Abstract
Background: Aberrant glycosylation is significantly related to the occurrence, progression, metastasis, and drug resistance of tumors. It is essential to identify glycosylation and related genes with prognostic value for breast cancer. Objective: We aimed to construct and validate a prognostic model based on glycosylation and related genes, and further investigate its prognosis values in validation set and external independent cohorts. Materials and Methods: The transcriptome and clinical data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA, n = 1072), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC, n = 1451), and GSE2741 (n = 120). Glycosylation-related genes were downloaded from the Genecards website. Differentially expressed glycosylation-related geneswere identified by comparing the tumor tissues with the adjacent tissues. The TCGA data were randomly divided into training set and validation set in a 1:1 ratio for further analysis. The glycosylation risk-scoring prognosis model was constructed by univariate and multivariate Cox regression analysis, followed by confirmation in TCGA validation, METABRIC, and GEO datasets. Gene set enrichment analysis (GSEA) and Gene ontology analysis for identifying the affected pathways in the high- and low-risk groups were performed. Results: We attained 1072 breast cancer samples from the TCGA database and 786 glycosylation genes from the Genecards website. A signature contains immunoglobulin, glycosylation and anti-viral related genes was constructed to separate BRCA patients into two risk groups. Low-risk patients had better overall survival than high-risk patients (p < 0.001). A nomogram was constructed with risk scores and clinical characteristics. The area under time-dependent ROC curve reached 0.764 at 1 year, 0.744 at 3 years, and 0.765 at 5 years in the training set. Subgroup analysis showed differences in OS between the high- and low-risk patients in different subgroups. Moreover, the risk score was confirmed as an independent prognostic indicator of BRCA patients and was potentially correlated with immunotherapy response and drug sensitivity. Conclusion: We identified a novel signature integrated of immunoglobulin (IGHA2), glycosylation-related (SLC35A2) and anti-viral gene (BST2) that was an independent prognostic indicator for BRCA patients. The risk-scoring model could be used for predicting prognosis and immunotherapy in BRCA, thus providing a powerful instrument for combating BRCA.