, Bossios A, Sjöstrand M, Lee JJ, Lötvall JO: Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007, 9: 654-659. 10.1038/ncb1596.
Babak T, Zhang W, Morris Q, Blencowe BJ, Hughes TR: Probing microRNAs with microarrays: tissue specificity and functional inference. RNA. 2004, 10: 1813-1819. 10.1261/rna.7119904.
Shen J, Liu Z, Todd NW, Zhang H, Liao J, Yu L, Guarnera MA, Li R, Cai L, Zhan M, Jiang F: Diagnosis of lung cancer in individuals with solitary pulmonary nodules by plasma microRNA biomarkers. BMC Cancer. 2011, 11: 374-10.1186/1471-2407-11-374.
Woenckhaus M, Grepmeier U, Wild PJ, Merk J, Pfeifer M, Woenckhaus U, Stoelcker B, Blaszyk H, Hofstaedter F, Dietmaier W, Hartmann A: Multitarget FISH and LOH analyses at chromosome 3p in non-small cell lung cancer and adjacent bronchial epithelium. Am J Clin Pathol. 2005, 123: 752-761. 10.1309/C4BK7GQV8E5XU2TL.
Chandran UR, Dhir R, Ma C, Michalopoulos G, Becich M, Gilbertson J: Differences in gene expression in prostate cancer, normal appearing prostate tissue adjacent to cancer and prostate tissue from cancer free organ donors. BMC Cancer. 2005, 5: 45-10.1186/1471-2407-5-45.
Li T, Li RS, Li YH, Zhong S, Chen YY, Zhang CM, Hu MM, Shen ZJ: miR-21 as an Independent Biochemical Recurrence Predictor and Potential Therapeutic Target for Prostate Cancer. J Urol. 2012, 187: 1466-1472. 10.1016/j.juro.2011.11.082.
Jamieson NB, Morran DC, Morton JP, Ali A, Dickson EJ, Carter CR, Sansom OJ, Evans TR, McKay CJ, Oien KA: MicroRNA molecular profiles associated with diagnosis, clinicopathologic criteria, and overall survival in patients with resectable pancreatic ductal adenocarcinoma. Clin Cancer Res. 2012, 18: 534-545. 10.1158/1078-0432.CCR-11-0679.
Schetter AJ, Leung SY, Sohn JJ, Zanetti KA, Bowman ED, Yanaihara N, Yuen ST, Chan TL, Kwong DL, Au GK, Liu CG, Calin GA, Croce CM, Harris CC: MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. JAMA. 2008, 299: 425-436. 10.1001/jama.299.4.425.
Voortman J, Goto A, Mendiboure J, Sohn JJ, Schetter AJ, Saito M, Dunant A, Pham TC, Petrini I, Lee A, Khan MA, Hainaut P, Pignon JP, Brambilla E, Popper HH, Filipits M, Harris CC, Giaccone G: MicroRNA expression and clinical outcomes in patients treated with adjuvant chemotherapy after complete resection of non-small cell lung carcinoma. Cancer Res. 2010, 70: 8288-8298. 10.1158/0008-5472.CAN-10-1348.
This work was supported by Key Laboratory Project, Liaoning Provincial Department of Education (No. LS2010168) and the National Natural Science Foundation of China (No. 81102194). The funding sources had no role in the study design, data collection, analysis and interpretation, or in the writing of this manuscript.
The authors declare that they have no competing interests.
PG conceived the study and drafted the manuscript. PG and ZY collected and analyzed the data, PG and ZY also secured funding. XL, WW and BZ contributed to the quality control of study inclusion and discussion. All authors read and approved the final manuscript.
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Guan, P., Yin, Z., Li, X. et al. Meta-analysis of human lung cancer microRNA expression profiling studies comparing cancer tissues with normal tissues. J Exp Clin Cancer Res 31, 54 (2012). https://doi.org/10.1186/1756-9966-31-54
- Lung cancer