{"title":"n6 -甲基腺苷甲基化在子宫内膜癌进展中的作用。","authors":"Kewei Song, Hongxia Xu, Changhe Wang","doi":"10.1089/cbr.2020.3912","DOIUrl":null,"url":null,"abstract":"<p><p><b><i>Purpose:</i></b> N6-methyladenosine (m6A) methylation was the most abundant internal modification on messenger RNAs in eukaryotes. This study intended to explore the role of m6A methylation in endometrial cancer (EC). <b><i>Materials and Methods:</i></b> The m6A-sequencing data \"GSE93911\" of human EC were downloaded from Gene Expression Omnibus database. Hisat2 software and MACS2 were used to perform the alignment of reads and m6A methylation peak calling, and the peaks were annotated using Chipseeker. Then, differential m6A methylation peaks between normal and tumor samples were analyzed, followed by the functional enrichment analysis of the differentially methylated genes in promoter and 3' untranslated region (UTR) using Clusterprofiler. Based on the 450K methylated chip data, gene expression and clinical data in The Cancer Genome Atlas, the differentially methylated genes were verified, followed by Cox univariate/multivariate regression analysis and survival analysis. Finally, a risk prognosis model was constructed. <b><i>Results:</i></b> The m6A peak number was decreased in EC. The distribution of m6A peaks was highly enriched near transcriptional start site, in promoter, UTR, intron and exon, followed by distal intergenic. A total of 581 differentially methylated genes (361 hyper- and 220 hypomethylated genes) were identified in promoter and UTR regions that were enriched in insulin resistance (IR) and extracellular matrix (ECM). A total of 181 genes with significant differential expressions and differential methylation site in EC were selected. Of which, 31 genes were correlated with survival, and an 11-gene risk prognosis model was identified, including GDF7, BNC2, SLC8A1, B4GALNT3, DHCR24, ESRP1, HOXB9, IGSF9, KIAA1324, MSnX1, and PHGDH. <b><i>Conclusion:</i></b> The m6A methylation regulated EC progression by targeting the genes related to IR and ECM. A 11-gene risk prognosis model was identified to predict survival of patients with EC.</p>","PeriodicalId":518937,"journal":{"name":"Cancer biotherapy & radiopharmaceuticals","volume":" ","pages":"737-749"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1089/cbr.2020.3912","citationCount":"5","resultStr":"{\"title\":\"The Role of N6-Methyladenosine Methylation in the Progression of Endometrial Cancer.\",\"authors\":\"Kewei Song, Hongxia Xu, Changhe Wang\",\"doi\":\"10.1089/cbr.2020.3912\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b><i>Purpose:</i></b> N6-methyladenosine (m6A) methylation was the most abundant internal modification on messenger RNAs in eukaryotes. This study intended to explore the role of m6A methylation in endometrial cancer (EC). <b><i>Materials and Methods:</i></b> The m6A-sequencing data \\\"GSE93911\\\" of human EC were downloaded from Gene Expression Omnibus database. Hisat2 software and MACS2 were used to perform the alignment of reads and m6A methylation peak calling, and the peaks were annotated using Chipseeker. Then, differential m6A methylation peaks between normal and tumor samples were analyzed, followed by the functional enrichment analysis of the differentially methylated genes in promoter and 3' untranslated region (UTR) using Clusterprofiler. Based on the 450K methylated chip data, gene expression and clinical data in The Cancer Genome Atlas, the differentially methylated genes were verified, followed by Cox univariate/multivariate regression analysis and survival analysis. Finally, a risk prognosis model was constructed. <b><i>Results:</i></b> The m6A peak number was decreased in EC. The distribution of m6A peaks was highly enriched near transcriptional start site, in promoter, UTR, intron and exon, followed by distal intergenic. A total of 581 differentially methylated genes (361 hyper- and 220 hypomethylated genes) were identified in promoter and UTR regions that were enriched in insulin resistance (IR) and extracellular matrix (ECM). A total of 181 genes with significant differential expressions and differential methylation site in EC were selected. Of which, 31 genes were correlated with survival, and an 11-gene risk prognosis model was identified, including GDF7, BNC2, SLC8A1, B4GALNT3, DHCR24, ESRP1, HOXB9, IGSF9, KIAA1324, MSnX1, and PHGDH. <b><i>Conclusion:</i></b> The m6A methylation regulated EC progression by targeting the genes related to IR and ECM. A 11-gene risk prognosis model was identified to predict survival of patients with EC.