| 引用本文: | 车运诚,陈梅,张昱,张文静.生物信息学分析筛选结直肠癌靶基因及评估预后价值[J].生物信息学,2021,19(3):195-204. |
| CHE Yuncheng,CHEN Mei,ZHANG Yu,ZHANG Wenjing.Identification of target genes and prognostic biomarkers in colorectal cancer via bioinformatics[J].Chinese Journal of Bioinformatics,2021,19(3):195-204. |
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| 生物信息学分析筛选结直肠癌靶基因及评估预后价值 |
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车运诚1,陈 梅1,张 昱1,2,张文静1,3
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(1．昆明理工大学 医学院, 昆明 650500;2．云南省第一人民医院 消化内科, 昆明 650032;3．云南省第一人民医院 肿瘤内科, 昆明 650032)
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| 摘要: |
| 为寻找与结直肠癌发展和预后相关的潜在关键基因及信号通路。从美国国立信息中心NCBI的GEO数据库获得结直肠癌基因表达数据集GSE106582,通过PCA对样本进行分组,利用GEO2R进行综合分析,筛选结直肠癌与癌旁对照组的差异表达基因；通过DAVID在线工具对差异表达基因进行GO本体分析和KEGG通路富集分析,初步分析差异表达基因的生物学作用；基于STRING数据库对差异表达基因进行蛋白质相互作用网络分析,利用Cytoscape软件进行可视化并筛选关键基因；用生存分析和ROC曲线诊断对关键基因进行鉴定并通过数据集GSE21510进行验证。共鉴定出199个差异表达基因,其中53个为上调基因,146个为下调基因；上调的差异表达基因主要富集在与胶原蛋白分解代谢过程、细胞外基质分解、细胞外基质受体相互作用和PI3K/AKT信号通路等生物学过程；下调的差异表达基因主要富集在碳酸氢盐运输、一碳代谢过程、矿物质吸收、药物代谢-细胞色素P450和氮代谢通路等生物学过程；MCODE分析、生存分析和ROC诊断共发现3个基因分别为 BGN、COL1A2 和 TIMP1 可能与结直肠癌的发生发展有关,它们在肿瘤组织中的异常高表达与患者较差的生存期呈正相关,GSE21510的验证结果与GSE106582的分析结果相同。本研究采用生物信息学方法对CRC基因芯片数据进行挖掘,从基因水平探讨CRC潜在的发病机制、肿瘤标志物的及患者预后分子的筛选,以及可能的药物治疗靶点提供了一定的参考价值和理论基础。 |
| 关键词: 结直肠癌 差异表达基因 生物信息学分析 信号通路 |
| DOI：10.12113/202006006 |
| 分类号:R735.3 |
| 文献标识码:A |
| 基金项目:国家自然科学基金地区项目(No.81860509)；云南省消化疾病内科研究所内设机构项目(No.2016NS6,7NS9,8NS0265). |
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| Identification of target genes and prognostic biomarkers in colorectal cancer via bioinformatics |
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CHE Yuncheng1, CHEN Mei1, ZHANG Yu1,2, ZHANG Wenjing1,3
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(1. School of Medicine, Kunming University of Science and Technology, Kunming 650500, China; 2. Department of Digestive Medicine, The First People's Hospital of Yunnan Province, Kunming 650032, China;3. Department of Oncology, The First People's Hospital of Yunnan Province, Kunming 650032, China)
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| Abstract: |
| The aim of this study is to find potential key genes and signal pathways related to the tumorigenesis, progression, and prognosis of colorectal cancer (CRC). The dataset of colorectal cancer gene expression GSE106582 was obtained from the GEO database of the U.S. National Center for Biotechnology Information(NCBI), and the samples were grouped by PCA. Comprehensive analysis was performed using GEO2R to screen the differentially expressed genes in colorectal cancer and normal tissues; Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID online database for the preliminary analysis of the biological function of the differentially expressed genes; protein interaction network analysis of differentially expressed genes were performed based on the STRING database; Cytoscape software was used for the visualization and screening of key genes; Survival analysis and ROC curve diagnosis were used to identify key genes in CRC and validated by dataset GSE21510. A total of 199 differentially expressed genes were identified in this study, including 53 up-regulated genes and 146 down-regulated genes; the up-regulated differentially expressed genes were mainly enriched in the process of collagen catabolism, extracellular matrix(ECM) disassembly, ECM-receptor interaction, and PI3K/AKT signaling pathway; the down-regulated differentially expressed genes were mainly enriched in biological processes such as bicarbonate transport, one-carbon metabolic process, mineral absorption, drug metabolism-cytochrome P450, and nitrogen metabolism pathway process; three genes, BGN,COL1A2 , and TIMP1, which were predicted by MCODE analysis, survival analysis, and ROC diagnosis, might be closely related to the occurrence and development of colorectal cancer. Their ectopic high-expression in tumor tissue was positively correlated with the poor survival of CRC patients. The verification result of GSE21510 was the same as the analysis result of GSE106582. This study explored the potential molecular mechanism of tumorigenesis and progression in CRC via a series of bioinformatics analysis methods. It also revealed several potential prognostic molecules, which can provide certain theoretical and practical significance for target therapy in CRC. |
| Key words: Colorectal cancer Differentially expressed genes Informatics analysis Signaling pathways |
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