Bioinformatics-Driven Investigations of Signature Biomarkers for Triple-Negative Breast Cancer

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Breast cancer is a heterogeneous disease with significant global health impact.
• Personalized therapies are emerging due to advancements in prognostic biomarkers.
• Triple-negative breast cancer (TNBC) requires further investigation for targeted treatments.

Purpose of the Study:

• To identify critical signature genes and signaling pathways in triple-negative breast cancer.
• To utilize a bioinformatics approach for analyzing gene expression data.
• To discover potential biomarkers for improved breast cancer patient outcomes.

Main Methods:

• Analysis of microarray dataset GSE65194 from NCBI Gene Expression Omnibus.
• Identification of differentially expressed genes (DEGs) using R software.
• Gene ontology and KEGG pathway enrichment analysis via ClueGO in Cytoscape.
• Protein-protein interaction (PPI) network analysis using cytoHubba to identify hub genes.

Main Results:

• Up-regulated DEGs are associated with cell cycle regulation and spindle assembly checkpoint.
• Down-regulated DEGs are linked to altered signaling pathways and metabolic reprogramming.
• Key hub genes were identified and validated as potential prognostic biomarkers.

Conclusions:

• Bioinformatics analysis successfully identified key molecular players in triple-negative breast cancer.
• The identified hub genes show potential as signature biomarkers for personalized breast cancer therapy.
• This study provides a foundation for developing targeted treatment strategies for TNBC.