</p>\",\"PeriodicalId\":518937,\"journal\":{\"name\":\"Cancer biotherapy & radiopharmaceuticals\",\"volume\":\" \",\"pages\":\"737-749\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1089/cbr.2020.3912\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer biotherapy & radiopharmaceuticals\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1089/cbr.2020.3912\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/10/14 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer biotherapy & radiopharmaceuticals","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/cbr.2020.3912","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/10/14 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
摘要
目的:n6 -甲基腺苷(m6A)甲基化是真核生物信使rna中最丰富的内部修饰。本研究旨在探讨m6A甲基化在子宫内膜癌(EC)中的作用。材料与方法:从Gene Expression Omnibus数据库下载人EC的m6a测序数据“GSE93911”。使用Hisat2软件和MACS2进行reads比对和m6A甲基化峰调用,并使用Chipseeker对峰进行注释。然后,分析正常和肿瘤样本之间m6A甲基化差异峰,然后使用Clusterprofiler对启动子和3'非翻译区(UTR)差异甲基化基因进行功能富集分析。根据the Cancer Genome Atlas中的450K甲基化芯片数据、基因表达和临床数据,验证差异甲基化基因,然后进行Cox单因素/多因素回归分析和生存分析。最后,构建风险预测模型。结果:EC组m6A峰数明显减少。m6A峰分布在转录起始位点附近、启动子、UTR、内含子和外显子高度富集,其次是远端基因间。在胰岛素抵抗(IR)和细胞外基质(ECM)富集的启动子和UTR区域共鉴定出581个差异甲基化基因(361个高甲基化基因和220个低甲基化基因)。共筛选出181个在EC中有显著差异表达和差异甲基化位点的基因。其中,31个基因与生存相关,确定了11个基因的风险预后模型,包括GDF7、BNC2、SLC8A1、B4GALNT3、DHCR24、ESRP1、HOXB9、IGSF9、KIAA1324、MSnX1和PHGDH。结论:m6A甲基化通过靶向与IR和ECM相关的基因调控EC的进展。确定了一个11基因风险预后模型来预测EC患者的生存。
The Role of N6-Methyladenosine Methylation in the Progression of Endometrial Cancer.
Purpose: N6-methyladenosine (m6A) methylation was the most abundant internal modification on messenger RNAs in eukaryotes. This study intended to explore the role of m6A methylation in endometrial cancer (EC). Materials and Methods: The m6A-sequencing data "GSE93911" of human EC were downloaded from Gene Expression Omnibus database. Hisat2 software and MACS2 were used to perform the alignment of reads and m6A methylation peak calling, and the peaks were annotated using Chipseeker. Then, differential m6A methylation peaks between normal and tumor samples were analyzed, followed by the functional enrichment analysis of the differentially methylated genes in promoter and 3' untranslated region (UTR) using Clusterprofiler. Based on the 450K methylated chip data, gene expression and clinical data in The Cancer Genome Atlas, the differentially methylated genes were verified, followed by Cox univariate/multivariate regression analysis and survival analysis. Finally, a risk prognosis model was constructed. Results: The m6A peak number was decreased in EC. The distribution of m6A peaks was highly enriched near transcriptional start site, in promoter, UTR, intron and exon, followed by distal intergenic. A total of 581 differentially methylated genes (361 hyper- and 220 hypomethylated genes) were identified in promoter and UTR regions that were enriched in insulin resistance (IR) and extracellular matrix (ECM). A total of 181 genes with significant differential expressions and differential methylation site in EC were selected. Of which, 31 genes were correlated with survival, and an 11-gene risk prognosis model was identified, including GDF7, BNC2, SLC8A1, B4GALNT3, DHCR24, ESRP1, HOXB9, IGSF9, KIAA1324, MSnX1, and PHGDH. Conclusion: The m6A methylation regulated EC progression by targeting the genes related to IR and ECM. A 11-gene risk prognosis model was identified to predict survival of patients with EC.