miR-129-5p调控的COL1A1作为胃癌潜在治疗靶点的生物信息学分析
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摘要
目的应用生物信息学技术探索胃癌发病机制,为胃癌的防治提供生物信息学依据。方法用GEO2R在线工具分析GSE79973中胃癌组织和正常胃黏膜组织的差异表达基因(Differentially expressed genes, DEGs),通过DAVID数据库对DEGs进行GO分析和KEGG通路富集分析,然后通过STRING数据库构建蛋白质相互作用网络,用Cytoscape软件进行关键基因(Hub基因)筛选和功能模块分析,并在GEPIA数据库对Hub基因进行验证,用Target Scan数据库预测调控靶基因的microRNAs,并用OncomiR分析microRNAs在胃癌组织中的表达及其与生存预后的关系。结果共筛选出181个在胃癌中差异表达的基因。蛋白质互作网络筛选出10个Hub基因。DEGs功能分析主要涉及蛋白质消化吸收、PI3K-Akt信号通路、ECM-受体相互作用、血小板激活信号通路。GEPIA数据库验证显示COL1A1在胃癌组织中高表达,并和胃癌患者的不良预后有关。miR-129-5p与COL1A1 mRNA的3'UTR结合。与正常组织相比,miR-129-5p在胃癌组织中表达明显下调,且与胃癌患者预后具有一定相关性。结论 miR-129-5p调控的COL1A1是胃癌潜在的治疗靶点。
Objective To explore the pathogenesis of gastric cancer through a bioinformatic approach to provide evidence for the prevention and treatment of gastric cancer. Methods The differentially expressed genes(DEGs) in gastric cancer and normal gastric mucosa in GSE79973 dataset were analyzed using GEO2 R online tool. GO analysis and KEGG pathway enrichment analysis of the DEGs in DAVID database were performed. The protein interaction network was constructed using STRING database, and the key genes(Hub genes) were screened and their functional modules were analyzed using Cytoscape software. The GEPIA database was used to validate the Hub genes, and the Target Scan database was used to predict the microRNAs that regulate the target genes; OncomiR was used to analyze the expressions of the microRNAs in gastric cancer tissues and their relationship with the survival outcomes of the patients. Results A total of 181 DEGs were identified in gastric cancer, and 10 hub genes were screened by the protein-protein interaction network. Functional analysis showed that these DEGs were involved mainly in protein digestion and absorption, PI3 K-Akt signaling pathway, ECM-receptor interaction and platelet activation signal pathway. GEPIA database validation showed that COL1 A1 was highly expressed in gastric cancer tissues and was associated with a poor prognosis of patients with gastric cancer. MiR-129-5 p was found to bind to the 3'UTR of COL1 A1 mRNA, and compared with that in normal tissues, miR-129-5 p expression was obviously down-regulated in gastric cancer tissues, and was correlated with the prognosis of the patients. Conclusion COL1 A1 under regulation by MiR-129-5 p is a potential therapeutic target for gastric cancer.
Objective To explore the pathogenesis of gastric cancer through a bioinformatic approach to provide evidence for the prevention and treatment of gastric cancer. Methods The differentially expressed genes(DEGs) in gastric cancer and normal gastric mucosa in GSE79973 dataset were analyzed using GEO2 R online tool. GO analysis and KEGG pathway enrichment analysis of the DEGs in DAVID database were performed. The protein interaction network was constructed using STRING database, and the key genes(Hub genes) were screened and their functional modules were analyzed using Cytoscape software. The GEPIA database was used to validate the Hub genes, and the Target Scan database was used to predict the microRNAs that regulate the target genes; OncomiR was used to analyze the expressions of the microRNAs in gastric cancer tissues and their relationship with the survival outcomes of the patients. Results A total of 181 DEGs were identified in gastric cancer, and 10 hub genes were screened by the protein-protein interaction network. Functional analysis showed that these DEGs were involved mainly in protein digestion and absorption, PI3 K-Akt signaling pathway, ECM-receptor interaction and platelet activation signal pathway. GEPIA database validation showed that COL1 A1 was highly expressed in gastric cancer tissues and was associated with a poor prognosis of patients with gastric cancer. MiR-129-5 p was found to bind to the 3'UTR of COL1 A1 mRNA, and compared with that in normal tissues, miR-129-5 p expression was obviously down-regulated in gastric cancer tissues, and was correlated with the prognosis of the patients. Conclusion COL1 A1 under regulation by MiR-129-5 p is a potential therapeutic target for gastric cancer.
引文
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[19]Shannon P, Markiel A, Ozier O, et al. Cytoscape:a software environment for integrated models of biomolecular interaction networks[J]. Genome Res, 2003, 13(11):2498-504.
