Integrated multi-omics approach to identify novel breast cancer biomarkers in the MTHFR gene 2767

Abstract Description Breast cancer is the second most common cancer among women in the United States. Enhancing early detection methods through biomarkers offers improved accuracy and non-invasive diagnostic options. This study investigates the MTHFR gene, which encodes an enzyme essential for amino acid processing, DNA synthesis, repair, and methylation—processes that may influence breast cancer cell proliferation. Using a logistic regression model, we identified cancer-associated SNPs, gene expression levels, and methylation patterns within the MTHFR gene. Multi-omics data integration, including epigenomic, transcriptomic, and SNP datasets, was performed on breast cancer and normal samples to pinpoint potential biomarker sites. Experimental validation was conducted using qPCR and sequencing. Our findings suggest that specific SNPs, gene expression levels, and methylation sites within the MTHFR gene may serve as novel biomarkers, supporting improved early detection strategies and non-invasive diagnostics for breast cancer. Topic Categories Computational and Systems Immunology (COMP)