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Analysis of circulating microRNA biomarkers for breast cancer detection: a meta-analysis

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Tumor Biology

Abstract

Circulating microRNAs (miRNAs) have been reported to be aberrantly expressed in patients with breast cancer (BC) and thus may serve as potential diagnostic biomarkers. This meta-analysis aimed to assess the potential diagnostic value of using circulating miRNAs for BC. The summary receiver operator characteristic (SROC) curve was used to assess the overall diagnostic performance of circulating miRNA. All analyses were performed using STATA 12.0 software. Thirty-one studies from 16 publications with a total of 1,668 BC patients and 1,111 healthy controls were included in this meta-analysis. Our results showed that the pooled sensitivity (SEN) for miRNAs assays was 0.77 (95 % CI 0.69–0.84), specificity (SPE) was 0.88 (95 % CI 0.79–0.93), positive likelihood ratio (PLR) was 4.2 (95 % CI 3.0–6.0), negative LR (NLR) was 0.29 (95 % CI 0.21–0.40), and diagnostic odds ratio (DOR) was 18 (95 % CI 10–32). The area under the SROC curve (AUC) was 0.89 (95 % CI 0.86–0.91). Subgroup analysis suggested that employing a combination of multiple miRNAs was better than using a single miRNA in SEN (0.88 vs. 0.69), SPE (0.88 vs. 0.89), PLR (6.3 vs. 3.3), NLR (0.14 vs. 0.41), DOR (48 vs. 10), and AUC (0.94 vs. 0.83). In conclusion, our meta-analysis suggested that the expression profiles of circulating miRNAs, especially using a combination of them, have potential to facilitate accurate breast tumor detection. However, there are still challenges that need to be addressed to establish these new biomarkers before they can be applied to routine clinical procedures.

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References

  1. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61:69–90. doi:10.3322/caac.20107.

    Google Scholar 

  2. Siegel R, Ma J, Zou Z, Jemal A. Cancer statistics, 2014. CA Cancer J Clin. 2014;64:9–29. doi:10.3322/caac.21208.

    Article  Google Scholar 

  3. Muller A, Homey B, Soto H, Ge N, Catron D, Buchanan ME, et al. Involvement of chemokine receptors in breast cancer metastasis. Nature. 2001;410:50–6. doi:10.1038/35065016.

    Article  CAS  Google Scholar 

  4. Cuk K, Zucknick M, Heil J, Madhavan D, Schott S, Turchinovich A, et al. Circulating microRNAs in plasma as early detection markers for breast cancer. Int J Cancer. 2013;132:1602–12. doi:10.1002/ijc.27799.

    Article  CAS  Google Scholar 

  5. Tertov VV, Orekhov AN, Smirnov VN. Effect of cyclic AMP on lipid accumulation and metabolism in human atherosclerotic aortic cells. Atherosclerosis. 1986;62:55–64.

    Article  CAS  Google Scholar 

  6. Fassan M, Volinia S, Palatini J, Pizzi M, Baffa R, De Bernard M, et al. MicroRNA expression profiling in human Barrett’s carcinogenesis. Int J Cancer. 2011;129:1661–70. doi:10.1002/ijc.25823.

    Article  CAS  Google Scholar 

  7. Heneghan HM, Miller N, Kelly R, Newell J, Kerin MJ. Systemic miRNA-195 differentiates breast cancer from other malignancies and is a potential biomarker for detecting noninvasive and early stage disease. Oncologist. 2010;15:673–82. doi:10.1634/theoncologist.2010-0103.

    Article  Google Scholar 

  8. Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, et al. Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A. 2002;99:15524–9. doi:10.1073/pnas.242606799.

    Article  CAS  Google Scholar 

  9. Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005;65:7065–70. doi:10.1158/0008-5472.CAN-05-1783.

    Article  CAS  Google Scholar 

  10. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, et al. MicroRNA expression profiles classify human cancers. Nature. 2005;435:834–8. doi:10.1038/nature03702.

