| 周添霖,薛茂杰,戴智翔,张汝阳,陈峰.基于表观基因组数据的乳腺癌预后基因-年龄交互作用研究[J].Chinese journal of Epidemiology,2024,45(7):1007-1013 |
| 基于表观基因组数据的乳腺癌预后基因-年龄交互作用研究 |
| Gene-age interaction study of breast cancer prognosis based on epigenomic data |
| Received:February 01, 2024 |
| DOI：10.3760/cma.j.cn112338-20240201-00057 |
| KeyWord: 乳腺癌 DNA甲基化 年龄 交互作用 |
| English Key Word: Breast cancer DNA methylation Age Interaction |
| FundProject:国家自然科学基金（82220108002，82273737） |
| Author Name | Affiliation | E-mail | | Zhou Tianlin | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China | | | Xue Maojie | Information Center, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou 213164, China | | | Dai Zhixiang | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China | | | Zhang Ruyang | Information Center, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou 213164, China | zhangruyang@njmu.edu.cn | | Chen Feng | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing 211166, China | fengchen@njmu.edu.cn |
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| Abstract: |
| 目的 基于表观基因组数据，探索乳腺癌预后基因-年龄交互作用。方法 利用癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）多个独立乳腺癌表观基因组数据集，进行DNA甲基化的差异表达分析。采用错误发现率（FDR）法进行多重校正，保留q-FDR≤0.05的差异表达甲基化位点。应用三阶段分析策略，采用多因素Cox比例风险回归模型检验基因-年龄交互作用。发现阶段使用TCGA-BRCA数据库筛选q-FDR≤0.05的信号。验证阶段Ⅰ使用GSE72245数据验证交互作用，标准为P≤0.05且效应方向一致。验证阶段Ⅱ使用GSE37754和GSE75067数据再次验证信号。通过结合临床指标与交互作用信号构建预后预测模型。结果 三阶段分析策略鉴定出一个甲基化位点（cg16126280EBF1），其与年龄存在交互作用，共同影响患者的生存时间（交互作用HR=1.001 1，95%CI：1.000 7~1.001 5，P<0.001）。年龄分层分析显示，cg16126280EBF1的高甲基化效应在乳腺癌年轻患者（HR=0.550 5，95%CI：0.383 8~0.789 6，P=0.001）和老年患者中完全相反（HR=2.166 5，95%CI：1.285 2~3.652 2，P=0.004）。结论 DNA甲基化位点cg16126280EBF1与年龄存在交互作用，以复杂的关联模式共同影响乳腺癌预后，为年龄特异性靶向药物研发提供了新的人群证据。 |
| English Abstract: |
| Objective Exploring gene-age interactions associated with breast cancer prognosis based on epigenomic data. Methods Differential expression analysis of DNA methylation was conducted using multiple independent epigenomic datasets of breast cancer from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The false discovery rate (FDR) method was used for multiple corrections, retaining differentially methylated sites with q-FDR≤0.05. A three-stage analytic strategy was implemented, using a multivariable Cox proportional hazards regression model to examine gene-age interactions. In the discovery phase, signals with q-FDR≤ 0.05 were screened out using TCGA-BRCA database. In validation phaseⅠ, the interaction was validated using GSE72245 data, with criteria of P≤0.05 and consistent effect direction. In validation phaseⅡ, the signals were further validated using GSE37754 and GSE75067 data. A prognostic prediction model was constructed by incorporating clinical indicators and interaction signals. Results The three-stage analytic strategy identified a methylation site (cg16126280EBF1), which interacted with age to jointly affect the overall survival time of patients (interaction HR= 1.001 1,95%CI:1.000 7-1.001 5,P<0.001). Stratified analysis by age showed that the effect of hypermethylation of cg16126280EBF1 was completely opposite in younger patients (HR=0.550 5, 95%CI: 0.383 8-0.789 6, P=0.001) and older patients (HR=2.166 5, 95%CI: 1.285 2-3.652 2, P=0.004). Conclusions The DNA methylation site cg16126280EBF1 exhibits an interaction with age, jointly influencing the prognosis of breast cancer in a complex association pattern. This finding contributes new population-based evidence for the development of age-specific targeted drugs. |
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