摘要： 目的 通过生物信息学的方法分析胰腺癌发生的潜在机制。方法 利用GEOquery分析差异基因表达，利用clusterProfiler进行富集分析。利用STRING数据库进行蛋白相互作用分析。通过TCGA数据库对核心基因进行预后分析。结果 通过差异分析得到277个差异基因。通过富集分析发现，低表达基因主要和胆固醇代谢过程、酒精代谢过程以及消化有关，高表达基因主要和消化系统过程有关。蛋白相互作用分析后找到胰腺癌发生的10个核心基因（ALB、EGF、FN1、COL1A1、COL3A1、ITGA2、COL17A1、CEL、PRSS1和TOP2A）。经过TCGA数据库预后分析发现3个基因（COL17A1、ITGA2、TOP2A）和预后相关。结论 发现了10个胰腺癌发病风险相关的核心基因和3个预后相关基因。这些核心基因可能可以作为胰腺癌发病预测的靶标。

利用生物信息学分析筛选胰腺癌发生的潜在基因及机制

杨明丽¹, 黄哲², 王倩¹, 陈欢欢¹, 马赛男¹, 吴荣¹, 蔡炜嵩¹

1. 中国医科大学附属盛京医院 第二肿瘤内科, 沈阳 110022;
2. 中国医科大学附属盛京医院 普通外科, 沈阳 110015

收稿日期:2019-01-08出版日期:2020-02-29发布日期:2019-12-26
通讯作者:蔡炜嵩E-mail:cailab9@hotmail.com
作者简介:杨明丽(1980-),女,主治医师,硕士.
基金资助:辽宁省自然科学基金（20170541001）


关键词: 富集分析, 胰腺癌, 基因, 分子机制
Abstract: Objective To analyze the mechanisms underlying pancreatic cancer using bioinformatics methods. Methods GEOquery was used to analyze differential gene expression,and clusterProfiler was used for enrichment analysis. Protein interaction analysis was performed using the STRING database. Prognostic analysis of core genes was performed using TCGA database. Results Based on the differential analysis,277 differentially expressed genes were identified. The enrichment analysis revealed that the low-expression genes were mainly involved in cholesterol metabolism,alcohol metabolism,and digestion,and high-expression genes were mainly related to digestive system processes. Protein interaction analysis identified 10 core genes related to pancreatic cancer(ALB,EGF,FN1,COL1A1, COL3A1,ITGA2,COL17A1,CEL,PRSS1,and TOP2A). After prognostic analysis of TCGA database was performed,three genes(COL17A1,ITGA2,and TOP2A supplemented with specific gene names)were found to be associated with prognosis. Conclusion Ten core genes associated with the risk of pancreatic cancer and three genes related to prognosis were identified. These core genes may serve as targets for the prediction of pancreatic cancer incidence.
Key words: enrichment analysis, pancreatic cancer, gene, molecular mechanism
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