Prognostic model based on tumor stemness genes for triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive disease with a poor prognosis and lack of effective treatment. In this study, TNBCs were analyzed from the perspective of tumor stemness based on scRNA-seq data. The analysis showed that tumor cells of TNBC were divided into 4 subtypes, with subtype 2 having the highest stemness score. A prognostic model of 7 tumor stemness-related genes (AP2S1, CHML, FABP7, FADS2, PAXX, SDC1 and TOP2A) was developed based on marker genes of this subtype and TCGA data, and the predictive power of this feature was well validated in different clinical subgroups. TNBC patients in the low TS group had a better prognosis. In addition, drug sensitivity analysis showed that patients in the high TS (tumor stemness) score group were more sensitive to PD-L1 inhibitors and the chemotherapeutic agents. In conclusion, our study developed a prognostic model based on TNBC tumor stemness cell marker genes, which has a good ability to predict the prognosis of TNBC patients and the effect of response to drug therapy.