Comprehensive single-cell analysis of triple-negative breast cancer based on cDC1 immune-related genes: prognostic model construction and immunotherapy potential.

BACKGROUND

Various components of the immunological milieu surrounding tumors have become a key focus in cancer immunotherapy research. There are currently no reliable biomarkers for triple-negative breast cancer (TNBC), leading to limited clinical benefits. However, some studies have indicated that patients with TNBC may achieve better outcomes after immunotherapy. Therefore, this study aimed to identify molecular features potentially associated with conventional type 1 dendritic cell (cDC1) immunity to provide new insights into TNBC prognostication and immunotherapy decision-making.

METHODS

Single-cell ribonucleic acid sequencing data from the Gene Expression Omnibus database were analyzed to determine which genes are differentially expressed genes (DEGs) in cDC1s. We then cross-referenced cDC1-related DEGs with gene sets linked to immunity from the ImmPort and InnateDB databases to screen for the genes linked to the immune response and cDC1s. We used univariate Cox and least absolute shrinkage and selection operator regression analyses to construct a risk assessment model based on four genes in patients with TNBC obtained from the Cancer Genome Atlas, which was validated in a testing group. This model was also used to assess immunotherapy responses among the IMvigor210 cohort. We subsequently utilized single sample Gene Set Enrichment Analysis, CIBERSORT, and ESTIMATE to analyze the immunological characteristics of the feature genes and their correlation with drug response.

RESULTS

We identified 93 DEGs related to the immune response and cDC1s, of which four (IDO1, HLA-DOB, CTSD, and IL3RA) were substantially linked to the overall survival rate of TNBC patients. The risk assessment model based on these genes stratified patients into high- and low-risk groups. Low-risk patients exhibited enriched ''hot tumor'' phenotypes, including higher infiltration of memory-activated CD4 + T cells, CD8 + T cells, gamma delta T cells, and M1 macrophages, as well as elevated immune checkpoint expression and tumor mutational burden, suggesting potential responsiveness to immunotherapy. Conversely, high-risk patients displayed "cold tumor" characteristics, with higher infiltration of M0 and M2 macrophages and lower immune scores, which may be poorer in response to immunotherapy. However, experimental validation and larger clinical studies are necessary to confirm these findings and explore the underlying mechanisms of the identified genes.

CONCLUSION

This study developed a robust risk assessment model using four genes that effectively forecast the outcome of patients with TNBC and have the potential to guide immunotherapy. This model provided new theoretical insights for knowing the TNBC immune microenvironment and developing personalized treatment strategies.