[20]Tang Z, Li C, Kang B, et al. GEPIA:a web server for cancer and normal gene expression profiling and interactive analyses[J].Nucleic Acids Res, 2017, 45(W1):W98-102.
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[23]Sun C, Yuan Q, Wu D, et al. Identification of core genes and outcome in gastric cancer using bioinformatics analysis[J]. Oncotarget, 2017,8(41):70271-80.
[24]Brugge J, Hung MC, Mills GB. A new mutational AKTivation in the PI3K pathway[J]. Cancer Cell, 2007, 12(2):104-7.
[25]Hao NB, Tang B, Wang GZ, et al. Hepatocyte growth factor(HGF)upregulates heparanase expression via the PI3K/Akt/NF-kappaB signaling pathway for gastric cancer metastasis[J]. Cancer Lett,2015, 361(1):57-66.
[26]Zhuo C, Li X, Zhuang H, et al. Elevated THBS2, COL1A2, and SPP1expression levels as predictors of gastric cancer prognosis[J]. Cell Physiol Biochem, 2016, 40(6):1316-24.
[27]Li J, Ding Y, Li A. Identification of COL1A1 and COL1A2 as candidate prognostic factors in gastric cancer[J]. World J Surg Oncol, 2016, 14(1):297.
[28] Hayashi M, Nomoto S, Hishida M, et al. Identification of the collagen type 1α1 gene(COL1A1)as a candidate survival-related factor associated with hepatocellular carcinoma[J]. BMC Cancer,2014, 14(1):108.
[29]Wolf K, Alexander S, Schacht V, et al. Collagen-based cell migration models in vitro and in vivo[J]. Semin Cell Dev Biol, 2009, 20(8):931-41.
[30]Yu PN, Yan MD, Lai HC, et al. Downregulation of miR-29contributes to cisplatin resistance of ovarian cancer cells[J]. Int J Cancer, 2013, 134(3):542-51.
[31]Sionov RV. MicroRNAs and glucocorticoid-induced apoptosis in lymphoid malignancies[J]. ISRN Hematol, 2013, 2013(13):348212.
[32]Han H, Li W, Shen H, et al. microRNA-129-5p, a c-Myc negative target, affects hepatocellular carcinoma progression by blocking the Warburg effect[J]. J Mol Cell Biol, 2016, 8(5):400-10.
[33]王球玉,唐珺,周佽想,等. miR-129在乳腺癌中表达下调及其对乳腺癌细胞迁移运动的影响[J].生理学报, 2012, 64(4):403-11.
[34]Wang Q, Yu J. MiR-129-5p suppresses gastric cancer cell invasion and proliferation by inhibiting COL1A1[J]. Biochem Cell Biol,2018, 96(1):19-25.
[2] Orditura M, Galizia G, Sforza V, et al. Treatment of gastric cancer[J].World J Gastroenterol, 2014, 20(7):1635-49.
[3] Malvezzi M, Bonifazi M, Bertuccio P, et al. An age-period-cohort analysis of gastric cancer mortality from 1950 to 2007 in Europe[J].Ann Epidemiol, 2010, 20(12):898-905.
[4] Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012[J].CA Cancer J Clin, 2015, 65(2):87-108.
[5] Moghbeli M, Makhdoumi Y, Soltani Delgosha M, et al. ErbB1 and ErbB3 co-over expression as a prognostic factor in gastric cancer[J].Biol Res, 2019, 52(1):2.
[6]吴永伟,赵刚. CEA、CA724、CA199与PGⅠ、PGⅡ、PGR联合检测在胃癌早期诊断中的价值分析[J].川北医学院学报, 2018, 33(6):836-9.
[7] Hasholzner U, Baumgartner L, Stieber P, et al. Clinical signifcance of the tumour markers CA 125 II and CA 72-4 in ovarian carcinoma[J]. Int J Cancer, 1996, 69(4):329-34.
[8] Luo G, Liu C, Guo M, et al. Potential Biomarkers in Lewis Negative Patients With Pancreatic Cancer[J]. Ann Surg, 2017, 265(4):800-05.
[9] Barcus CE, O'Leary KA, Brockman JL, et al. Elevated collagen-I augments tumor progressive signals, intravasation and metastasis of prolactin-induced estrogen receptor alpha positive mammary tumor cells[J]. Breast Cancer Res, 2017, 19(1):9.
[10]Ibanez de Caceres I, Dulaimi E, Hoffman AM, et al. Identification of novel target genes by an epigenetic reactivation screen of renal cancer[J]. Cancer Res, 2006, 66(10):5021-8.
