基于加权基因共表达网络分析构建胃癌转移预测模型

龚　超; 陈　魁; 章德昆; 谢径峰; 吴芳华; 黄玉钿; 薛玉钦; 王力群

doi:10.19401/j.cnki.1007-3639.2021.08.008

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基于加权基因共表达网络分析构建胃癌转移预测模型

论著 | 更新时间：2025-12-31

- 基于加权基因共表达网络分析构建胃癌转移预测模型
- Construction of a prediction model for metastasis in gastric cancer based on the weighted gene co-expression network analysis
- 中国癌症杂志 2021年31卷第8期页码：746-753
- 作者机构：
  
  1. 福建医科大学附属福州市第一医院普通外科,福建,福州,350009
  2. 福建医科大学附属福州市第一医院检验科,福建,福州,350009
  3. 福建医科大学附属福州市第一医院病理科,福建,福州,350009
- 作者简介：
- 基金信息：
  
  福州市卫生健康中青年科学研究项目（2019-S-wq1）;福建医科大学启航基金项目（2017XQ1207）。
- DOI：10.19401/j.cnki.1007-3639.2021.08.008
  中图分类号：
- 网络出版：2021-09-03，
  
  纸质出版：2021-09-03
- 稿件说明：
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龚　超, 陈　魁, 章德昆, 谢径峰, 吴芳华, 黄玉钿, 薛玉钦, 王力群 . 基于加权基因共表达网络分析构建胃癌转移预测模型[J]. 中国癌症杂志, 2021, 31(8): 746-753.

龚　超, 陈　魁, 章德昆, et al. Construction of a prediction model for metastasis in gastric cancer based on the weighted gene co-expression network analysis[J]. China Oncology, 2021, 31(8): 746-753.
龚　超, 陈　魁, 章德昆, 谢径峰, 吴芳华, 黄玉钿, 薛玉钦, 王力群 . 基于加权基因共表达网络分析构建胃癌转移预测模型[J]. 中国癌症杂志, 2021, 31(8): 746-753. DOI： 10.19401/j.cnki.1007-3639.2021.08.008.

龚　超, 陈　魁, 章德昆, et al. Construction of a prediction model for metastasis in gastric cancer based on the weighted gene co-expression network analysis[J]. China Oncology, 2021, 31(8): 746-753. DOI： 10.19401/j.cnki.1007-3639.2021.08.008.

摘要

背景与目的：通过对高通量功能基因组数据库（Gene Expression Omnibus，GEO）中一组含有转移和非转移性胃癌以及癌旁组织的基因芯片进行加权基因共表达网络分析（weighted gene co-expression network analysis，WGCNA），筛选出与胃癌发生和转移显著相关的分子，为胃癌的治疗和生存期延长的研究提供参考。方法：采用WGCNA方法对19例胃癌患者基因表达进行差异分析；结合临床数据，选取与临床信息高度相关的基因模块构建网络。结果：利用WGCNA我们筛选出了Lightsteelblue模块与胃癌转移明确相关，同时对模块中的基因进一步进行分析，筛选出4个基因：C5AR1、AP3M2、TYMP、ANXA2P1作为核心靶基因。通过表达分析和受试者工作特征（receiver operating characteristic，ROC）曲线分析验证上述基因与胃癌发生、转移明确相关。同时，通过外部ONCOMINE和Kaplan-Meier plot数据库验证上述基因在胃癌中高表达，高表达这些基因的患者有着更差的预后。并利用GSE14210数据集构建基于这些基因的预测患者预后和疾病进展模型。结果提示我们所筛选的4个基因具有成为潜在胃癌转移和治疗生物标志物的可能。结论：鉴定筛选出与胃癌发生和转移相关的4个基因，可为胃癌发生、转移和治疗的研究提供参考。

Abstract

Background and purpose: This study aimed to screen out the bio-markers related to the occurrence and metastasis of gastric cancer. The weighted gene co-expression network analysis (WGCNA) was performed to analyze the gene chips containing metastatic and non-metastatic gastric cancer and adjacent tissues in Gene Expression Omnibus (GEO) data set. Methods: The gene expression differences of 19 patients with gastric cancer were analyzed by WGCNA. In combination with clinical data

gene modules highly relevant to clinical information were selected to construct the network. Results: Through WGCNA

we screened out the Lightsteelblue module that was mostly related to gastric cancer metastasis. Then we further analyzed the genes in the module

and screened out 4 genes: C5AR1

AP3M2

TYMP

ANXA2P1 as “real” hub genes. Through expression analysis and receiver operating characteristic (ROC) curve analysis

the 4 genes identified were related to the occurrence and metastasis of gastric cancer. Meanwhile

external ONCOMINE and Kaplan-Meier plot databases were used to verify that the above genes were highly expressed in gastric cancer

and patients with high expression of the above genes had a worse prognosis. And the GSE14210 dataset was used to build the risk score model to predict the prognosis and progression of disease. These results suggested that the four genes we screened were potential bio-markers for gastric cancer metastasis and treatment. Conclusion: In the present study

we screened and identified 4 genes related to the occurrence and metastasis of gastric cancer

which could provide evidence for the research on the occurrence

metastasis and treatment of gastric cancer.

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