Integrated bioinformatic analysis of potential biomarkers of poor prognosis in triple-negative breast cancer

Abstract

Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease associated with late-stage diagnosis and high metastatic rates. However, a gene signature for reliable TNBC biomarkers is not available yet. We aimed to identify potential key genes and their association with poor prognosis in TNBC through integrated bioinformatics.Methods: Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database.Differentially expressed genes (DEGs) in TNBC vs. non-TNBC and TNBC vs. normal tissues were analyzed. Overlapping upregulated and downregulated DEGs were selected as inputs for Gene Ontology and pathway enrichment analyses using Metascape. Then, UALCAN and Kaplan-Meier plotter were employed to analyze the prognostic values of all overlapping DEGs. Results:We identified 21 upregulated and 24 downregulated overlapping DEGs in TNBC vs. non-TNBC and TNBC vs. normal breast tissue. The upregulated overlapping DEGs were mainly enriched in various pathways including chromosome segregation, cell cycle phase transition, and cell division, whereas overlapping DEGs were significantly downregulated in pathways, such as multicellular organismal homeostasis, tissue homeostasis, and negative regulation of cell population proliferation. Key genes were identified by association with poor overall survival (OS). Our results showed that high expression of CENPW and HORMAD1 was associated with poor OS of TNBC patients. Conversely, the low expression of PIP, APOD, and ZNF703 indicated worse OS. Conclusions:We identified key genes (CENPW, HORMAD1, APOD, PIP, and ZNF703) associated with poor OS. Thus, these genes might serve as candidate prognostic markers for TNBC.

Overview of triple negative breast cancer prognostic signatures in the context of data science-driven clinico-genomics research

“…Key DEGs ( CENPW , HORMAD1 , APOD , PIP , and ZNF703 ) were found as candidate prognostic markers associated with poor OS through integrated bioinformatics performed on GEO microarray TNBC data ( 26 ). Network analysis focused on 147 co-DEGs in TNBC vs non-cancerous tissue samples, identifying a 15-gene signature inclusive of BUB1 and CENPF significantly correlated with OS, while BUB1 , CCNA2 , and PACC1 showed significant poor DFS ( 27 ).…”