COL1A1在胃癌中的表达及临床意义
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摘要
目的利用生物信息学手段探讨胃癌中的差异表达基因及其临床意义。方法收集四个GEO胃癌数据集及TCGA胃癌数据集。分析各数据集中癌周组织和癌组织间存在显著表达差异的基因。对整合后的差异表达基因进行GO基因富集分析及KEGG信号通路富集分析。同时对差异表达基因编码的蛋白质进行蛋白互作网络分析。最后利用TCGA数据集分析了COL1A1基因的表达水平及其与患者TNM分期和生存的关系。结果整合四个GEO数据集的结果发现在癌周组织和癌组织间存在105个具有显著表达差异的基因,其中34个基因上调,71个基因下调。其中COL1A1可能参与细胞外基质的调控,处于蛋白互作网络的核心地位。在四个GEO数据集和TCGA数据集中COL1A1均在胃癌组织中存在高表达。COL1A1在较晚TNM分期的患者组织中存在高表达,COL1A1表达较高的患者预后较差。结论 COL1A1可能作为胃癌的诊断指标、治疗靶点或者预后评估标志物。
Objective To explore the genes with differential expression in gastric cancer and their clinical significance using bioinformatics methods. Methods Four gastric cancer datasets were collected from the GEO database and the TCGA database, respective. Genes with significant expression differences between peritumoral tissues and cancer tissues in each data set were analyzed and integrated. GO gene enrichment analysis and KEGG signal pathway enrichment analysis were performed on the integrated differentially expressed genes. At the same time, protein interaction network analysis was performed on proteins encoded by differentially expressed genes. Finally, the expression level of COL1 A1 gene and its relationship with TNM staging and survival were analyzed by TCGA dataset. Results The integration of four GEO datasets revealed that there were 105 genes with significant expression differences between cancerous and cancerous tissues, of which 34 genes were up-regulated and 71 genes were down-regulated. COL1 A1 might participate in the regulation of extracellular matrix and was at the core of protein interaction network. COL1 A1 was highly expressed in gastric cancer tissues in the four GEO datasets and the TCGA dataset. COL1 A1 was highly expressed in patients with late TNM stage, and patients with higher COL1 A1 expression had a poor prognosis. Conclusion The collagen regulation-related gene represented by COL1 A1 may be used as a diagnostic index, therapeutic target or prognostic evaluation marker for gastric cancer.
Objective To explore the genes with differential expression in gastric cancer and their clinical significance using bioinformatics methods. Methods Four gastric cancer datasets were collected from the GEO database and the TCGA database, respective. Genes with significant expression differences between peritumoral tissues and cancer tissues in each data set were analyzed and integrated. GO gene enrichment analysis and KEGG signal pathway enrichment analysis were performed on the integrated differentially expressed genes. At the same time, protein interaction network analysis was performed on proteins encoded by differentially expressed genes. Finally, the expression level of COL1 A1 gene and its relationship with TNM staging and survival were analyzed by TCGA dataset. Results The integration of four GEO datasets revealed that there were 105 genes with significant expression differences between cancerous and cancerous tissues, of which 34 genes were up-regulated and 71 genes were down-regulated. COL1 A1 might participate in the regulation of extracellular matrix and was at the core of protein interaction network. COL1 A1 was highly expressed in gastric cancer tissues in the four GEO datasets and the TCGA dataset. COL1 A1 was highly expressed in patients with late TNM stage, and patients with higher COL1 A1 expression had a poor prognosis. Conclusion The collagen regulation-related gene represented by COL1 A1 may be used as a diagnostic index, therapeutic target or prognostic evaluation marker for gastric cancer.
引文
[1]Li H,Liu JW,Liu S,et al. Bioinformatics-Based Identification of Methylated-Differentially Expressed Genes and Related Pathways in Gastric Cancer[J]. Dig Dis Sci,2017,62(11):3029-3039.
[2]ShenX,LiP,XuY,etal.Associationofsirtuinswith clinicopathological parameters and overall survival in gastric cancer[J].Oncotarget,2017,8(43):74359-74370.
[3]Sun C,YuanQ,Wu D,et al. Identificationof core genes and outcome in gastric cancer using bioinformatics analysis[J].Oncotarget,2017,8(41):70271-70280.
[4]ZhaoL,LeiH,ShenL,etal.Prognosisgenesingastric adenocarcinoma identified by cross talk genes in diseaserelated pathways[J]. Mol Med Rep,2017,16(2):1232-1240.
