非综合征型唇腭裂DNA甲基化谱的生物信息学分析

目的:针对非综合征型唇腭裂(non-syndrome cleft lip/cleft, NSCL/P)DNA甲基化谱,采用生物信息学技术筛选NSCL/P异常甲基化位点和异常甲基化区域,探讨DNA甲基化与NSCL/P发病机制的关系。方法:从基因表达综合数据库(Gene Expression Omnibus, GEO)的数据集下载原始数据,纳入67例NSCL/P患者和59例无出生缺陷者的全血甲基化数据。数据分析包括①探针过滤、质控、归一化等数据清洗;②差异甲基化位点和差异甲基化区域等差异甲基化分析;③对差异甲基化区域所在的基因进行基因本体(Gene Ontology,GO)富集分析、京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes, KEGG)信号通路富集分析,明确异常DNA甲基化对生物功能学的影响。采用R 3.4.3统计学软件进行数据清洗、筛选差异甲基化位点和差异甲基化区域,采用DAVID 6.8工具对差异甲基化区域进行GO和KEGG富集分析。结果:NSCL/P患者和正常对照者差异显著的甲基化位点共814个(校正P<0.001,|Δβ|>0.125),其中NSCL/P组与对照组相比,高甲基化位点178个,低甲基化位点636个;差异显著的甲基化区域共386个(P<0.05),其中高甲基化区域204个,低甲基化区域182个。GO富集分析显示,富集于7个生化过程相关的差异性甲基化基因共38个,富集于3个分子功能相关的差异性甲基化基因共163个,富集于3个细胞成分有关的差异性甲基化基因共114个(P<0.01)。KEGG通路分析显示,9个信号通路出现差异性甲基化基因富集,涉及59个基因(P<0.05)。结论:DNA甲基化异常可能是影响NSCL/P发生、发展的重要表观遗传学机制,为诊断标志物筛选和靶向干预提供了线索。

Study on DNA methylation profiles in non-syndromic cleft lip/palate based on bioinformatics

PURPOSE: To study DNA methylation patterns of non-syndromic cleft lip/palate(NSCL/P) using bioinformatic methods, including methylated positions and regions. METHODS: Whole blood DNA methylation data of NSCL/P samples was download from Gene Expression Omnibus(GEO) database, including 67 NSCL/P cases and 59 controls without birth defects. Data analysis included ①data cleaning, such as probes filtering, quality control and normalization; ②differential methylation analysis, including methylated positions and regions; ③Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on differential methylated genes. R 3.4.3 software was used for data cleaning, differential methylated positions and regions analysis. DAVID6.8 tool was used for GO and KEGG analysis. RESULTS: 814 differential methylated positions were detected (adjusted P<0.001,|Δβ|>0.125）, of which 178 were hypermethylated in NSCL/P patients, and 636 were hypomethylated. In addition, 386 differential methylated regions were identified (P<0.05), of which 204 were hypermethylation regions and 182 were hypomethylation regions. GO analysis showed that 38 differential methylated genes were involved in 7 kinds of biological processes, 163 differential methylated genes were involved in 3 kinds of molecular functions, and 114 differential methylated genes were involved in 3 kinds of cellular components (P<0.01). KEGG analysis showed that 59 differential methylated genes were involved in 9 kinds of signal pathways. CONCLUSIONS: Abnormal DNA methylation patterns of NSCL/P might be an important epigenetic mechanism affecting the development of NSCL/P. This study might contribute to the identification of identification of biomarkers and targeted interventions of NSCL/P.