Abstract
Background: Breast cancers characterized by triple-negative status tend to be more malignant and have a poorer prognosis. A risk model for predicting breast cancer risk should be developed. Methods: We obtained gene expression and clinical characteristics data using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) database. Differential gene screening between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancers (NTNBC) was performed according to the "edgeR" filter criteria. Univariate and multivariate Cox regression analyses were used to construct a risk model and identify prognosis-related genes. XCELL, TIMER, EPIC, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT software programs were used to determine the extent of tumor immune cell infiltration. To evaluate the clinical responses to breast cancer treatment, the half maximal inhibitory concentration (IC50s) of common chemotherapeutics were calculated using "pRRophetic" and "ggplot2". Cell proliferation was assayed using cell counting kit-8 (CCK8) and 5-Ethynyl-2'-deoxyuridine (EdU) Cell Proliferation Kit. A dual-luciferase reporter assay confirmed the gene regulatory relationship of sex determining region Y-box 10 (SOX10). Results: An assessment model was established for Keratin23 (KRT23) and non-specific cytotoxic cell receptor 1 (NCCRP1) using the univariate and multivariate Cox regression analyses. In addition, high expression levels of KRT23 and NCCRP1 indicated high proliferation and poor prognosis. We also found that the gene expression patterns of multiple genes were significantly more predictive of risks and have a higher level of consistency when assessing risk. In vitro experiments showed that the expressions of KRT23 and NCCRP1 were increased in TNBCs and promoted cell proliferation. Mechanically, the dual-luciferase reporter assay confirmed that SOX10 regulated the expressions of KRT23 and NCCRP1. The risk score model revealed a close relationship between the expressions of KRT23 and NCCRP1, the tumor immune microenvironment, and chemotherapeutics. Conclusions: In conclusion, we constructed a risk assessment model to predict the risk of TNBC patients, which acted as a potential predictor for chemosensitivity.