    Article  CAS  Google Scholar 

  11. Zhao H, Shen J, Medico L, Wang D, Ambrosone CB, Liu S. A pilot study of circulating miRNAs as potential biomarkers of early stage breast cancer. PLoS One. 2010;5:e13735. doi:10.1371/journal.pone.0013735.

    Article  Google Scholar 

  12. Sun Y, Wang M, Lin G, Sun S, Li X, Qi J, et al. Serum microRNA-155 as a potential biomarker to track disease in breast cancer. PLoS One. 2012;7:e47003. doi:10.1371/journal.pone.0047003.

    Article  CAS  Google Scholar 

  13. Eichelser C, Flesch-Janys D, Chang-Claude J, Pantel K, Schwarzenbach H. Deregulated serum concentrations of circulating cell-free microRNAs miR-17, miR-34a, miR-155, and miR-373 in human breast cancer development and progression. Clin Chem. 2013;59:1489–96. doi:10.1373/clinchem.2013.205161.

    Article  CAS  Google Scholar 

  14. Zeng RC, Zhang W, Yan XQ, Ye ZQ, Chen ED, Huang DP, et al. Down-regulation of miRNA-30a in human plasma is a novel marker for breast cancer. Med Oncol. 2013;30:477. doi:10.1007/s12032-013-0477-z.

    Article  Google Scholar 

  15. Mar-Aguilar F, Mendoza-Ramirez JA, Malagon-Santiago I, Espino-Silva PK, Santuario-Facio SK, Ruiz-Flores P, et al. Serum circulating microRNA profiling for identification of potential breast cancer biomarkers. Dis Markers. 2013;34:163–9. doi:10.3233/dma-120957.

    Article  CAS  Google Scholar 

  16. Gao J, Zhang Q, Xu J, Guo L, Li X. Clinical significance of serum miR-21 in breast cancer compared with CA153 and CEA. Chin J Cancer Res. 2013;25:743–8. doi:10.3978/j.issn.1000-9604.2013.12.04.

    PubMed  PubMed Central  Google Scholar 

  17. Asaga S, Kuo C, Nguyen T, Terpenning M, Giuliano AE, Hoon DS. Direct serum assay for microRNA-21 concentrations in early and advanced breast cancer. Clin Chem. 2011;57:84–91. doi:10.1373/clinchem.2010.151845.

    Article  CAS  Google Scholar 

  18. Barker 2nd FG, Carter BS. Synthesizing medical evidence: systematic reviews and metaanalyses. Neurosurg Focus. 2005;19:E5.

    Article  Google Scholar 

  19. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529–36. doi:10.7326/0003-4819-155-8-201110180-00009.

    Article  Google Scholar 

  20. Mitchell AJ, Vaze A, Rao S. Clinical diagnosis of depression in primary care: a meta-analysis. Lancet. 2009;374:609–19. doi:10.1016/S0140-6736(09)60879-5.

    Article  Google Scholar 

  21. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–60. doi:10.1136/bmj.327.7414.557.

    Article  Google Scholar 

  22. Dinnes J, Deeks J, Kirby J, Roderick P. A methodological review of how heterogeneity has been examined in systematic reviews of diagnostic test accuracy. Health Technol Assess. 2005;9:1–113. iii.

    Article  CAS  Google Scholar 

  23. Deeks JJ, Macaskill P, Irwig L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol. 2005;58:882–93. doi:10.1016/j.jclinepi.2005.01.016.

    Article  Google Scholar 

  24. Heneghan HM, Miller N, Lowery AJ, Sweeney KJ, Newell J, Kerin MJ. Circulating microRNAs as novel minimally invasive biomarkers for breast cancer. Ann Surg. 2010;251:499–505. doi:10.1097/SLA.0b013e3181cc939f.

    Article  Google Scholar 

  25. Guo LJ, Zhang QY. Decreased serum miR-181a is a potential new tool for breast cancer screening. Int J Mol Med. 2012;30:680–6. doi:10.3892/ijmm.2012.1021.

    Article  CAS  Google Scholar 

  26. Hu Z, Dong J, Wang LE, Ma H, Liu J, Zhao Y, et al. Serum microRNA profiling and breast cancer risk: the use of miR-484/191 as endogenous controls. Carcinogenesis. 2012;33:828–34. doi:10.1093/carcin/bgs030.

