Comprehensive analysis of gene expression and DNA methylation data identifies potential biomarkers and functional epigenetic modules for lung adenocarcinoma

Abstract Lung cancer has one of the highest mortality rates of malignant neoplasms. Lung adenocarcinoma (LUAD) is one of the most common types of lung cancer. DNA methylation is more stable than gene expression and could be used as a biomarker for early tumor diagnosis. This study is aimed to screen potential DNA methylation signatures to facilitate the diagnosis and prognosis of LUAD and integrate gene expression and DNA methylation data of LUAD to identify functional epigenetic modules. We systematically integrated gene expression and DNA methylation data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), bioinformatic models and algorithms were implemented to identify signatures and functional modules for LUAD. Three promising diagnostic and five potential prognostic signatures for LUAD were screened by rigorous filtration, and our tumor-normal classifier and prognostic model were validated in two separate data sets. Additionally, we identified functional epigenetic modules in the TCGA LUAD dataset and GEO independent validation data set. Interestingly, the MUC1 module was identified in both datasets. The potential biomarkers for the diagnosis and prognosis of LUAD are expected to be further verified in clinical practice to aid in the diagnosis and treatment of LUAD.摘要 肺癌是恶性肿瘤中死亡率最高的病种之一。肺腺癌（Lung adenocarcinoma, LUAD）是肺癌最为常见的类型之一。DNA甲基化（DNA methylation）较基因表达更为稳定，可作为肿瘤早期诊断的生物标志物（biomarker）。本研究旨在筛选潜在的DNA甲基化标志物，以助力肺腺癌的诊断与预后评估，并整合肺腺癌的基因表达与DNA甲基化数据，以识别功能性表观遗传模块（epigenetic modules）。本研究系统整合了来自癌症基因组图谱（The Cancer Genome Atlas, TCGA）与基因表达综合数据库（Gene Expression Omnibus, GEO）的基因表达及DNA甲基化数据，并通过生物信息学（bioinformatics）模型与算法筛选肺腺癌的标志物及功能性模块。经严格筛选，本研究共得到3个具有应用前景的诊断标志物与5个潜在预后标志物；我们构建的肿瘤-正常组织分类器与预后模型，在两个独立数据集上均得到了验证。此外，本研究在TCGA肺腺癌数据集与GEO独立验证数据集中均识别出了功能性表观遗传模块。值得注意的是，MUC1模块在两个数据集中均被成功识别。本研究筛选出的肺腺癌诊断与预后潜在标志物，有望在临床实践中得到进一步验证，以辅助肺腺癌的诊疗工作。