Abstract
PURPOSE: Breast cancer (BRCA) is a prevalent tumor worldwide. The association between the coiled-coil domain-containing (CCDC) protein family and different tumors has been established. However, the prognostic significance of this protein family in breast cancer remains uncertain. METHODS: Gene expression and clinical data were obtained from the TCGA, METABRIC, and GEO databases. Prognosis genes were identified using univariate Cox and LASSO Cox regression, leading to the establishment of a prognostic signature. Subsequently, the risk model was conducted based on survival and clinical feature analyses, and a nomogram for prognosis prediction was developed. Furthermore, analyses of biological function, immune characteristics, and drug sensitivity were performed. Finally, single-cell sequencing data were utilized to uncover the expression patterns of genes in the risk model. RESULTS: Five genes were identified and utilized for risk modeling. The model demonstrated excellent prognostic value as indicated by ROC and Kaplan-Meier analysis. The high-risk group exhibited shorter survival time and higher likelihood of recurrence. Functional annotation indicated a correlation between the risk score and immune pathways. Conversely, the low-risk group displayed a greater enrichment in immune pathways and exhibited more active immune microenvironment characteristics. Additionally, drug sensitivity analysis using both public and our sequencing data revealed that the risk model possessed a broad range of predictive values. CONCLUSIONS: We have developed a gene signature and have verified that patients with low-risk are more likely to have better prognosis and respond positively to therapy. This finding offers a valuable point of reference for BRCA individualized treatment.