The cross-talk of cancer-associated fibroblasts assist in prognosis and immunotherapy in patients with breast carcinoma.

The association between cancer-associated fibroblasts (CAFs) and tumor microenvironment (TME) is a key factor in promoting tumor progression. However, the correlation between CAFs and TME in breast carcinoma has not been elucidated. Thus, further study about the cross-effect between CAFs and TME can provide novel strategies for breast carcinoma treatment, particularly targeted immunotherapy. First, we systematically analyzed cell communication in a single-cell dataset and identified the interacted genes between CAFs and TME components. Then, a robust fibroblast-related score (FRS) model was developed using the LASSO algorithm. The FRS can be a reliable adverse prognostic factor in three cohorts with breast carcinoma. Functional enrichment analysis and single-sample Gene Set Enrichment Analysis showed that patients with a high FRS had cold tumors with active proliferation and immunosuppression. Patients with a low FRS presented with hot tumors with active immune and cell-killing functions. Genomic variation analysis revealed that patients with a low FRS had a higher somatic mutation load and copy number variation burden. Finally, patients with a low FRS were more sensitive to chemotherapy and immunotherapy, particularly anti-PD-1 therapy. In conclusion, a reliable FRS model was constructed not only reliable for predicting prognosis but also competent to estimate clinical immunotherapy and chemotherapy response for patients with BRCA, which might provide significant clinical implications for guiding clinical decision-making for patients with BRCA.