The Prediction Model for Triple-Negative Breast Cancer Prognosis and Immunotherapy Efficacy Based on Single-Cell Sequencing of CD8+ T cells.

Triple-negative breast cancer (TNBC) exhibits a higher propensity for recurrence, distant metastasis, and mortality than the other subtypes of breast cancer. TNBC is primarily attributed to the lack of expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Single-cell sequencing results of CD8 T cells in TNBC patients were screened for differentially expressed and immune-related genes. The selected genes were then analyzed with immunohistochemistry for their prognostic effects. Additionally, a regression model was constructed to ascertain the gene expression score and classify patients into high- and low-risk groups. We further analyzed the impact of gene expression on prognosis based on risk grouping and evaluated its potential as a prognostic predictor for TNBC patients. This analysis was validated using PCR and the prognostic data from patient samples. We also investigated the effect of risk grouping on immunotherapy in TNBC patients and evaluated its potential to predict the efficacy of immunotherapy in TNBC patients. Single-cell sequencing of CD8 T cells from TNBC patients identified 191 differentially expressed genes. Among them, XCL1, RASGRP1, CTSD, and AIP were reported to be independent prognostic factors for TNBC. The results were verified through immunohistochemistry. Additionally, a regression analysis model was constructed using these four genes to classify patients into risk groups. The high-risk group correlated with a poor prognosis in patients and could serve as an independent prognostic factor for TNBC. The results were further validated through PCR. Notably, patients in the low-risk group displayed a better response to immunotherapy. Based on the single-cell sequencing results of CD8 T cells from TNBC patients, a prediction model was established, which facilitated prognosis prediction in TNBC patients and evaluated the patients' response to immunotherapy. In summation, this model could potentially assist in improving the efficacy of TNBC immunotherapy.