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Single-Cell Sequence and Machine Learning Identify a CD79A+B Cells-Related Transcriptional Signature for Predicting
Haihong Hu1,2, Wendi Zhan1,2, Hongxia Zhu1,2
1Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hunan, China.
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.
