The N6-methyladenosine methylation landscape stratifies breast cancer into two subtypes with distinct immunological characteristics

IF 2.9 4区 医学 Q2 Medicine
Yang Chen, Yijiang Hou, Shuguang Li, Wenxing Qin, Jian Zhang
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Abstract

N6-methyladenosine (m6A) methylation modification affects the tumorigenesis and metastasis of breast cancer (BC). This study investigated the association between m6A regulator-mediated methylation modification patterns and characterization of the tumour microenvironment in BC, as well as their prognostic importance. Public gene expression data and clinical annotations were collected from The Cancer Genome Atlas (TCGA) database, the Gene Expression Omnibus website and the METABRIC program. We analysed the genetic expression, gene–gene interactions, gene mutations and copy number variations using R software. The data were screened for risk genes using the Cox risk regression model, and we developed an algorithm for risk score and its predictive value. Compared to adjacent normal tissue, we identified 16 differentially expressed m6A regulators in BC, including six writers and 10 readers. Under unsupervised clustering, two distinguished modification patterns were identified, cluster C1 and C2. Compared to m6A cluster C2, cluster C1 was found to be more involved in immune-related pathways, with a relatively higher immune score and stromal score (P < 0.05). Patients were divided into two groups based on their risk scores for survival analysis. The patients in the high-risk score group had significantly worse overall survival than patients in the low-risk score group, (P < 0.0001). The TCGA database validation revealed the same prognostic tendency. In summary, our study showed distinct m6A regulator modification patterns contribute to the immunological heterogeneity and diversity of BC. The development of m6A gene signatures and the m6A score aid in the prognostic prediction of patients with BC.

N6-甲基腺苷甲基化图谱将乳腺癌分为两种具有不同免疫学特征的亚型。
N6-甲基腺苷(m6A)甲基化修饰影响乳腺癌(BC)的肿瘤发生和转移。本研究探讨了m6A调节因子介导的甲基化修饰模式与乳腺癌肿瘤微环境特征之间的关联及其对预后的重要性。我们从癌症基因组图谱(TCGA)数据库、基因表达总库网站和METABRIC程序中收集了公开的基因表达数据和临床注释。我们使用 R 软件分析了基因表达、基因与基因之间的相互作用、基因突变和拷贝数变异。我们使用 Cox 风险回归模型对数据进行了风险基因筛选,并开发了风险评分算法及其预测价值。与邻近的正常组织相比,我们在 BC 中发现了 16 个差异表达的 m6A 调节因子,包括 6 个写入因子和 10 个读出因子。在无监督聚类下,我们发现了两种不同的修饰模式,即C1群和C2群。与 m6A C2 群相比,C1 群更多地参与免疫相关通路,免疫得分和基质得分相对更高(P
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
128
审稿时长
6 months
期刊介绍: Clinical and Experimental Pharmacology and Physiology is an international journal founded in 1974 by Mike Rand, Austin Doyle, John Coghlan and Paul Korner. Our focus is new frontiers in physiology and pharmacology, emphasizing the translation of basic research to clinical practice. We publish original articles, invited reviews and our exciting, cutting-edge Frontiers-in-Research series’.
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