Research Papers:

Identification of breast cancer recurrence risk factors based on functional pathways in tumor and normal tissues

Xiujie Chen _, Lei Liu, Yunfeng Wang, Bo Liu, Diheng Zeng, Qing Jin, Mengjian Li, Denan Zhang, Qiuqi Liu and Hongbo Xie

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Oncotarget. 2017; 8:20679-20694. https://doi.org/10.18632/oncotarget.11557

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Xiujie Chen1,*, Lei Liu1,*, Yunfeng Wang1,*, Bo Liu1,*, Diheng Zeng1, Qing Jin1, Mengjian Li1, Denan Zhang1, Qiuqi Liu1, Hongbo Xie1

1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China

*These authors contributed equally to this work

Correspondence to:

Xiujie Chen, email: [email protected]

Keywords: breast cancer, normal tissue, tumor tissue, recurrence, risk factors

Received: September 30, 2015     Accepted: July 26, 2016     Published: August 23, 2016


The recurrence of breast cancer (BC) is a serious therapeutic problem, and the risk factors for recurrence urgently need to be identified. In this study, we examined the functional pathways in tumor and normal tissues to more comprehensively identify biomarkers for the risk of BC recurrence. We collected tumor and normal tissue gene expression profiles of recurrent BC patients and non-recurrent BC patients from the TCGA database.We derived an expression interval (mean ± 1.96SD) based on non-recurrent patients rather than a single value, such as a mean or median. If the expression of a gene was significantly different from its normal expression interval, it was considered a differentially expressed gene. Eight pathways that significantly distinguished recurrent and non-recurrent BC patients were obtained based on 65% accuracy, and these pathways were all associated with the immune response and sensitivity to drugs. The genes in these eight pathways were also used to analyze survival, and the significance level reached 0.003 in an independent dataset (p = 0.02 in tumor and p = 0.03 in normal tissue). Our results reveal that the integration of tumor and normal tissue functional analyses can comprehensively enhance the understanding of BC prognosis.

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