Suppr超能文献

基于TCGA数据库表达分析的肺腺癌关键预后基因筛选

Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database.

作者信息

Shen Youfeng, Tang Xiaoqing, Zhou Xiaoqin, Yi Yuanxue, Qiu Yuan, Xu Jian, Tian Xingzhong

机构信息

Chongqing Precision Medical Industry Technology Research Institute, Chongqing 400000, China.

Department of Laboratory Medicine, Nanan People's Hospital, Chongqing 400060, China.

出版信息

J Oncol. 2022 Dec 26;2022:4435092. doi: 10.1155/2022/4435092. eCollection 2022.

Abstract

OBJECTIVE

The data of lung adenocarcinoma- (LUAD-) related gene expression profiles were mined from the Cancer Genome Atlas (TCGA) database using bioinformatics methods and potential biomarkers related to the occurrence, development, and prognosis of LUAD were screened out to explore the key prognostic genes and clinical significance.

METHODS

Following the LUAD gene expression profile data that were initially exported from the TCGA database, R software DESeq2 was employed to analyze the difference between the expression profiles of LUAD and normal tissues. The R package "clusterProfiler" was subsequently utilized to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the differential genes. A protein-protein interaction (PPI) network was constructed via the String database, and cytohubba, a plugin of Cytoscape, was applied to screen hub genes using the MCC algorithm. The Gene Expression Profile Data Interactive Analysis (GEPIA) was used to analyze expressions of 10 candidate genes in LUAD samples and healthy lung samples, and the selected genes were employed for survival analysis.

RESULTS

A total of 1,598 differential genes were identified through differential analyses and data mining, with 1,394 genes upregulated and 204 downregulated. A total of 10 hub genes CCNA2, CDC20, CCNB2, KIF11, TOP2A, BUB1, BUB1B, CENPF, TPX2, and KIF2C were obtained using the cytohubba plugin. The results of the GEPIA analysis indicated that compared with normal lung tissue, the mRNA expression level of the described hub genes in LUAD tissue was significantly increased ( < 0.05). Survival analysis revealed that these genes had a significant impact on the overall survival time of LUAD patients ( < 0.05).

CONCLUSION

The previously described key genes related to LUAD identified in the TCGA database may be used as potential prognostic biomarkers, which will contribute to further comprehension of the occurrence and development of LUAD and provide references for its diagnosis and treatment.

摘要

目的

利用生物信息学方法从癌症基因组图谱(TCGA)数据库中挖掘肺腺癌(LUAD)相关基因表达谱数据,筛选出与LUAD发生、发展及预后相关的潜在生物标志物,以探索关键预后基因及其临床意义。

方法

在最初从TCGA数据库导出的LUAD基因表达谱数据基础上,使用R软件DESeq2分析LUAD与正常组织表达谱之间的差异。随后利用R包“clusterProfiler”对差异基因进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路分析。通过String数据库构建蛋白质-蛋白质相互作用(PPI)网络,并使用Cytoscape的插件cytohubba通过MCC算法筛选枢纽基因。利用基因表达谱数据交互式分析(GEPIA)分析10个候选基因在LUAD样本和健康肺样本中的表达情况,并对所选基因进行生存分析。

结果

通过差异分析和数据挖掘共鉴定出1598个差异基因,其中1394个基因上调,204个基因下调。使用cytohubba插件共获得10个枢纽基因CCNA2、CDC20、CCNB2、KIF11、TOP2A、BUB1、BUB1B、CENPF、TPX2和KIF2C。GEPIA分析结果表明,与正常肺组织相比,LUAD组织中上述枢纽基因的mRNA表达水平显著升高(<0.05)。生存分析显示,这些基因对LUAD患者的总生存时间有显著影响(<0.05)。

结论

在TCGA数据库中鉴定出的上述与LUAD相关的关键基因可能作为潜在的预后生物标志物,有助于进一步了解LUAD的发生和发展,并为其诊断和治疗提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a22/9807302/7742f82fc5fc/JO2022-4435092.001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验