利用RNA-seq数据分析肝细胞癌差异表达基因及蛋白相互作用
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摘要
RNA-Seq已成为当前转录组学研究的强有力工具,尤其在肿瘤差异表达基因的筛选方面有重要的应用价值。为进一步阐明肝细胞癌(HCC)的分子机制,本研究对GEO中1个包括12对HCC组织标本的RNA-Seq数据集(GSE63863)进行了生物信息学分析。采用edgeR、DESeq2、voom等3种不同算法的软件进行统计分析,共获得976个差异表达基因(adj. p-value<0.01或FDR<0.01,|logFC|≥2),其中上调表达422个(43.2%),下调554个(56.8%)。GO富集分析显示这些差异表达基因主要涉及离子结合、氧化还原酶活性等分子功能以及氧化还原、细胞分裂等生物学过程;KEGG通路分析显示,这些差异表达基因主要涉及细胞周期、视黄醇等代谢通路。STRING分析显示,共有654个基因编码的蛋白质存在相互作用,进一步利用MCODE分析显示,169个基因编码蛋白构成4个子网络,相应的中心节点基因分别为UBE2C、GNG4、TTR、FOS,这些基因的异常表达可能在HCC的发生发展过程中具有重要作用。上述研究结果将为进一步阐明HCC分子发病机制、寻找新型生物标志物提供初步的依据。
RNA-Seq has emerged as a powerful research tool to transcriptomics, especially for screening differentially expressed genes(DEGs) of cancer. To further clarify the molecular mechanism of hepatocellular carcinoma(HCC), an RNA-Seq dataset(GSE63863) including 12 matched pairs of HCC tissue samples in GEO was performed with bioinformatics analysis. Three software in different algorithms of edgeR, DESeq2 and voom were applied for statistical analysis. A total of 976 differentially expressed genes were obtained(adj. p-value<0.01 or FDR<0.01, |logFC|≥2), including 422 upregulated genes(43.2%) and 554 downregulated(56.8%) genes. GO enrichment analysis indicated that these differentially expressed genes were mainly involved in molecular functions such as ion binding, oxidoreductase activity, etc., and biological processes(such as oxidation-reduction process, cell division, etc. KEGG pathways analysis showed that these differentially expressed genes were mainly involved in metabolic pathways such as cell cycle, retinol metabolism, etc.. STRING analysis suggested that a total of 654 gene encoded protein were interreacted. 169 proteins were clustered into 4 sub-networks further analyzed by MCODE, and the corresponding central node genes were UBE2 C, GNG4, TTR and FOS, respectively. Abnormal expression of these genes might play an impartment role in the development of HCC. These findings would provide preliminary basis for further illustrating the molecular pathogenesis, and finding new biomarkers of HCC.
RNA-Seq has emerged as a powerful research tool to transcriptomics, especially for screening differentially expressed genes(DEGs) of cancer. To further clarify the molecular mechanism of hepatocellular carcinoma(HCC), an RNA-Seq dataset(GSE63863) including 12 matched pairs of HCC tissue samples in GEO was performed with bioinformatics analysis. Three software in different algorithms of edgeR, DESeq2 and voom were applied for statistical analysis. A total of 976 differentially expressed genes were obtained(adj. p-value<0.01 or FDR<0.01, |logFC|≥2), including 422 upregulated genes(43.2%) and 554 downregulated(56.8%) genes. GO enrichment analysis indicated that these differentially expressed genes were mainly involved in molecular functions such as ion binding, oxidoreductase activity, etc., and biological processes(such as oxidation-reduction process, cell division, etc. KEGG pathways analysis showed that these differentially expressed genes were mainly involved in metabolic pathways such as cell cycle, retinol metabolism, etc.. STRING analysis suggested that a total of 654 gene encoded protein were interreacted. 169 proteins were clustered into 4 sub-networks further analyzed by MCODE, and the corresponding central node genes were UBE2 C, GNG4, TTR and FOS, respectively. Abnormal expression of these genes might play an impartment role in the development of HCC. These findings would provide preliminary basis for further illustrating the molecular pathogenesis, and finding new biomarkers of HCC.
