综合生物信息学分析鉴定与胃癌预后相关关键基因

唐永曜; 宋晶; 缪诗琪; 蔡璟; 宋方洲

doi:10.16016/j.2097-0927.202201028

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综合生物信息学分析鉴定与胃癌预后相关关键基因

基础医学 | 更新时间：2026-01-09

- 综合生物信息学分析鉴定与胃癌预后相关关键基因
- Identification of hub genes associated with prognosis of gastric cancer by integrated bioinformatics analysis
- 陆军军医大学学报 2022年44卷第6期页码：522-532
- 作者机构：
  
  1.400016 重庆，重庆医科大学基础医学院：分子医学与肿瘤研究中心
  2.400016 重庆，重庆医科大学基础医学院：生物信息教研室
- 作者简介：
  
  [宋方洲，E-mail：fzsong@cqmu.edu.cn SONG Fangzhou, E-mail: fzsong@cqmu.edu.cn
- 基金信息：
  
  重庆市技术创新与应用发展专项重点项目(cstc2018jscx-mszdX0031)
- DOI：10.16016/j.2097-0927.202201028
  中图分类号： R318.04;R730.7;R735.2
- 收稿：2022-01-05，
  
  修回：2022-02-25，
  
  纸质出版：2022-03-30
- 稿件说明：
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唐永曜, 宋晶, 缪诗琪, 等. 综合生物信息学分析鉴定与胃癌预后相关关键基因[J]. 陆军军医大学学报, 2022,44(6):522-532.

Yongyao TANG, Jing SONG, Shiqi MIAO, et al. Identification of hub genes associated with prognosis of gastric cancer by integrated bioinformatics analysis[J]. Journal of Army Medical University, 2022, 44(6): 522-532.
唐永曜, 宋晶, 缪诗琪, 等. 综合生物信息学分析鉴定与胃癌预后相关关键基因[J]. 陆军军医大学学报, 2022,44(6):522-532. DOI： 10.16016/j.2097-0927.202201028.

Yongyao TANG, Jing SONG, Shiqi MIAO, et al. Identification of hub genes associated with prognosis of gastric cancer by integrated bioinformatics analysis[J]. Journal of Army Medical University, 2022, 44(6): 522-532. DOI： 10.16016/j.2097-0927.202201028.

摘要

目的

综合生物信息学方法分析识别与胃癌预后相关关键基因，探讨其能否作为胃癌预后预测的潜在生物标志物。

方法

从美国高通量基因表达(Gene Expression Omnibus

GEO)数据库下载胃癌患者基因芯片数据集(GSE79973)并在癌症和肿瘤基因图谱(The Cancer Genome Atlas

TCGA)数据库中获取胃癌转录组数据和相应临床资料。利用R软件中的Limma和WGCNA程序包筛选差异共表达基因。使用ClusterProfler程序包对差异共表达基因进行GO和KEGG富集分析。采用STRING数据库对差异共表达基因进行蛋白质相互作用(protein-protein interaction，PPI)网络分析，并用Cytoscape软件筛选关键基因。通过在线工具GEPIA2验证关键基因的转录表达情况，利用Kaplan-Meier单因素生存分析鉴定与胃癌预后相关的关键基因。最后采用RT-qPCR和免疫组化方法验证预后相关关键基因的差异表达。

结果

共筛选出18个差异共表达基因，通过PPI网络分析，识别出10个关键基因(

TNFRSF

、

HOXC

10、

HOXA

10、

TEAD

4、

GKN

2、

GKN

1、

SALL

4、

SGK

1、

ANGPT

2、

NKX

6-2)。生存分析结果显示

HOXA

10(

=0.014)和

ANGPT

=0.002)的高表达可能与胃癌患者预后生存较差有关，RT-qPCR和免疫组化结果显示其在胃癌组织中高表达(

0.05)。

结论

HOXA

10和

ANGPT

2是胃癌预后相关关键基因，可作为胃癌临床治疗和预后预测的潜在生物标志物。

Abstract

Objective

To identify the hub genes associated with development of gastric cancer (GC) by integrated bioinformatics analysis

and investigate whether these genes can be used as latent biomarkers of predicting GC prognosis.

Methods

The gastric cancer of microarray data was downloaded from the Gene Expression Omnibus (GEO) database (GSE79973)

and the transcriptome data and corresponding clinical data of GC patients were obtained from The Cancer Genome Atlas (TCGA) database. Differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) were performed on the dataset using the Limma and WGCNA packages in the R software

respectively

in order to screen differentially co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differential co-expression genes were performed using the ClusterProfler package. The search tool for the retrieval of interacting genes (STRING) was carried out the protein-protein interaction (PPI) network of differential co-expression genes

and the software Cytoscape was used to visualize and screen out hub genes. The transcriptional expression of the hub gene was further verified by GEPIA2 database. Survival analysis was performed by using the survival package in R software to identify prognostic hub genes. Finally

RT-qPCR and immunohistochemical assay were used to verify the differential expression of hub genes related to prognosis.

Results

A total of 18 differential co-expression genes were screened out in th

is study

and PPI network analysis identified 10 key genes (

TNFRSF

HOXC

HOXA

TEAD

GKN

SALL

SGK

ANGPT

2 and

NKX

6-2). The Results of survival analysis found that the high expression of

HOXA

10(

=0.014) and

ANGPT

=0.002) may be related to the survival and prognosis of GC patients

and RT-qPCR and immunohistochemical assay further verified the high expression of these 2 molecules in GC tissues(

0.05).

Conclusion

HOXA

10 and

ANGPT

2 are hub genes for prognosis of GC

and may be used as latent biomarkers of prediction.

关键词

Keywords

references

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