Department of Oncology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Oncology, Harbin Medical University, Harbin, Heilongjiang, China.
Aging (Albany NY). 2024 May 22;16(10):8998-9022. doi: 10.18632/aging.205853.
The discovery of RNA methylation alterations associated with cancer holds promise for their utilization as potential biomarkers in cancer diagnosis, prognosis, and prediction. RNA methylation has been found to impact the immunological microenvironment of tumors, but the specific role of methylation-related genes (MRGs), particularly in breast cancer (BC), the most common cancer among women globally, within the tumor microenvironment remains unknown. In this study, we obtained data from TCGA and GEO databases to investigate the expression patterns of MRGs in both genomic and transcriptional domains in BC. By analyzing the data, we identified two distinct genetic groupings that were correlated with clinicopathological characteristics, prognosis, degree of TME cell infiltration, and other abnormalities in MRGs among patients. Subsequently, an MRG model was developed to predict overall survival (OS) and its accuracy was evaluated in BC patients. Additionally, a highly precise nomogram was created to enhance the practical usability of the MRG model. In low-risk groups, we observed lower TBM values and higher TIDE scores. We further explored how MRGs influence a patient's prognosis, clinically significant characteristics, response to therapy, and the TME. These risk signatures have the potential to improve treatment strategies for BC patients and could be applied in future clinical settings. Moreover, they may also be utilized to determine prognosis and biological features in these patients.
RNA 甲基化改变与癌症相关的发现有望将其作为癌症诊断、预后和预测的潜在生物标志物加以利用。RNA 甲基化已被发现会影响肿瘤的免疫微环境,但甲基化相关基因 (MRGs),特别是在乳腺癌 (BC) 中的具体作用,全球范围内女性最常见的癌症,在肿瘤微环境中的作用仍不清楚。在这项研究中,我们从 TCGA 和 GEO 数据库中获取数据,以研究 BC 中基因组和转录域中 MRGs 的表达模式。通过分析这些数据,我们确定了两个与临床病理特征、预后、TME 细胞浸润程度以及患者中 MRGs 其他异常相关的不同遗传分组。随后,我们开发了一个用于预测总体生存 (OS) 的 MRG 模型,并在 BC 患者中评估了其准确性。此外,还创建了一个高度精确的列线图,以增强 MRG 模型的实际可用性。在低风险组中,我们观察到较低的 TBM 值和较高的 TIDE 评分。我们进一步探讨了 MRGs 如何影响患者的预后、临床显著特征、对治疗的反应以及 TME。这些风险特征有可能改善 BC 患者的治疗策略,并可在未来的临床环境中应用。此外,它们还可能用于确定这些患者的预后和生物学特征。