    Article  CAS  Google Scholar 

  27. Madhavan D, Zucknick M, Wallwiener M, Cuk K, Modugno C, Scharpff M, et al. Circulating miRNAs as surrogate markers for circulating tumor cells and prognostic markers in metastatic breast cancer. Clin Cancer Res. 2012;18:5972–82. doi:10.1158/1078-0432.ccr-12-1407.

    Article  CAS  Google Scholar 

  28. Schrauder MG, Strick R, Schulz-Wendtland R, Strissel PL, Kahmann L, Loehberg CR, et al. Circulating micro-RNAs as potential blood-based markers for early stage breast cancer detection. PLoS One. 2012;7:e29770. doi:10.1371/journal.pone.0029770.

    Article  CAS  Google Scholar 

  29. Wang B, Zhang Q. The expression and clinical significance of circulating microRNA-21 in serum of five solid tumors. J Cancer Res Clin Oncol. 2012;138:1659–66. doi:10.1007/s00432-012-1244-9.

    Article  CAS  Google Scholar 

  30. Wu Q, Wang C, Lu Z, Guo L, Ge Q. Analysis of serum genome-wide microRNAs for breast cancer detection. Clin Chim Acta. 2012;413:1058–65. doi:10.1016/j.cca.2012.02.016.

    Article  CAS  Google Scholar 

  31. Ng EK, Li R, Shin VY, Jin HC, Leung CP, Ma ES, et al. Circulating microRNAs as specific biomarkers for breast cancer detection. PLoS One. 2013;8:e53141. doi:10.1371/journal.pone.0053141.

    Article  CAS  Google Scholar 

  32. McDermott AM, Miller N, Wall D, Martyn LM, Ball G, Sweeney KJ, et al. Identification and validation of oncologic miRNA biomarkers for luminal a-like breast cancer. PLoS One. 2014;9:e87032. doi:10.1371/journal.pone.0087032.

    Article  Google Scholar 

  33. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55:74–108.

    Article  Google Scholar 

  34. Yang L, Parkin DM, Ferlay J, Li L, Chen Y. Estimates of cancer incidence in China for 2000 and projections for 2005. Cancer Epidemiol Biomarkers Prev. 2005;14:243–50.

    Article  Google Scholar 

  35. Duffy MJ. Role of tumor markers in patients with solid cancers: a critical review. Eur J Intern Med. 2007;18:175–84. doi:10.1016/j.ejim.2006.12.001.

    Article  CAS  Google Scholar 

  36. Roulston JE. Limitations of tumour markers in screening. Br J Surg. 1990;77:961–2.

    Article  CAS  Google Scholar 

  37. Jones CM, Athanasiou T. Summary receiver operating characteristic curve analysis techniques in the evaluation of diagnostic tests. Ann Thorac Surg. 2005;79:16–20. doi:10.1016/j.athoracsur.2004.09.040.

    Article  Google Scholar 

  38. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol. 2003;56:1129–35.

    Article  Google Scholar 

  39. Zamora J, Abraira V, Muriel A, Khan K, Coomarasamy A. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC Med Res Methodol. 2006;6:31. doi:10.1186/1471-2288-6-31.

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Hospital Management and Research, Chinese PLA General Hospital, Beijing, 100853, China

    Lihua Liu, Xiutang Cao & Jianchao Liu

  2. Department of Epidemiology and Biostatistics, Institute of Basic Medicine, Peking Union Medical College/Chinese Academy of Medical Sciences, Beijing, 100050, China

    Shan Wang

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  1. Lihua Liu
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  2. Shan Wang
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Correspondence to Lihua Liu.

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L. Liu and S. Wang are the co-first authors.

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Liu, L., Wang, S., Cao, X. et al. Analysis of circulating microRNA biomarkers for breast cancer detection: a meta-analysis. Tumor Biol. 35, 12245–12253 (2014). https://doi.org/10.1007/s13277-014-2533-5

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