Identification and Validation of Centromere proteins Signatures as a Novel Prognostic Model for Breast Cancer

Background: Breast cancer (BC) has the highest incidence and the leading mortality rate among all the malignant tumors in women worldwide. Increasing evidence has indicated that Centromere protein family members (CENPs) are involved in cancer development and progression. However, the role and mechanism of CENPs in BC still have not been fully elucidated. Methods: In this study, comprehensive bioinformatics analysis was conducted with data from The Cancer Genome Atlas and Gene Expression Omnibus database (TCGA). R software, cBioPortal, TISIDB and the GSCA Lite online tool were utilized for bioinformatics analysis. Prognostic risk models were built using univariate Cox regression analysis, multivariate Cox regression analysis, and LASSO regression analysis. The CENPs prognostic model was constructed and validated using internal and external validation cohorts. Correlations between the prognostic signature and the tumor immune microenvironment and immune cell infiltrates were further explored. Finally, we investigated the function of the most significant gene CENPW in this signature using cell experiments. Results: Multiple CENPs are dysregulated in BC progression and involved in the biological processes related to BC development. High CENPA/CENPI/CENPN/CENPW expression in BC patients was associated with shorter OS, DSS and PFI. Secondly, we constructed a risk model containing 4 CENPs (CENPA/CENPI/CENPN/CENPW) by using univariate Cox analysis, LASSO regression analysis, and multivariate Cox regression analysis. The prognostic model distinguished the training data sets and the validation sets into different prognosis clusters. Patients in the high-risk group had worse prognosis than patients in the low-risk group. Moreover, GSEA showed that pathways associated with tumor progression were enriched in the high-risk group. The immune cell infiltrationanalysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. Further experiments proved that knocking down CENPW expression reduced the proliferation, migration and invasion of breast cancer cells. Conclusions: We identified a novel CENPs-related signature which could precisely predict the prognosis of breast cancer patients，and it would play an important role in the personalized precision therapy of patients with BC in the future.