School of Chemical Engineering and Technology, Tianjin University, 300072 Tianjin, China.
Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, 300072 Tianjin, China.
Biomed Res Int. 2020 Apr 24;2020:2471915. doi: 10.1155/2020/2471915. eCollection 2020.
Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman's rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD.
烟草暴露是肺癌发生和进展的主要危险因素之一。然而,确切的相应机制主要未知。最近,越来越多的证据表明,DNA 甲基化参与了癌细胞中基因表达的调控。鉴定与烟草相关的特征性甲基化探针,并分析它们在不同分子水平上的调控网络,可能有助于理解与烟草相关的肿瘤发生。使用三个独立的肺腺癌(LUAD)数据集来训练和验证烟草暴露模式分类模型。提出并使用一种深度选择方法从数十万整个表观基因组探针中识别甲基化特征探针。然后,分别使用 BIMC(双权重中位数相关系数)算法、SRC(Spearman 秩相关)分析和最短路径跟踪方法来识别基因调控水平和蛋白质-蛋白质相互作用水平的相关基因。之后,使用 KEGG(京都基因与基因组百科全书)通路分析和 GO(基因本体论)富集分析来分析它们的分子功能和相关通路。鉴定出 105 个探针作为与烟草相关的 DNA 甲基化特征。它们属于 95 个基因,这些基因参与了 hsa04512、hsa04151 和其他重要途径。在基因调控水平上,通过 BIMC 和 SRC 方法都发现 33 个基因与特征探针高度相关。其中,FARSB 和其他八个基因被揭示为基因调控网络中的枢纽基因。同时,这些 33 个基因的 PPI 网络表明,MAGOH、FYN 等五个基因是其中最连接的核心基因。这些分析结果可能为肿瘤发生的分子机制提供明确的生物学解释线索。此外,鉴定出的特征性探针可作为 LUAD 精准医学的潜在药物靶点。