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

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

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 Li  3 , Hong Xu  3 , Minjie Wei  1   2
Affiliations

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

Xuemei Lv et al. Cancer Cell Int. .

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.

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Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Identification of differentially expressed genes (DEGs) and Venn diagram of DEGs in triple-negative breast cancer (TNBC). Volcano plot of all genes a between 1109 breast cancer tissue samples and 113 normal tissue samples, and b between 117 TNBC and 970 non-TNBC breast cancer samples from TCGA database. Red dots represent upregulated genes, and green dots represent downregulated genes. c Venn diagram for overlapping upregulated genes and downregulated genes in the two sets. T: Tumour; N: normal
Fig. 2
Fig. 2
Top 10 functional enrichment analyses of the overlapping DEGs. a GO: Gene ontology; BP: biological process. b MF: molecular function. c CC: cellular component. d KEGG: Kyoto Encyclopedia of Genes and Genomes
Fig. 3
Fig. 3
PPI network of DEGs. DEGs, differentially expressed genes; PPI: protein–protein interaction
Fig. 4
Fig. 4
The module identified in the PPI network of the DEGs. a A significant module selected from the PPI network. b Functional and pathway enrichment analysis of the DEGs in the module
Fig. 5
Fig. 5
Expression of the eight hub genes correlated with the cell cycle in TNBC (TCGA dataset). Expression values of genes are log2-transformed
Fig. 6
Fig. 6
Prognostic gene signature of the six genes in 117 TNBC patients. a Risk score distribution; b patients’ survival status distribution; c Kaplan–Meier curves for low-risk and high-risk groups; d time-dependent ROC curves for predicting OS in TNBC patients by the risk score; e expression of the six genes in low- and high-risk groups (TCGA dataset). Gene expression values are log2-transformed
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