J Breast Cancer.  2018 Dec;21(4):363-370. 10.4048/jbc.2018.21.e56.

Identification and Validation of Circulating MicroRNA Signatures for Breast Cancer Early Detection Based on Large Scale Tissue-Derived Data

Affiliations
  • 1School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China. hldu@scut.edu.cn
  • 2Department of Laboratory Science, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • 3Department of Laboratory Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.

Abstract

PURPOSE
Breast cancer is the most commonly occurring cancer among women worldwide, and therefore, improved approaches for its early detection are urgently needed. As microRNAs (miRNAs) are increasingly recognized as critical regulators in tumorigenesis and possess excellent stability in plasma, this study focused on using miRNAs to develop a method for identifying noninvasive biomarkers.
METHODS
To discover critical candidates, differential expression analysis was performed on tissue-originated miRNA profiles of 409 early breast cancer patients and 87 healthy controls from The Cancer Genome Atlas database. We selected candidates from the differentially expressed miRNAs and then evaluated every possible molecular signature formed by the candidates. The best signature was validated in independent serum samples from 113 early breast cancer patients and 47 healthy controls using reverse transcription quantitative real-time polymerase chain reaction.
RESULTS
The miRNA candidates in our method were revealed to be associated with breast cancer according to previous studies and showed potential as useful biomarkers. When validated in independent serum samples, the area under curve of the final miRNA signature (miR-21-3p, miR-21-5p, and miR-99a-5p) was 0.895. Diagnostic sensitivity and specificity were 97.9% and 73.5%, respectively.
CONCLUSION
The present study established a novel and effective method to identify biomarkers for early breast cancer. And the method, is also suitable for other cancer types. Furthermore, a combination of three miRNAs was identified as a prospective biomarker for breast cancer early detection.

Keyword

Breast neoplasms; Data mining; Early detection of cancer; MicroRNAs; Tumor biomarkers

MeSH Terms

Area Under Curve
Biomarkers
Biomarkers, Tumor
Breast Neoplasms*
Breast*
Carcinogenesis
Data Mining
Early Detection of Cancer
Female
Genome
Humans
Methods
MicroRNAs*
Plasma
Prospective Studies
Real-Time Polymerase Chain Reaction
Reverse Transcription
Sensitivity and Specificity
Biomarkers
Biomarkers, Tumor
MicroRNAs

Figure

  • Figure 1 Flow chart of the analysis design in the present study. The expression change-based method pipeline was described on the left and the random forest algorithm-based method on the right. Tissue profiles were used in discovery stage while independent serum profiles were used in validation stage. Intermediate results of the expression change-based method were compared with those of the random forest algorithm-based method [11] for evaluation purpose.miRNA=microRNA; qPCR=quantitative real-time polymerase chain reaction.

  • Figure 2 Comparison between three threshold defining methods. Signatures from the 20 microRNA (miRNA) candidates in the expression change-based method were grouped by the number of miRNAs, and the mean sensitivity and specificity were calculated respectively.

  • Figure 3 Expression levels of the three final microRNAs (miRNAs) in serum samples. The relative expression level of miRNAs was normalized to 2−ΔΔCq value and two-sided Student t-test was used to compare miRNA expression level.*p-value < 0.01.

  • Figure 4 Receiver operating characteristic curve of the final signature based on tissue data and independent serum data. The number of normal expressed microRNAs in signature was used as diagnostic index in this analysis.AUC=area under curve.