[11]Lv J, Guo L, Wang JH, et al. Biomarker identification and transregulatory network analyses in esophageal adenocarcinoma and Barrett's esophagus[J]. World J Gastroenterol, 2019, 25(2):233-44.
[12]Zang S, Guo R, Xing R, et al. Identification of differentiallyexpressed genes in intestinal gastric cancer by microarray analysis[J]. Genom Proteom Bioinformat, 2014, 12(6):276-83.
[13]Tinker AV, Boussioutas A, Bowtell DD. The challenges of gene expression microarrays for the study of human cancer[J]. Cancer Cell, 2006, 9(5):333-9.
[14]Davis S, Meltzer PS. GEOquery:a bridge between the Gene Expression Omnibus(GEO)and BioConductor[J]. Bioinformatics,2007, 23(14):1846-7.
[15]Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[J]. Nat Protoc, 2009, 4(1):44-57.
[16]Sherman BT, Huang DW, Tan Q, et al. DAVID Knowledgebase:a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis[J].BMC Bioinformatics, 2007, 8:426.
[17]Jiao X, Sherman BT, Huang D W, et al. DAVID-WS:a stateful web service to facilitate gene/protein list analysis[J]. Bioinformatics,2012, 28(13):1805-6.
[18]Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10:proteinprotein interaction networks, integrated over the tree of life[J].Nucleic Acids Res, 2015, 43(Database issue):D447-52.
[19]Shannon P, Markiel A, Ozier O, et al. Cytoscape:a software environment for integrated models of biomolecular interaction networks[J]. Genome Res, 2003, 13(11):2498-504.
[20]Tang Z, Li C, Kang B, et al. GEPIA:a web server for cancer and normal gene expression profiling and interactive analyses[J].Nucleic Acids Res, 2017, 45(W1):W98-102.
[21]Zong L, Abe M, Seto Y, et al. The challenge of screening for early gastric cancer in China[J]. Lancet, 2016, 388(10060):2606.
[22]杨梦祺,刘盼盼,黄蓬.肿瘤氧化还原代谢与干预[J].中国生化药物杂志, 2016, 36(9):16-23.
[23]Sun C, Yuan Q, Wu D, et al. Identification of core genes and outcome in gastric cancer using bioinformatics analysis[J]. Oncotarget, 2017,8(41):70271-80.
[24]Brugge J, Hung MC, Mills GB. A new mutational AKTivation in the PI3K pathway[J]. Cancer Cell, 2007, 12(2):104-7.
[25]Hao NB, Tang B, Wang GZ, et al. Hepatocyte growth factor(HGF)upregulates heparanase expression via the PI3K/Akt/NF-kappaB signaling pathway for gastric cancer metastasis[J]. Cancer Lett,2015, 361(1):57-66.
[26]Zhuo C, Li X, Zhuang H, et al. Elevated THBS2, COL1A2, and SPP1expression levels as predictors of gastric cancer prognosis[J]. Cell Physiol Biochem, 2016, 40(6):1316-24.
[27]Li J, Ding Y, Li A. Identification of COL1A1 and COL1A2 as candidate prognostic factors in gastric cancer[J]. World J Surg Oncol, 2016, 14(1):297.
[28] Hayashi M, Nomoto S, Hishida M, et al. Identification of the collagen type 1α1 gene(COL1A1)as a candidate survival-related factor associated with hepatocellular carcinoma[J]. BMC Cancer,2014, 14(1):108.
[29]Wolf K, Alexander S, Schacht V, et al. Collagen-based cell migration models in vitro and in vivo[J]. Semin Cell Dev Biol, 2009, 20(8):931-41.
[30]Yu PN, Yan MD, Lai HC, et al. Downregulation of miR-29contributes to cisplatin resistance of ovarian cancer cells[J]. Int J Cancer, 2013, 134(3):542-51.
[31]Sionov RV. MicroRNAs and glucocorticoid-induced apoptosis in lymphoid malignancies[J]. ISRN Hematol, 2013, 2013(13):348212.
[32]Han H, Li W, Shen H, et al. microRNA-129-5p, a c-Myc negative target, affects hepatocellular carcinoma progression by blocking the Warburg effect[J]. J Mol Cell Biol, 2016, 8(5):400-10.
[33]王球玉,唐珺,周佽想,等. miR-129在乳腺癌中表达下调及其对乳腺癌细胞迁移运动的影响[J].生理学报, 2012, 64(4):403-11.
[34]Wang Q, Yu J. MiR-129-5p suppresses gastric cancer cell invasion and proliferation by inhibiting COL1A1[J]. Biochem Cell Biol,2018, 96(1):19-25.
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