ArticleFrontiers in genetics2022
Identification of a prognostic risk-scoring model and risk signatures based on glycosylation-associated cluster in breast cancer.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Integrating machine learning and experimental validation identifies a post-translational modification gene signature for prognosis and treatment response in breast cancer.Scientific reports · 2025Article
- Identification of glycogene-based prognostic signature and validation of B3GNT7 as a potential biomarker and therapeutic target in breast cancer.Journal of cancer research and clinical oncology · 2023Article
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4 authors.
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Abstract
Breast cancer is a heterogeneous disease whose subtypes represent different histological origins, prognoses, and therapeutic sensitivity. But there remains a strong need for more specific biomarkers and broader alternatives for personalized treatment. Our study classified breast cancer samples from The Cancer Genome Atlas (TCGA) into three groups based on glycosylation-associated genes and then identified differentially expressed genes under different glycosylation patterns to construct a prognostic model. The final prognostic model containing 23 key molecules achieved exciting performance both in the TCGA training set and testing set GSE42568 and GSE58812. The risk score also showed a significant difference in predicting overall clinical survival and immune infiltration analysis. This work helped us to understand the heterogeneity of breast cancer from another perspective and indicated that the identification of risk scores based on glycosylation patterns has potential clinical implications and immune-related value for breast cancer.
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