| 引用本文: | 郁乐,张轶雯,辛文秀,潘宗富,钟里科,黄萍.基于GEO芯片数据的肝癌特征基因生物信息学及预后关联性分析[J].中国现代应用药学,2019,36(17):2177-2182. |
| YU Le,ZHANG Yiwen,XIN Wenxiu,PAN Zongfu,ZHONG Like,HUANG Ping.Bioinformatics and Prognosis Correlation Analysis of Liver Cancer Genes Based on GEO Chip Data[J].Chin J Mod Appl Pharm(中国现代应用药学),2019,36(17):2177-2182. |
|
| 本文已被：浏览 3149次 下载 2167次 |
码上扫一扫！ |
|
|
| 基于GEO芯片数据的肝癌特征基因生物信息学及预后关联性分析 |
|
郁乐1,2, 张轶雯3, 辛文秀3, 潘宗富3, 钟里科3, 黄萍1,3
|
|
1.浙江中医药大学药学院, 杭州 310053;2.杭州市西溪医院, 杭州 310023;3.浙江省肿瘤医院药剂科, 杭州 310022
|
|
| 摘要: |
| 目的 通过生物信息学方法利用GEO芯片数据分析肝癌组织中的特征基因及预后的相关性。方法 在GEO数据库中获取肝癌芯片数据，并利用GEO2R工具分析肝癌组织与正常肝组织之间的差异表达基因；利用GO数据库和KEGG数据库对差异表达基因进行富集和功能注释；基于STRING数据库和Cytoscape软件构建蛋白质互相作用网络（protein-protein interaction，PPI），分析其关键基因；用在线工具Kaplan Meier-Plotter对这些关键基因进行生存分析；用The Human Protein Atlas数据库对这些关键基因在肝癌组织中的蛋白质表达进行免疫组化分析。结果 共鉴定出338个差异表达基因，其中97个为上调基因，241个为下调基因。其中上调的差异表达基因显著富集在细胞核有丝分裂、细胞分裂、有丝分裂成对染色单体分离、成对染色单体凝聚等生物过程；下调的差异表达基因显著富集在环氧化酶P450途径、氧化还原过程、外源性药物分解代谢过程等生物过程。根据PPI网络，对关键模块的24个关键基因进行鉴定，发现这些关键基因的高表达与肝癌患者的生存率低有相关性，并截取关键差异表达基因免疫染色代表性图像。结论 本研究发现的关键差异基因有助于更全面地了解肝癌发生的分子机制，可作为肝癌预后的生物标志物以及潜在的肝癌治疗分子靶点。 |
| 关键词: 肝癌 差异表达基因 生物信息学分析 关键基因 |
| DOI：10.13748/j.cnki.issn1007-7693.2019.17.012 |
| 分类号:R966 |
| 基金项目:国家自然科学基金项目（81503165）；浙江省自然科学基金项目（LQ18H160018，LQ17H310002）；浙江省卫生高层次创新人才（黄萍）；浙江省151人才工程第二层次（黄萍） |
|
| Bioinformatics and Prognosis Correlation Analysis of Liver Cancer Genes Based on GEO Chip Data |
|
YU Le1,2, ZHANG Yiwen3, XIN Wenxiu3, PAN Zongfu3, ZHONG Like3, HUANG Ping1,3
|
|
1.School of Pharmacy, Zhejiang Chinese Medical University, Hangzhou 310053, China;2.Xixi Hospital of Hangzhou, Hangzhou 310023, China;3.Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou 310022, China
|
| Abstract: |
| OBJECTIVE To use the bioinformatics method based on data of GEO chip to analyze the development of the molecular markers and prognosis in liver cancer. METHODS Liver cancer chip data was obtained in CEO database. The differentially expressed genes in liver cancer tissues and normal liver tissues were identified by GEO2R; the pathway enrichment of differentially expressed genes were performed by using KEGG and GO. The protein-protein interaction(PPI) network of differentially expressed genes was constructed by STRING database and visualized by Cytoscape. The perform survival analysis of these key genes were analyzed by online tool Kaplan Meier-Plotter. The immunohistochemistry analysis of these key genes were analyzed by The Human Protein Atlas Database. RESULTS A total of 338 differentially expressed genes were identified, including 97 up-regulated genes and 241 down-regulated genes. These up-regulated differentially expressed genes were significantly enriched in mitotic nuclear division, cell division, mitotic sister chromatid segregation, sister chromatid cohesion. These down-regulated differentially expressed genes were significantly enriched in epoxygenase P450 pathway, oxidation-reduction process, exogenous drug catabolic process. The PPI network was constructed a key module by 24 key genes. These key genes were found to be associated with poor survival in patients with liver cancer. Representative images of key differentially expressed genes immunostained were taken. CONCLUSION These key genes are found by this study which contributed to understanding the molecular mechanism of liver cancer, and can be used as a biomarker for the prognosis of liver cancer and a molecular target for the treatment of liver cancer. |
| Key words: liver cancer differentially expressed gene bioinformatics analysis key genes |
