Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.

Area of Science:

• Oncology
• Bioinformatics
• Computational Biology

Background:

• Breast cancer is a diverse global health issue requiring advanced analytical strategies.
• The Cancer Genome Atlas (TCGA) provides extensive genomic data crucial for understanding cancer complexity.

Purpose of the Study:

• To leverage machine learning and bioinformatics for breast cancer staging, classification, and diagnosis.
• To identify molecular signatures and potential biomarkers associated with breast cancer subtypes and stages.

Main Methods:

• Utilized The Cancer Genome Atlas (TCGA) gene expression data.
• Applied machine learning algorithms (Random Forest, XGBoost) and systems biology techniques.
• Analyzed differentially expressed genes, signaling pathways, protein-protein interactions, and regulatory networks.

Main Results:

• Identified specific proteins (MYH2, MYL1, MYL2, MYH7) and microRNAs (hsa-let-7d-5p) as potential biomarkers for cancer progression.
• Achieved high diagnostic accuracy for cancer staging: Random Forest at 97.19% and XGBoost at 95.23%.

Conclusions:

• The integration of bioinformatics and machine learning offers a powerful approach to discovering breast cancer biomarkers.
• This methodology enhances the understanding of breast cancer complexity and improves clinical outcomes in diagnosis and categorization.