An analysis of the prognostic role of reactive oxygen species-associated genes in breast cancer

Area of Science:

• Oncology
• Genomics
• Immunology

Background:

• Breast cancer exhibits heterogeneity influenced by reactive oxygen species (ROS), genetic mutations, and immune infiltration.
• Understanding these factors is crucial for predicting treatment response and patient prognosis.

Purpose of the Study:

• To classify breast cancer subtypes based on ROS, clinical indicators, single nucleotide variant (SNV) mutations, and immune infiltration.
• To develop a prognostic model for breast cancer risk stratification and personalized therapy.

Main Methods:

• Utilized The Cancer Genome Atlas (TCGA) data for clustering analysis and identification of differentially expressed ROS-related genes.
• Integrated clinical data, SNV analysis, gene set enrichment analysis (GSEA), KEGG pathway analysis, and immune cell infiltration assessment.
• Developed a prognostic model using survival data and validated key gene expression differences via RT-qPCR.

Main Results:

• Identified two distinct breast cancer subtypes with significant variations in ROS gene expression, clinical features, SNV profiles, and immune microenvironment.
• The prognostic model, based on 16 survival-associated genes, effectively stratified patients into high- and low-risk groups.
• High-risk patients demonstrated poorer prognosis, higher tumor purity, distinct immune profiles, and reduced response to immunotherapy.

Conclusions:

• The study reveals significant molecular and immunological heterogeneity in breast cancer, aiding prognosis.
• The developed prognostic model offers a valuable tool for risk stratification and guiding personalized treatment decisions.
• Findings provide insights for optimizing immunotherapy and chemotherapy strategies in clinical settings.