Reference

1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015; 65:87–108. PMID: 25651787.
Article
2. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Oncol Pract. 2007; 3:336–339. PMID: 29436954.
3. Lin XJ, Chong Y, Guo ZW, Xie C, Yang XJ, Zhang Q, et al. A serum microRNA classifier for early detection of hepatocellular carcinoma: a multicentre, retrospective, longitudinal biomarker identification study with a nested case-control study. Lancet Oncol. 2015; 16:804–815. PMID: 26088272.
Article
4. Du M, Shi D, Yuan L, Li P, Chu H, Qin C, et al. Circulating miR-497 and miR-663b in plasma are potential novel biomarkers for bladder cancer. Sci Rep. 2015; 5:10437. PMID: 26014226.
Article
5. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci U S A. 2008; 105:10513–10518. PMID: 18663219.
Article
6. Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 2008; 18:997–1006. PMID: 18766170.
Article
7. Gilad S, Meiri E, Yogev Y, Benjamin S, Lebanony D, Yerushalmi N, et al. Serum microRNAs are promising novel biomarkers. PLoS One. 2008; 3:e3148. PMID: 18773077.
Article
8. Wang K, Zhang S, Weber J, Baxter D, Galas DJ. Export of microRNAs and microRNA-protective protein by mammalian cells. Nucleic Acids Res. 2010; 38:7248–7259. PMID: 20615901.
Article
9. Pigati L, Yaddanapudi SC, Iyengar R, Kim DJ, Hearn SA, Danforth D, et al. Selective release of microRNA species from normal and malignant mammary epithelial cells. PLoS One. 2010; 5:e13515. PMID: 20976003.
Article
10. Kayala MA, Baldi P. Cyber-T web server: differential analysis of high-throughput data. Nucleic Acids Res. 2012; 40:W553–W559. PMID: 22600740.
Article
11. Frères P, Wenric S, Boukerroucha M, Fasquelle C, Thiry J, Bovy N, et al. Circulating microRNA-based screening tool for breast cancer. Oncotarget. 2016; 7:5416–5428. PMID: 26734993.
Article
12. Allaya N, Khabir A, Sallemi-Boudawara T, Sellami N, Daoud J, Ghorbel A, et al. Over-expression of miR-10b in NPC patients: correlation with LMP1 and Twist1. Tumour Biol. 2015; 36:3807–3814. PMID: 25597482.
Article
13. Chang YY, Kuo WH, Hung JH, Lee CY, Lee YH, Chang YC, et al. Deregulated microRNAs in triple-negative breast cancer revealed by deep sequencing. Mol Cancer. 2015; 14:36. PMID: 25888956.
Article
14. Song C, Zhang L, Wang J, Huang Z, Li X, Wu M, et al. High expression of microRNA-183/182/96 cluster as a prognostic biomarker for breast cancer. Sci Rep. 2016; 6:24502. PMID: 27071841.
Article
15. Calvano Filho CM, Calvano-Mendes DC, Carvalho KC, Maciel GA, Ricci MD, Torres AP, et al. Triple-negative and luminal A breast tumors: differential expression of miR-18a-5p, miR-17-5p, and miR-20a-5p. Tumour Biol. 2014; 35:7733–7741. PMID: 24810926.
Article
16. Matamala N, Vargas MT, González-Cámpora R, Miñambres R, Arias JI, Menéndez P, et al. Tumor microRNA expression profiling identifies circulating microRNAs for early breast cancer detection. Clin Chem. 2015; 61:1098–1106. PMID: 26056355.
Article
17. Ouyang M, Li Y, Ye S, Ma J, Lu L, Lv W, et al. MicroRNA profiling implies new markers of chemoresistance of triple-negative breast cancer. PLoS One. 2014; 9:e96228. PMID: 24788655.
Article
18. Mar-Aguilar F, Mendoza-Ramírez JA, Malagón-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; 34:163–169. PMID: 23334650.
Article
19. Li J, Song ZJ, Wang YY, Yin Y, Liu Y, Nan X. Low levels of serum miR-99a is a predictor of poor prognosis in breast cancer. Genet Mol Res. 2016; 15(3):gmr8338.
Article
20. Pan Y, Zhang J, Fu H, Shen L. miR-144 functions as a tumor suppressor in breast cancer through inhibiting ZEB1/2-mediated epithelial mesenchymal transition process. Onco Targets Ther. 2016; 9:6247–6255. PMID: 27785072.
Article
21. Madhavan D, Peng C, Wallwiener M, Zucknick M, Nees J, Schott S, et al. Circulating miRNAs with prognostic value in metastatic breast cancer and for early detection of metastasis. Carcinogenesis. 2016; 37:461–470. PMID: 26785733.
Article
22. Zhang G, Liu Z, Cui G, Wang X, Yang Z. MicroRNA-486-5p targeting PIM-1 suppresses cell proliferation in breast cancer cells. Tumour Biol. 2014; 35:11137–11145. PMID: 25104088.
Article
23. Zhu J, Zheng Z, Wang J, Sun J, Wang P, Cheng X, et al. Different miRNA expression profiles between human breast cancer tumors and serum. Front Genet. 2014; 5:149. PMID: 24904649.
Article
24. Si H, Sun X, Chen Y, Cao Y, Chen S, Wang H, et al. Circulating microRNA-92a and microRNA-21 as novel minimally invasive biomarkers for primary breast cancer. J Cancer Res Clin Oncol. 2013; 139:223–229. PMID: 23052693.
Article
25. Kodahl AR, Lyng MB, Binder H, Cold S, Gravgaard K, Knoop AS, et al. Novel circulating microRNA signature as a potential non-invasive multi-marker test in ER-positive early-stage breast cancer: a case control study. Mol Oncol. 2014; 8:874–883. PMID: 24694649.
Article
26. Shimomura A, Shiino S, Kawauchi J, Takizawa S, Sakamoto H, Matsuzaki J, et al. Novel combination of serum microRNA for detecting breast cancer in the early stage. Cancer Sci. 2016; 107:326–334. PMID: 26749252.
Article
27. Dai X, Fang M, Li S, Yan Y, Zhong Y, Du B. miR-21 is involved in transforming growth factor beta1-induced chemoresistance and invasion by targeting PTEN in breast cancer. Oncol Lett. 2017; 14:6929–6936. PMID: 29151919.
28. Doberstein K, Bretz NP, Schirmer U, Fiegl H, Blaheta R, Breunig C, et al. miR-21-3p is a positive regulator of L1CAM in several human carcinomas. Cancer Lett. 2014; 354:455–466. PMID: 25149066.
Article
29. Xia M, Li H, Wang JJ, Zeng HJ, Wang SH. MiR-99a suppress proliferation, migration and invasion through regulating insulin-like growth factor 1 receptor in breast cancer. Eur Rev Med Pharmacol Sci. 2016; 20:1755–1763. PMID: 27212167.
30. Fan T, Mao Y, Sun Q, Liu F, Lin JS, Liu Y, et al. Branched rolling circle amplification method for measuring serum circulating microRNA levels for early breast cancer detection. Cancer Sci. 2018; 109:2897–2906. PMID: 29981251.
Full Text Links
  • JBC
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr