食管癌甲基化驱动基因的筛选、功能及调控通路分析
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摘要
目的筛选食管癌甲基化驱动基因,并进行功能及调控通路分析。方法搜集TCGA数据库中基因表达数据、DNA甲基化数据、临床信息完整的食管癌患者资料162例,记为肿瘤组;其中16例有癌旁对照资料,记为对照组。使用R语言Methyl Mix包综合分析两组患者的基因表达数据和DNA甲基化数据。R语言Methyl Mix包定义肿瘤组和对照组甲基化平均值的差值倍数为差异甲基化值(DM值),Spearman相关分析法分析基因表达与甲基化相关性,以|DM值|>0、|相关系数(Cor)|>0. 3为截断值筛选甲基化驱动基因。使用在线分析软件DAVID 6. 8(https://david. ncifcrf. gov/)对食管癌甲基化驱动基因进行基因本体(GO)功能富集分析。使用在线数据库KOBAS 3. 0(http://kobas. cbi. pku. edu. cn/)对食管癌甲基化驱动基因进行京都基因与基因组百科全书(KEGG)通路分析。结果筛选得到88个食管癌甲基化驱动基因,其中74(84. 1%)个为高甲基化驱动基因,14个为低甲基化驱动基因。最可能发生甲基化的食管癌甲基化驱动基因有Makorin环指蛋白3(MKRN3)基因、人Fermitin家族同系物3(FERMT3)基因、含V-Set免疫球蛋白域蛋白2(VSIG2)基因等。在分子功能层面,食管癌甲基化驱动基因主要影响了金属离子结合、DNA结合、转录因子活性、序列特异性、核酸结合;在生物学过程层面,食管癌甲基化驱动基因影响了转录调控、DNA模板化;在细胞成分层面,食管癌甲基化驱动基因影响了核组分、胞内组分。锌指蛋白家族如ZNF582、ZNF69、ZKSCAN7、ZNF583、ZNF568、ZNF454、ZNF790、ZNF880等和SOX1基因被富集在了多个GO中。食管癌甲基化驱动基因主要参与了乙醛酸和二羧酸代谢通路,甘氨酸、丝氨酸和苏氨酸代谢通路,碳代谢通路及谷胱甘肽代谢通路的调控。结论食管癌甲基化驱动基因有88个,以高甲基化驱动基因为主;最可能发生甲基化调控的食管癌甲基化驱动基因有MKRN3基因、FERMT3基因、VSIG2基因等;食管癌甲基化驱动基因主要影响了金属离子结合、DNA结合等分子功能,转录调控、DNA模板化等生物学过程,核组分、胞内组分等细胞成分;食管癌甲基化驱动基因主要调控代谢相关的多个通路。
Objective To screen out the esophageal cancer-related methylation-driven genes and to perform pathway analysis to identify the function of these genes. Methods RNA-Seq data and methylation data of 162 esophageal cancer were extracted from the public Cancer Genome Atlas( TCGA) database and standardized by edgR and Limma. The R package Methyl Mix defined the difference between the mean value of the methylation of the tumor group and the control group as the DM-value,and the Spearman correlation analysis was used to assess the correlation between gene expression and methylation,with | DM-value | > 0 and | Cor | > 0. 3 as the cut-off value for screening the methylation-driven genes. A web-based online software DAVID 6. 8( https://david. ncifcrf. gov/) was used to perform Gene Ontology( GO),and other online database KOBAS 3. 0( http://kobas. cbi. pku. edu. cn/) was used to perform KEGG pathway analysis. Results A total of 88 methylation-driven genes were screened out,of which 74 were hypermethylated and 14 were hypomethylated,such as MKRN3,FERMT3,VSIG2 and the like. At the molecular function level,the esophageal cancer methylation-driven genes mainly affected metal ion binding,DNA binding,transcription factor activity,sequence specificity,and nucleic acid binding; at the biological process level,the esophageal cancer methylation-driven genes influenced the transcriptional regulation and DNA template; at the cellular level,the esophageal cancer methylation-driven genes affected the nuclear components and intracellular components. Zinc finger protein families such as ZNF582,ZNF69,ZKSCAN7,ZNF583,ZNF568,ZNF454,ZNF790,ZNF880,etc. and the SOX1 gene were enriched in the GO. The methylation-driven genes of esophageal cancer were mainly involved in the glyoxylic acid and dicarboxylic acid metabolism pathways,the glycine,serine and threonine metabolic pathways,the carbon metabolism pathway and the glutathione metabolism pathway. Conclusions There are 88 methylation-driven genes in the esophageal cancer,which are mainly driven by hypermethylation. The methylation-driven genes of esophageal cancer include MKRN3 gene,FERMT3 gene,VSIG2 gene,etc. The methylation-driven genes of esophageal cancer mainly affect the molecular functions such as metal ion binding and DNA binding,biological processes such as regulation of transcription and DNA template,nuclear components,intracellular components and other cellular components; and the esophageal cancer methylation-driven genes mainly regulate the multiple pathways-related to metabolism.