引文
Anders S.,Pyl P.T.,and Huber W.,2015,HTSeq-a Python framework to work with high-throughput sequencing data,Bioinformatics,31(2):166-169
Bottomly D.,Walter N.A.,Hunter J.E.,Darakjian P.,Kawane S.,Buck K.J.,Searles R.P.,Mooney M.,Mc Weeney S.K.,and Hitzemann R.,2011,Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays,PLoS One,6(3):e17820
Chen W.,Zheng R.,Baade P.D.,Zhang S.,Zeng H.,Bray F.,Jemal A.,Yu X.Q.,and He J.,2016,Cancer statistics in China,2015,CA Cancer J.Clin.,66(2):115-132
Katare D.P.,Malik S.,Mani R.J.,Ranjpour M.,and Jain S.K.,2018,Novel mutations in transthyretin gene associated with hepatocellular carcinoma,Mol.Carcinog.,57(1):70-77
Huang D.W.,Sherman B.T.,and Lempicki R.A.,2009,Systematic and integrative analysis of large gene lists using DAVIDbioinformatics resources,Nat.Protoc.,4(1):44-57
Ieta K.,Ojima E.,Tanaka F.,Nakamura Y.,Haraguchi N.,Mimori K.,Inoue H.,Kuwano H.,and Mori M.,2007,Identification of overexpressed genes in hepatocellular carcinoma,with special reference to ubiquitin-conjugating enzyme E2Cgene expression,Int.J.Cancer,121(1):33-38
Lafaro K.J.,Demirjian A.N.,and Pawlik T.M.,2015,Epidemiology of hepatocellular carcinoma,Surg.Oncol.Clin.N.Am.,24(1):1-17
Law C.W.,Chen Y.,Shi W.,and Smyth G.K.,2014,Voom:Precision weights unlock linear model analysis tools for RNA-seq read counts,Genome Biol.,15(2):R29
Love M.I.,Huber W.,and Anders S.,2014,Moderated estimation of fold change and dispersion for RNA-seq data with DE-Seq2,Genome Biol.,15(12):550
Martin M.,2011,Cutadapt removes adapter sequences from high-throughput sequencing reads,EMBnet.Journal,17(1):10-12
Pal J.,Patil V.,Mondal B.,Shukla S.,Hegde A.S.,Arivazhagan A.,Santosh V.,and Somasundaram K.,2016,Epigenetically silenced GNG4 inhibits SDF1alpha/CXCR4 signaling in mesenchymal glioblastoma,Genes Cancer,7(3-4):136-147
Palma P.,Cano C.,Conde-Muino R.,Comino A.,Bueno P.,Ferron J.A.,and Cuadros M.,2014,Expression profiling of rectal tumors defines response to neoadjuvant treatment related genes,PLoS One,9(11):e112189
Robinson M.D.,McCarthy D.J.,and Smyth G.K.,2010,edgeR:a Bioconductor package for differential expression analysis of digital gene expression data,Bioinformatics,26(1):139-140
Shannon P.,Markiel A.,Ozier O.,Baliga N.S.,Wang J.T.,Ramage D.,Amin N.,Schwikowski B.,and Ideker T.,2003,Cytoscape:a software environment for integrated models of biomolecular interaction networks,Genome Res.,13(11):2498-2504
Szklarczyk D.,Franceschini A.,Wyder S.,Forslund K.,Heller D.,Huerta-Cepas J.,Simonovic M.,Roth A.,Santos A.,Tsafou K.P.,Kuhn M.,Bork P.,Jensen L.J.,and von Mering C.,2015,STRING v10:protein-protein interaction networks,integrated over the tree of life,Nucleic Acids Res.,43(Database issue):D447-D452
Trapnell C.,Roberts A.,Goff L.,Pertea G.,Kim D.,Kelley D.R.,Pimentel H.,Salzberg S.L.,Rinn J.L.,and Pachter L.,2012,Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks,Nat.Protoc.,7(3):562-578
Tsuchiya M.,Parker J.S.,Kono H.,Matsuda M.,Fujii H.,and Rusyn I.,2010,Gene expression in nontumoral liver tissue and recurrence-free survival in hepatitis C virus-positive hepatocellular carcinoma,Mol.Cancer,9:74
Wang K.,Liu J.,Yan Z.L.,Li J.,Shi L.H.,Cong W.M.,Xia Y.,Zou Q.F.,Xi T.,Shen F.,Wang H.Y.,and Wu M.C.,2010,Overexpression of aspartyl-(asparaginyl)-beta-hydroxylase in hepatocellular carcinoma is associated with worse surgical outcome,Hepatology,52(1):164-173
Yang M.R.,Zhang Y.,Wu X.X.,and Chen W.,2016,Critical genes of hepatocellular carcinoma revealed by network and module analysis of RNA-seq data,Eur.Rev.Med.Pharmacol.Sci.,20(20):4248-4256
Zhang H.,Weng X.,Ye J.,He L.,Zhou D.,and Liu Y.,2015,Promoter hypermethylation of TERT is associated with hepatocellular carcinoma in the Han Chinese population,Clin.Res.Hepatol.Gastroenterol.,39(5):600-609