[5]Tirino G,Pompella L,Petrillo A,et al. What's New in Gastric Cancer:The Therapeutic Implications of Molecular Classifications and Future Perspectives[J]. Int J Mol Sci,2018,19(9):2659.
[6]Xu G,Li K,Zhang N,et al. Screening Driving Transcription Factors in the Processing of Gastric Cancer[J]. Gastroenterol Res Pract,2016(2016):8431480.
[7]Panarese I,De Vita F,Ronchi A,et al. Predictive biomarkers along gastric cancer pathogenetic pathways[J]. Expert Rev Anticancer Ther,2017,17(5):417-425.
[8]Jang M,Koh I,Lee JE,et al. Increased extracellular matrix density disrupts E-cadherin/beta-catenin complex in gastric cancer cells[J].Biomater Sci,2018,6(10):2704-2713.
[9]Huang R,Gu W,Sun B,et al. Identification of COL4A1 as a potential gene conferring trastuzumab resistance in gastric cancer based on bioinformatics analysis[J]. Mol Med Rep,2018,17(5):6387-6396.
[10]Roncati L,Manenti A,Barbolini G,et al. Deep inside of gastric signet-ring cell carcinoma[J]. Neoplasma,2018,65(4):579-584.
[11]Climent M,Pera M,Aymar I,et al. Bone health in long-term gastric cancer survivors:A prospective study of high-dose vitamin D supplementation using an easy administration scheme[J]. J Bone Miner Metab,2018,36(4):462-469.
[12]Zhou ZH,Ji CD,Xiao HL,et al. Reorganized Collagen in the Tumor Microenvironment of Gastric Cancer and Its Association with Prognosis[J]. J Cancer,2017,8(8):1466-1476.
[13]Jang M,Koh I,Lee SJ,et al. Droplet-based microtumor model to assess cell-ECM interactions and drug resistance of gastric cancer cells[J]. Sci Rep,2017,27(7):41541.
[2]ShenX,LiP,XuY,etal.Associationofsirtuinswith clinicopathological parameters and overall survival in gastric cancer[J].Oncotarget,2017,8(43):74359-74370.
[3]Sun C,YuanQ,Wu D,et al. Identificationof core genes and outcome in gastric cancer using bioinformatics analysis[J].Oncotarget,2017,8(41):70271-70280.
[4]ZhaoL,LeiH,ShenL,etal.Prognosisgenesingastric adenocarcinoma identified by cross talk genes in diseaserelated pathways[J]. Mol Med Rep,2017,16(2):1232-1240.
[5]Tirino G,Pompella L,Petrillo A,et al. What's New in Gastric Cancer:The Therapeutic Implications of Molecular Classifications and Future Perspectives[J]. Int J Mol Sci,2018,19(9):2659.
[6]Xu G,Li K,Zhang N,et al. Screening Driving Transcription Factors in the Processing of Gastric Cancer[J]. Gastroenterol Res Pract,2016(2016):8431480.
[7]Panarese I,De Vita F,Ronchi A,et al. Predictive biomarkers along gastric cancer pathogenetic pathways[J]. Expert Rev Anticancer Ther,2017,17(5):417-425.
[8]Jang M,Koh I,Lee JE,et al. Increased extracellular matrix density disrupts E-cadherin/beta-catenin complex in gastric cancer cells[J].Biomater Sci,2018,6(10):2704-2713.
[9]Huang R,Gu W,Sun B,et al. Identification of COL4A1 as a potential gene conferring trastuzumab resistance in gastric cancer based on bioinformatics analysis[J]. Mol Med Rep,2018,17(5):6387-6396.
[10]Roncati L,Manenti A,Barbolini G,et al. Deep inside of gastric signet-ring cell carcinoma[J]. Neoplasma,2018,65(4):579-584.
[11]Climent M,Pera M,Aymar I,et al. Bone health in long-term gastric cancer survivors:A prospective study of high-dose vitamin D supplementation using an easy administration scheme[J]. J Bone Miner Metab,2018,36(4):462-469.
[12]Zhou ZH,Ji CD,Xiao HL,et al. Reorganized Collagen in the Tumor Microenvironment of Gastric Cancer and Its Association with Prognosis[J]. J Cancer,2017,8(8):1466-1476.
[13]Jang M,Koh I,Lee SJ,et al. Droplet-based microtumor model to assess cell-ECM interactions and drug resistance of gastric cancer cells[J]. Sci Rep,2017,27(7):41541.