Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis

Abstract

Triple‑negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) analysis based on genome‑wide gene expression datasets involving TNBC patients from the Gene Expression Omnibus (GEO) database was performed to identify key genes associated with TNBC. A total of 194 highly ranked differentially expressed genes (DEGs) were identified in TNBC vs. non‑TNBC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was utilized to explore the biological functions of the identified genes. These DEGs were mainly involved in the biological processes termed positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptotic process, response to drug, response to estradiol and negative regulation of cell growth. Genes were mainly involved in the KEGG pathway termed estrogen signaling pathway. The aberrant expression of several randomly selected DEGs were further validated in cell lines, clinical tissues and The Cancer Genome Atlas (TCGA) cohort. Furthermore, all the top‑ranked DEGs underwent survival analysis using TCGA database, of which overexpression of 4 genes (FABP7, ART3, CT83, and TTYH1) were positively correlated to the life expectancy (P<0.05) of TNBC patients. In addition, a model consisting of two genes (FABP7 and CT83) was identified to be significantly associated with the overall survival (OS) of TNBC patients by means of Cox regression, Kaplan‑Meier, and receiver operating characteristic (ROC) analyses. In conclusion, the present study identified a number of key genes as potential biomarkers involved in TNBC, which provide novel insights into the tumorigenesis of TNBC at the gene level and may serve as independent prognostic factors for TNBC prognosis.

Keywords: triple-negative breast cancer; robust rank aggregation; biomarker; prognostic risk model; differentially expressed gene.

Figure 1.

Top 20 upregulated and downregulated genes in TNBC vs. non-TNBC. The red indicates increased gene expression, whereas the green indicates decreased gene expression; the number exhibited in the figure indicates the logarithmic fold change of genes in each dataset. TNBC, triple-negative breast cancer.

Figure 2.

GO analysis of DEGs identified by the RRA method. (A) Enriched BP of these genes; (B) enriched MF of these genes; (C) enriched CC of these genes. DEGs, differentially expressed genes; RRA robust rank aggregation; BP, biological processes; MF, molecular functions; CC, cellular components.

Figure 3.

Differentially expressed genes in the estrogen signaling pathway. Red, upregulated genes; green, downregulated genes.

Figure 4.

Validation of six randomly selected DEGs through RT-qPCR. (A) Expression of ART3, FABP7, HORMAD1, TFF1, AGR2 and FOXA1 in TNBC cell lines compared with non-TNBC cell lines. (B) Expression of ART3, FABP7, HORMAD1, TFF1, AGR2 and FOXA1 in TNBC patients compared with non-TNBC patients. *P<0.05. DEGs, differentially expressed genes; ART3, ADP-ribosyltransferase 3; FABP7, fatty acid binding protein 7; HORMAD1, HORMA domain containing 1; TFF1, trefoil factor1; AGR2, anterior gradient 2; FOXA1, forkhead box A1; TNBC, triple-negative breast cancer.

Figure 5.

Kaplan-Meier estimates of the OS of TNBC patients in TCGA cohort using a two-gene signature. A log-rank test was conducted to evaluate the survival differences between the two curves. OS, overall survival; TNBC, triple-negative breast cancer; TCGA, The Cancer Genome Atlas.

Figure 6.

ROC curve analysis reveals the sensitivity and specificity of a two-gene signature in predicting the OS of patients. ROC, receiver operating characteristic; OS, overall survival.