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Biomarker discovery to improve prediction of breast cancer survival: using gene expression profiling, meta-analysis, and tissue validation

Authors Meng L, Xu Y, Xu C, Zhang W

Received 29 May 2016

Accepted for publication 10 August 2016

Published 11 October 2016 Volume 2016:9 Pages 6177—6185

DOI https://doi.org/10.2147/OTT.S113855

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Akshita Wason

Peer reviewer comments 3

Editor who approved publication: Dr Jianmin Xu


Liwei Meng, Yingchun Xu, Chaoyang Xu, Wei Zhang

Department of Breast and Thyroid Surgery, Shaoxing People’s Hospital, Shaoxing Hospital of Zhejiang University, Shaoxing, Zhejiang, People’s Republic of China


Purpose: Breast cancer is the leading cause of cancer death worldwide in women. The molecular mechanism for human breast cancer is unknown. Gene microarray has been widely used in breast cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis survival. So far, the valuable multigene signatures in clinical practice are unclear, and the biological importance of individual genes is difficult to detect, as the described signatures virtually do not overlap. Early prognosis of this disease, breast invasive ductal carcinoma (IDC) and breast ductal carcinoma in situ (DCIS), is vital in breast surgery.
Methods: Thus, this study reports gene expression profiling in large breast cancer cohorts from Gene Expression Omnibus, including GSE29044 (N=138) and GSE10780 (N=185) test series and four independent validation series GSE21653 (N=266), GSE20685 (N=327), GSE26971 (N=276), and GSE12776 (N=204). Significantly differentially expressed genes in human breast IDC and breast DCIS were detected by transcriptome microarray analysis.
Results: We created a set of three genes (MAMDC2, TSHZ2, and CLDN11) that were significantly correlated with disease-free survival of breast cancer patients using a univariate Cox regression model (significance level P<0.01) in a meta-analysis. Based on the risk score of the three genes, the test series patients could be separated into low-risk and high-risk groups with significantly different survival times. This signature was validated in the other three cohorts. The prognostic value of this three-gene signature was confirmed in the internal validation series and another four independent breast cancer data sets. The prognostic impact of one of the three genes, CLDN11, was confirmed by immunohistochemistry. CLDN11 was significantly overexpressed in human breast IDC as compared with normal breast tissues and breast DCIS.
Conclusion: Using novel gene expression profiling together with a meta-analysis validation approach, we have identified a three-gene signature with independent prognostic impact. Furthermore, CLDN11 may offer a biomarker to predict prognosis as well as a new target for prognostic and therapeutic intervention for human breast IDC.

Keywords: meta-analysis, biomarker, IDC

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