Single-Cell Sequence and Machine Learning Identify a CD79A+B Cells-Related Transcriptional Signature for Predicting Clinical Outcomes and Immune Microenvironment in Breast Cancer

Objective: The aim of this study was to investigate the role and mechanism of CD79A + B cells in mediating the microenvironment of breast cancer and the relationship with the prognosis of breast cancer. Methods: Single-cell RNA sequencing and bulk RNA sequencing analysis were combined to annotate breast cancer cell subtypes, perform cell communication and trajectory analysis. CD79A-related signature was constructed by LASSO and multivariate Cox analysis. CD79A + B cell subsets in the tumor microenvironment were explored by immunoanalysis and multiple immunofluorescence analysis. Results: There were communication relationships between CD79A + B cells and multiple cell types. A prognostic risk signature containing 6 genes was constructed by combining the TCGA dataset. The immune profile analysis showed that the low-risk group showed a higher immune response. In addition, multiple immunofluorescence analysis showed an attraction between CD79A + B cells and tumor cells, and patients with high CD79A + B cells expression had significantly higher survival rates. Conclusion: This study comprehensively explored the heterogeneity of CD79A + B cells through transcriptome analysis and chromatin analysis, which contributes to an in-depth understanding of the function of CD79A + B cells in biological processes as well as the molecular mechanism of breast carcinogenesis, providing a theoretical basis for treatment and prevention.