Gao Shengnan, Wu Xinjie, Lou Xiaoying, Cui Wei
Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/ State Key Laboratory of Molecular Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
Front Genet. 2022 Oct 20;13:960567. doi: 10.3389/fgene.2022.960567. eCollection 2022.
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.
乳腺癌是一种异质性疾病，其亚型代表不同的组织学起源、预后和治疗敏感性。但仍迫切需要更具特异性的生物标志物和更广泛的个性化治疗替代方案。我们的研究基于糖基化相关基因将来自癌症基因组图谱（TCGA）的乳腺癌样本分为三组，然后在不同糖基化模式下鉴定差异表达基因，以构建一个预后模型。最终包含23个关键分子的预后模型在TCGA训练集以及测试集GSE42568和GSE58812中均表现出色。风险评分在预测总体临床生存和免疫浸润分析方面也显示出显著差异。这项工作帮助我们从另一个角度理解乳腺癌的异质性，并表明基于糖基化模式识别风险评分对乳腺癌具有潜在的临床意义和免疫相关价值。