Zhao S.,Fung-Leung W.P.,Bittner A.,Ngo K.,and Liu X.,2014,Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells,PLoS One,9(1):e78644
Bottomly D.,Walter N.A.,Hunter J.E.,Darakjian P.,Kawane S.,Buck K.J.,Searles R.P.,Mooney M.,Mc Weeney S.K.,and Hitzemann R.,2011,Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays,PLoS One,6(3):e17820
Chen W.,Zheng R.,Baade P.D.,Zhang S.,Zeng H.,Bray F.,Jemal A.,Yu X.Q.,and He J.,2016,Cancer statistics in China,2015,CA Cancer J.Clin.,66(2):115-132
Katare D.P.,Malik S.,Mani R.J.,Ranjpour M.,and Jain S.K.,2018,Novel mutations in transthyretin gene associated with hepatocellular carcinoma,Mol.Carcinog.,57(1):70-77
Huang D.W.,Sherman B.T.,and Lempicki R.A.,2009,Systematic and integrative analysis of large gene lists using DAVIDbioinformatics resources,Nat.Protoc.,4(1):44-57
Ieta K.,Ojima E.,Tanaka F.,Nakamura Y.,Haraguchi N.,Mimori K.,Inoue H.,Kuwano H.,and Mori M.,2007,Identification of overexpressed genes in hepatocellular carcinoma,with special reference to ubiquitin-conjugating enzyme E2Cgene expression,Int.J.Cancer,121(1):33-38
Lafaro K.J.,Demirjian A.N.,and Pawlik T.M.,2015,Epidemiology of hepatocellular carcinoma,Surg.Oncol.Clin.N.Am.,24(1):1-17
Law C.W.,Chen Y.,Shi W.,and Smyth G.K.,2014,Voom:Precision weights unlock linear model analysis tools for RNA-seq read counts,Genome Biol.,15(2):R29
Love M.I.,Huber W.,and Anders S.,2014,Moderated estimation of fold change and dispersion for RNA-seq data with DE-Seq2,Genome Biol.,15(12):550
Martin M.,2011,Cutadapt removes adapter sequences from high-throughput sequencing reads,EMBnet.Journal,17(1):10-12
Pal J.,Patil V.,Mondal B.,Shukla S.,Hegde A.S.,Arivazhagan A.,Santosh V.,and Somasundaram K.,2016,Epigenetically silenced GNG4 inhibits SDF1alpha/CXCR4 signaling in mesenchymal glioblastoma,Genes Cancer,7(3-4):136-147
Palma P.,Cano C.,Conde-Muino R.,Comino A.,Bueno P.,Ferron J.A.,and Cuadros M.,2014,Expression profiling of rectal tumors defines response to neoadjuvant treatment related genes,PLoS One,9(11):e112189
Robinson M.D.,McCarthy D.J.,and Smyth G.K.,2010,edgeR:a Bioconductor package for differential expression analysis of digital gene expression data,Bioinformatics,26(1):139-140
Shannon P.,Markiel A.,Ozier O.,Baliga N.S.,Wang J.T.,Ramage D.,Amin N.,Schwikowski B.,and Ideker T.,2003,Cytoscape:a software environment for integrated models of biomolecular interaction networks,Genome Res.,13(11):2498-2504
Szklarczyk D.,Franceschini A.,Wyder S.,Forslund K.,Heller D.,Huerta-Cepas J.,Simonovic M.,Roth A.,Santos A.,Tsafou K.P.,Kuhn M.,Bork P.,Jensen L.J.,and von Mering C.,2015,STRING v10:protein-protein interaction networks,integrated over the tree of life,Nucleic Acids Res.,43(Database issue):D447-D452
Trapnell C.,Roberts A.,Goff L.,Pertea G.,Kim D.,Kelley D.R.,Pimentel H.,Salzberg S.L.,Rinn J.L.,and Pachter L.,2012,Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks,Nat.Protoc.,7(3):562-578
Tsuchiya M.,Parker J.S.,Kono H.,Matsuda M.,Fujii H.,and Rusyn I.,2010,Gene expression in nontumoral liver tissue and recurrence-free survival in hepatitis C virus-positive hepatocellular carcinoma,Mol.Cancer,9:74
Wang K.,Liu J.,Yan Z.L.,Li J.,Shi L.H.,Cong W.M.,Xia Y.,Zou Q.F.,Xi T.,Shen F.,Wang H.Y.,and Wu M.C.,2010,Overexpression of aspartyl-(asparaginyl)-beta-hydroxylase in hepatocellular carcinoma is associated with worse surgical outcome,Hepatology,52(1):164-173
Yang M.R.,Zhang Y.,Wu X.X.,and Chen W.,2016,Critical genes of hepatocellular carcinoma revealed by network and module analysis of RNA-seq data,Eur.Rev.Med.Pharmacol.Sci.,20(20):4248-4256
Zhang H.,Weng X.,Ye J.,He L.,Zhou D.,and Liu Y.,2015,Promoter hypermethylation of TERT is associated with hepatocellular carcinoma in the Han Chinese population,Clin.Res.Hepatol.Gastroenterol.,39(5):600-609
Zhao S.,Fung-Leung W.P.,Bittner A.,Ngo K.,and Liu X.,2014,Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells,PLoS One,9(1):e78644