Chen Yong, Xia Fada, Jiang Bo, Wang Wenlong, Li Xinying
Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China.
Front Med (Lausanne). 2021 Aug 26;8:674338. doi: 10.3389/fmed.2021.674338. eCollection 2021.
Epigenetic regulation, including DNA methylation, plays a major role in shaping the identity and function of immune cells. Innate and adaptive immune cells recruited into tumor tissues contribute to the formation of the tumor immune microenvironment (TIME), which is closely involved in tumor progression in breast cancer (BC). However, the specific methylation signatures of immune cells have not been thoroughly investigated yet. Additionally, it remains unknown whether immune cells-specific methylation signatures can identify subgroups and stratify the prognosis of BC patients. DNA methylation profiles of six immune cell types from eight datasets downloaded from the Gene Expression Omnibus were collected to identify immune cell-specific hypermethylation signatures (IC-SHMSs). Univariate and multivariate cox regression analyses were performed using BC data obtained from The Cancer Genome Atlas to identify the prognostic value of these IC-SHMSs. An unsupervised clustering analysis of the IC-SHMSs with prognostic value was performed to categorize BC patients into subgroups. Multiple Cox proportional hazard models were constructed to explore the role of IC-SHMSs and their relationship to clinical characteristics in the risk stratification of BC patients. Integrated discrimination improvement (IDI) was performed to determine whether the improvement of IC-SHMSs on clinical characteristics in risk stratification was statistically significant. A total of 655 IC-SHMSs of six immune cell types were identified. Thirty of them had prognostic value, and 10 showed independent prognostic value. Four subgroups of BC patients, which showed significant heterogeneity in terms of survival prognosis and immune landscape, were identified. The model incorporating nine IC-SHMSs showed similar survival prediction accuracy as the clinical model incorporating age and TNM stage [3-year area under the curve (AUC): 0.793 vs. 0.785; 5-year AUC: 0.735 vs. 0.761]. Adding the IC-SHMSs to the clinical model significantly improved its prediction accuracy in risk stratification (3-year AUC: 0.897; 5-year AUC: 0.856). The results of IDI validated the statistical significance of the improvement ( < 0.05). Our study suggests that IC-SHMSs may serve as signatures of classification and risk stratification in BC. Our findings provide new insights into epigenetic signatures, which may help improve subgroup identification, risk stratification, and treatment management.
表观遗传调控，包括DNA甲基化，在塑造免疫细胞的特性和功能方面起着主要作用。招募到肿瘤组织中的先天性和适应性免疫细胞有助于肿瘤免疫微环境（TIME）的形成，而肿瘤免疫微环境与乳腺癌（BC）的肿瘤进展密切相关。然而，免疫细胞的特定甲基化特征尚未得到充分研究。此外，免疫细胞特异性甲基化特征是否能够识别乳腺癌患者的亚组并对其预后进行分层仍不清楚。我们收集了从基因表达综合数据库下载的八个数据集中六种免疫细胞类型的DNA甲基化谱，以识别免疫细胞特异性高甲基化特征（IC-SHMSs）。使用从癌症基因组图谱获得的乳腺癌数据进行单变量和多变量cox回归分析，以确定这些IC-SHMSs的预后价值。对具有预后价值的IC-SHMSs进行无监督聚类分析，将乳腺癌患者分为亚组。构建多个Cox比例风险模型，以探讨IC-SHMSs在乳腺癌患者风险分层中的作用及其与临床特征的关系。进行综合判别改善（IDI）分析，以确定IC-SHMSs在风险分层中对临床特征的改善是否具有统计学意义。共鉴定出六种免疫细胞类型的655个IC-SHMSs。其中30个具有预后价值，10个具有独立预后价值。识别出四个乳腺癌患者亚组，它们在生存预后和免疫格局方面表现出显著的异质性。包含九个IC-SHMSs的模型显示出与包含年龄和TNM分期的临床模型相似的生存预测准确性[3年曲线下面积（AUC）：0.793对0.785；5年AUC：0.735对0.761]。将IC-SHMSs添加到临床模型中显著提高了其在风险分层中的预测准确性（3年AUC：0.897；5年AUC：0.856）。IDI结果验证了这种改善的统计学意义（P<0.05）。我们的研究表明，IC-SHMSs可能作为乳腺癌分类和风险分层的特征。我们的发现为表观遗传特征提供了新的见解，这可能有助于改善亚组识别、风险分层和治疗管理。
