Single Nucleotide Polymorphisms Reveal Tumor SizeDependent Mutation Patterns in Breast Cancer

Breast cancer treatment is often complicated by the intricate dynamics of gene mutations, which vary across different tumor sizes. Understanding these variations is essential for the development of targeted therapies and the improvement of patient outcomes. In this study, we conducted a comprehensive analysis of gene variant counts and mutation rates in breast cancer tumors, categorized by size into three groups: 0-49 mm, 50- 99 mm, and 100-149 mm. Genomic data were systematically extracted and analyzed to identify key genes with the highest variant counts and to calculate mutation rates normalized per 1,000 observations. Data visualization techniques were employed to elucidate trends in mutation frequencies across the different tumor sizes. Our findings identified PARP1P1 as a gene with consistently high variant counts and mutation rates across all tumor sizes, indicating its potential as a robust biomarker for cancer progression. Conversely, CCDC7, DLGAP1, and SYN3 exhibited higher mutation rates predominantly in smaller tumors, suggesting their involvement in the early stages of tumorigenesis. These results underscore the critical importance of gene-specific mutation patterns in breast cancer, with PARP1P1 emerging as a gene of significant clinical relevance. Additionally, the sizedependent mutation patterns of CCDC7, DLGAP1, and SYN3 emphasize the need for early detection and the development of targeted interventions. Future research should aim to validate these findings in larger patient cohorts and investigate the functional implications of these mutations to further advance precision medicine in breast cancer treatment.