Understanding the relationship between mutational processes and gene expression patterns is essential for gaining insights into tumor heterogeneity. In this study, we analyze single-cell RNA sequencing data from a breast cancer tumor to investigate associations between mutational signature exposures and gene expression profiles. We propose a scoring method that integrates principal component loadings, canonical correlation analysis (CCA) loadings, and signature contributions to quantify gene-signature associations. Enrichment analysis of the top-ranking genes reveals consistent involvement of extracellular matrix (ECM) receptor interaction, focal adhesion, and immunerelated pathways across multiple mutational signatures. These findings suggest that different mutational processes converge on pathways involved in cell adhesion, invasion, and immune modulation. Our approach demonstrates the utility of multivariate statistical methods combined with enrichment analysis to explore the transcriptional consequences of mutational processes in cancer at the single-cell level.