Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer

Xuemei Lv; Miao He; Yanyun Zhao; Liwen Zhang; Wenjing Zhu; Longyang Jiang; Yuanyuan Yan; Yue Fan; Hongliang Zhao; Shuqi Zhou; Heyao Ma; Yezhi Sun; Xiang Li; Hong Xu; Minjie Wei

doi:10.1186/s12935-019-0884-0

Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer

Cancer Cell Int. 2019 Jun 28:19:172. doi: 10.1186/s12935-019-0884-0. eCollection 2019.

Authors

Xuemei Lv^{1

2}, Miao He^{1

2}, Yanyun Zhao^{1

2}, Liwen Zhang^{1

2}, Wenjing Zhu^{1

2}, Longyang Jiang^{1

2}, Yuanyuan Yan^{1

2}, Yue Fan^{1

2}, Hongliang Zhao^{1

2}, Shuqi Zhou^{1

2}, Heyao Ma^{1

2}, Yezhi Sun^{1

2}, Xiang Li³, Hong Xu³, Minjie Wei^{1

2}

Affiliations

¹ 1Department of Pharmacology, School of Pharmacy, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, 110122 Liaoning Province People's Republic of China.
² Liaoning Key Laboratory of Molecular Targeted Anti-tumour Drug Development and Evaluation, Shenyang, China.
³ 3Department of Breast Cancer, Cancer Hospital of China Medical University, No. 44, Xiaoheyan Road, Dadong District, Shenyang, 110042 China.

Abstract

Background: Triple negative breast cancer (TNBC) is a specific subtype of breast cancer with a poor prognosis due to its aggressive biological behaviour and lack of therapeutic targets. We aimed to explore some novel genes and pathways related to TNBC prognosis through bioinformatics methods as well as potential initiation and progression mechanisms.

Methods: Breast cancer mRNA data were obtained from The Cancer Genome Atlas database (TCGA). Differential expression analysis of cancer and adjacent cancer, as well as, triple negative breast cancer and non-triple negative breast cancer were performed using R software. The key genes related to the pathogenesis were identified by functional and pathway enrichment analysis and protein-protein interaction network analysis. Based on univariate and multivariate Cox proportional hazards model analyses, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic performance of our model.

Results: Based on mRNA expression profiling of breast cancer patients from the TCGA database, 755 differentially expressed overlapping mRNAs were detected between TNBC/non-TNBC samples and normal tissue. We found eight hub genes associated with the cell cycle pathway highly expressed in TNBC. Additionally, a novel six-gene (TMEM252, PRB2, SMCO1, IVL, SMR3B and COL9A3) signature from the 755 differentially expressed mRNAs was constructed and significantly associated with prognosis as an independent prognostic signature. TNBC patients with high-risk scores based on the expression of the 6-mRNAs had significantly shorter survival times compared to patients with low-risk scores (P < 0.0001).

Conclusions: The eight hub genes we identified might be tightly correlated with TNBC pathogenesis. The 6-mRNA signature established might act as an independent biomarker with a potentially good performance in predicting overall survival.

Keywords: Differentially expressed genes; Pathogenesis; Prognostic; Survival; Triple-negative breast cancer; mRNA-signature.