Identifying Biomarkers for Prognostic Treatment in Breast Cancer through Bioinformatics Analysis

Abstract

Abstract Objective This study aims to identify biomarkers linked to breast cancer for potential treatment. Methods Three breast cancer gene microarrays were selected from the Gene Expression Omnibus (GEO) database, meeting specific criteria. Paired data analysis revealed shared Differentially Expressed Genes (DEGs) among them. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. A Protein-Protein Interaction (PPI) network was constructed using String databases and Cytoscape software to identify hub genes. These hub genes underwent analysis for differential expression, survival, and pathological presentation in various databases (UALCAN, Kaplan-Meier Plotter, and HPA(The Human Protein Atlas)). Results Integrated analysis yielded 202 shared DEGs, with 164 downregulated and 38 upregulated genes.Highlighted 10 hub genes associated with breast cancer: KIF20A, CCVB1, KIF2C, TTK, CCNA2, RRM2, TOP2A, CDK1, KIF4A and CACA8. Conclusion The study uncovers the roles of these hub genes in cancer growth and proliferation, particularly TTK's link to basal-like and triple-positive breast cancer.RRM2 exhibited significance in HER2-positive cases, while others were prominent in triple-negative breast cancer. Exploring these hub genes provides potential biomarkers and insights for breast cancer prognosis and treatment decisions.