Integrative Bioinformatics and Statistical Approaches for Identifying Prognostic Biomarkers and Therapeutic Targets in Breast Cancer

Gangga Anuraga; Dito Anurogo; Fenny Fitriani; Hani Brilianti Rochmanto; Zulhan Widya Baskara

doi:10.29303/emj.v8i1.277

Authors

Gangga Anuraga Department of Statistics, Universitas PGRI Adi Buana, Indonesia
Dito Anurogo Faculty of Medicine and Health Sciences, Universitas Muhammadiyah Makassar, Indonesia
Fenny Fitriani Department of Statistics, Universitas PGRI Adi Buana, Indonesia
Hani Brilianti Rochmanto Department of Statistics, Universitas PGRI Adi Buana, Indonesia
Zulhan Widya Baskara Department of Statistics, Universitas Mataram, Indonesia

DOI:

https://doi.org/10.29303/emj.v8i1.277

Keywords:

Breast cancer, Prognostic biomarkers, Protein-protein interaction, Survival analysis, Cell cycle regulation

Abstract

Breast cancer is a leading cause of cancer-related mortality worldwide, necessitating the identification of reliable biomarkers for prognosis and targeted therapy. This study employed an integrative bioinformatics and statistical approach to analyze differentially expressed genes (DEGs) in breast cancer using datasets GSE70947 and GSE22820 from the gene expression omnibus (GEO). A protein-protein interaction (PPI) network was constructed to identify hub genes, followed by functional enrichment analysis to determine their biological significance. Survival analysis using the KMplot database revealed that CDC45, KIF2C, CCNB1, KIF4A, CENPE, CHEK1, KIF15, AURKB, NCAPG, and HJURP were significantly associated with poor prognosis. These genes were primarily enriched in cell cycle regulation, mitotic spindle organization, and DNA damage response, highlighting their role in tumor progression. Among them, CCNB1, CHEK1, and AURKB were strongly linked to cell cycle progression and checkpoint regulation, while KIF2C and CENPE played essential roles in mitotic division. High expression levels of these genes correlated with reduced overall survival, suggesting their potential as prognostic biomarkers and therapeutic targets in breast cancer.These discoveries help us better understand how breast cancer develops and point to potential targets for tailored treatments.

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