基于生物信息学方法对胰腺癌差异基因表达的分析
Expression Analysis of Different Genes in Pancreas Cancer Using Bioinformatic
DOI: 10.12677/ACM.2020.1012436, PDF,   
作者: 王 朦, 许帮亮：浙江大学医学院，浙江 杭州；朱梦迪：大连医科大学医学院，辽宁 大连；蒋桂星*：浙江大学医学院附属邵逸夫医院肝胆胰外科，浙江 杭州
关键词: 胰腺癌组织；人类；表达；Pancreas Cancer Tissue； Human； Express
摘要: 目的：该文旨在利用生物信息分析方法研究胰腺癌组织的差异表达基因(DEGs)，为进一步实验以及胰腺癌的诊疗和提供新途径。方法：分析来自美国国立生物技术信息中心(NCBI)的公共基因芯片数据库(GEO)中基因芯片数据(GSE15471、GSE16515)，其包括了胰腺癌及对应正常组织的基因表达数据，利用GEO在线分析工具GEO2R分别对两个基因芯片进行分析，初步筛选胰腺癌与正常组织的相关DEGs。对初步筛选出的两组相关差异表达基因进行Veen分析，得到显著DEGs。相关基因功能富集分析由DAVID在线数据库分析得到。同时，通过String在线数据库构建蛋白互作网络(PPI)并使用Cytoscape软件分析PPI网络。结果：设定GEO2R分析参数(|logFC| ≥ 2.0, P < 0.05)，并进行Veen分析，共筛选出111个显著DEGs，其中显著上调DEGs 84个，显著下调DEGs 27个。富集分析显示显著DEGs参与的主要生物过程有：胶原蛋白分解代谢、细胞外基质组织、胶原原纤维组织、细胞外基质分解；细胞成分有：细胞外隙、胞外区、胞外区、细胞外基质外来体；分子功能有：细胞外基质结构成分、钙离子结合、肝素结合。KEGG (Kyoto Encyclopedia of Genes and Genomes)通路分析显示，显著DEGs主要富集于癌症、小细胞肺癌、蛋白质消化吸收、胰腺分泌和PI3K-Akt信号通路。对显著DEGs编码的蛋白质所构建的PPI分析，发现COL1A2、ALB、COL12A1、COL5A1、COL5A2、COL11A1、MMP1、ITGA2、COL8A1SKA1等9个关键蛋白，由此确定关键DEGs。结论：我们的研究提示了胰腺癌与正常胰腺组织间关键DEGs，并发掘关键DEGs的相互作用关系，为胰腺癌的后续研究和诊疗提供新的方向。
Abstract: Objective: The differentially expressed genes (DEGs) in pancreatic cancer tissues were studied by bioinformatics analysis, which provided a new way for further experiments and diagnosis and treatment of pancreatic cancer. Methods: Analysis from the national center for biotechnology information (NCBI) public gene chip databases (GEO) gene chip data (GSE15471, GSE16515), which include the pancreatic cancer and gene expression data of corresponding normal tissues using GEO online analytical tools GEO2R respectively to analyze two gene chip, preliminary screening of pancreatic cancer and normal tissue DEGs. Veen analysis was performed on the two groups of DEGs, and significant DEGs were obtained. Functional enrichment analysis of prominent DEGs was obtained by DAVID online database analysis. Meanwhile, protein interaction network (PPI) was constructed through String online database and ANALYZED by Cytoscape software. Results: A total of 111 prominent DEGs were screened out, including 84 upregulated and 27 downregulated genes (|logFC| ≥ 2.0, P < 0.05). Enrichment analysis showed that prominent DEGs were involved in the following biological processes: collagen catabolism, extracellular matrix tissue, collagen fibril tissue, extracellular matrix decomposition. The cell components include: extracellular space, extracellular region, extracellular region and extracellular matrix. Molecular functions include: extracellular matrix structural components, calcium ion binding, heparin binding. Results of KEGG pathway analysis showed that prominent DEGs were mainly concentrated in cancer, small cell lung cancer, protein digestion and absorption, pancreatic secretion and PI3K-Akt signaling pathways. PPI analysis of proteins encoded with significant DEGs revealed nine key proteins, including COL1A2, ALB, COL12A1, COL5A1, COL5A2, COL11A1, MMP1, ITGA2, and COL8A1SKA1, thus determining the key differential genes. Conclusion: Our study indicated that the key differentially expressed genes between pancreatic cancer and normal pancreatic tissue, and explored the interaction relationship between the key DEGs, providing a new direction for the follow-up research and diagnosis of pancreatic cancer.
文章引用：王朦, 朱梦迪, 许帮亮, 蒋桂星. 基于生物信息学方法对胰腺癌差异基因表达的分析[J]. 临床医学进展, 2020, 10(12): 2883-2889. https://doi.org/10.12677/ACM.2020.1012436

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