Shen Sipeng, Chen Jiajin, Li Hongru, Jiang Yunke, Wei Yongyue, Zhang Ruyang, Zhao Yang, Chen Feng
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China.
Cell Rep. 2023 Mar 28;42(3):112261. doi: 10.1016/j.celrep.2023.112261. Epub 2023 Mar 15.
Characterizing influences of DNA methylation (DNAm) on non-coding RNAs (ncRNAs) is important to understand the mechanisms of gene regulation and cancer outcome. In our study, we describe the results of ncRNA quantitative trait methylation sites (ncQTM) analyses on 8,545 samples from The Cancer Genome Atlas (TCGA), 763 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 516 samples from Genotype-Tissue Expression (GTEx) to identify the significant associations between DNAm sites and ncRNAs (miRNA, long non-coding RNA [lncRNA], small nuclear RNA [snRNA], small nucleolar RNA [snoRNA], and rRNA) across 32 cancer types. With more than 22 billion tests, we identify 302,764 cis-ncQTMs (6.28% of all tested) and 79,841,728 trans-ncQTMs (1.15% of all tested). Most DNAm sites (70.6% on average) are in trans association, while only 25.2% DNAm sites are in cis association. Further, we develop a subtype named ncm based on cancer-specific ncRNAs thatis associated with tumor microenvironment, clinical outcome, and biological pathways. To comprehensively describe the ncQTM patterns, we developed a database named Pancan-ncQTM (http://bigdata.njmu.edu.cn/Pancan-ncQTM/).
表征DNA甲基化(DNAm)对非编码RNA(ncRNA)的影响对于理解基因调控机制和癌症预后至关重要。在我们的研究中,我们描述了对来自癌症基因组图谱(TCGA)的8545个样本、临床蛋白质组肿瘤分析联盟(CPTAC)的763个样本以及基因型-组织表达(GTEx)的516个样本进行的ncRNA定量性状甲基化位点(ncQTM)分析结果,以确定32种癌症类型中DNAm位点与ncRNA(微小RNA [miRNA]、长链非编码RNA [lncRNA]、小核RNA [snRNA]、小核仁RNA [snoRNA]和核糖体RNA [rRNA])之间的显著关联。通过超过220亿次测试,我们鉴定出302,764个顺式ncQTM(占所有测试的6.28%)和79,841,728个反式ncQTM(占所有测试的1.15%)。大多数DNAm位点(平均70.6%)呈反式关联,而只有25.2%的DNAm位点呈顺式关联。此外,我们基于与肿瘤微环境、临床结局和生物学途径相关的癌症特异性ncRNA开发了一种名为ncm的亚型。为了全面描述ncQTM模式,我们开发了一个名为Pancan-ncQTM的数据库(http://bigdata.njmu.edu.cn/Pancan-ncQTM/)。