Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Breast cancer is a leading cause of cancer death globally, particularly among females.
• Identifying novel biomarkers for prognosis and targeted therapy is crucial for improving patient outcomes.

Purpose of the Study:

• To identify differentially expressed genes (DEGs) associated with poor prognosis in breast cancer using bioinformatics.
• To discover potential therapeutic targets for breast cancer treatment.

Main Methods:

• Utilized GEO datasets (GSE86374, GSE5364, GSE70947) to identify DEGs between tumor and normal tissues.
• Performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis.
• Constructed protein-protein interaction (PPI) networks and identified core genes using STRING and MCODE in Cytoscape.
• Validated prognostic significance of core genes using UALCAN, GEPIA, and Kaplan-Meier plotter.

Main Results:

• Identified 96 upregulated and 98 downregulated DEGs.
• Selected 55 hub genes from the PPI network.
• Five core genes (KIF4A, RACGAP1, CKS2, SHCBP1, HMMR) were highly expressed in breast cancer tissues and correlated with poor prognosis.
• These genes showed differential expression across breast cancer subtypes.

Conclusions:

• The five identified genes (KIF4A, RACGAP1, CKS2, SHCBP1, HMMR) show potential as biomarkers for predicting breast cancer prognosis.
• These genes represent promising therapeutic targets for breast cancer intervention.