Objective To screen out the esophageal cancer-related methylation-driven genes and to perform pathway analysis to identify the function of these genes. Methods RNA-Seq data and methylation data of 162 esophageal cancer were extracted from the public Cancer Genome Atlas( TCGA) database and standardized by edgR and Limma. The R package Methyl Mix defined the difference between the mean value of the methylation of the tumor group and the control group as the DM-value,and the Spearman correlation analysis was used to assess the correlation between gene expression and methylation,with | DM-value | > 0 and | Cor | > 0. 3 as the cut-off value for screening the methylation-driven genes. A web-based online software DAVID 6. 8( https://david. ncifcrf. gov/) was used to perform Gene Ontology( GO),and other online database KOBAS 3. 0( http://kobas. cbi. pku. edu. cn/) was used to perform KEGG pathway analysis. Results A total of 88 methylation-driven genes were screened out,of which 74 were hypermethylated and 14 were hypomethylated,such as MKRN3,FERMT3,VSIG2 and the like. At the molecular function level,the esophageal cancer methylation-driven genes mainly affected metal ion binding,DNA binding,transcription factor activity,sequence specificity,and nucleic acid binding; at the biological process level,the esophageal cancer methylation-driven genes influenced the transcriptional regulation and DNA template; at the cellular level,the esophageal cancer methylation-driven genes affected the nuclear components and intracellular components. Zinc finger protein families such as ZNF582,ZNF69,ZKSCAN7,ZNF583,ZNF568,ZNF454,ZNF790,ZNF880,etc. and the SOX1 gene were enriched in the GO. The methylation-driven genes of esophageal cancer were mainly involved in the glyoxylic acid and dicarboxylic acid metabolism pathways,the glycine,serine and threonine metabolic pathways,the carbon metabolism pathway and the glutathione metabolism pathway. Conclusions There are 88 methylation-driven genes in the esophageal cancer,which are mainly driven by hypermethylation. The methylation-driven genes of esophageal cancer include MKRN3 gene,FERMT3 gene,VSIG2 gene,etc. The methylation-driven genes of esophageal cancer mainly affect the molecular functions such as metal ion binding and DNA binding,biological processes such as regulation of transcription and DNA template,nuclear components,intracellular components and other cellular components; and the esophageal cancer methylation-driven genes mainly regulate the multiple pathways-related to metabolism.
引文
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[2]Fernandez AF,Assenov Y,Martin-subero JI,et al. A DNA methylation fingerprint of 1628 human samples[J]. Genome Res,2012,22(2):407-419.
[3]Gevaert O. MethylMix:an R package for identifying DNA methylation-driven genes[J]. Bioinformatics,2015,31(11):1839-1841.
[4]Gao C,Zhuang J,Li H,et al. Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma[J]. Cancer Cell Int,2018,18:194.
[5]Kanehisa M,Goto S,Kawashima S,et al. The KEGG resource for deciphering the genome[J]. Nucleic Acids Research,2004,32:277-280.
[6]Krzyzewska IM,Ensink JBM,Nawijn L,et al. Genetic variant in CACNA1C is associated with PTSD in traumatized police officers[J]. Eur J Hum Genet,2018,26(2):247-257.
[7]Abreu AP,Macedo DB,Brito VN,et al. A new pathway in the control of the initiation of puberty:the MKRN3 gene[J]. J Mol Endocrinol,2015,54(3):R131-R139.
[8]Brito VN,Spinola-Castro AM,Kochi C,et al. Central precocious puberty:revisiting the diagnosis and therapeutic management[J].Arch Endocrinol Metab,2016,60(2):163-172.
[9]Lu C,Cui C,Liu B,et al. FERMT3 contributes to glioblastoma cell proliferation and chemoresistance to temozolomide through integrin mediated Wnt signaling[J]. Neurosci Lett,2017,657:77-83.
[10]Kiriyama K,Hirohashi Y,Torigoe T,et al. Expression and function of FERMT genes in colon carcinoma cells[J]. Anticancer Res,2013,33(1):167-173.
[11]Kariuki SN,Franek BS,Kumar AA,et al. Trait-stratified genomewide association study identifies novel and diverse genetic associations with serologic and cytokine phenotypes in systemic lupus erythematosus[J]. Arthritis Res Ther,2010,12(4):151.
[12]Jen J,Wang YC. Zinc finger proteins in cancer progression[J]. J Biomed Sci,2016,23(1):53.
[13]Lin YW,Tsao CM,Yu PN,et al. SOX1 suppresses cell growth and invasion in cervical cancer[J]. Gynecol Oncol,2013,131(1):174-181.
[14]Marengo B,De Ciusis C,Ricciarelli R,et al. DNA oxidative damage of neoplastic rat liver lesions[J]. Oncol Rep,2010,23(5):1241-1246.