加权相关网络分析确定了与胃癌进展和预后相关的纤连蛋白1（FN1）、I型胶原蛋白α1链（COL1A1）和纤溶酶原激活物抑制剂1（SERPINE1）。

Weighted correlation network analysis identifies FN1, COL1A1 and SERPINE1 associated with the progression and prognosis of gastric cancer.

作者信息

Zhao Qiaoyun, Xie Jun, Xie Jinliang, Zhao Rulin, Song Conghua, Wang Huan, Rong Jianfang, Yan Lili, Song Yanping, Wang Fangfei, Xie Yong

机构信息

Department of Gastroenterology, First Affiliated Hospital of Nanchang University, Donghu District, Nanchang, Jiangxi, China.

Laboratory of Biochemistry and Molecular Biology, Jiangxi Institute of Medical Sciences, Donghu District, Nanchang, Jiangxi, China.

出版信息

Cancer Biomark. 2021;31(1):59-75. doi: 10.3233/CBM-200594.

DOI:10.3233/CBM-200594

PMID:33780362

Abstract

BACKGROUND

Gastric cancer (GC) is one of the most deadliest tumours worldwide, and its prognosis remains poor.

OBJECTIVE

This study aims to identify and validate hub genes associated with the progression and prognosis of GC by constructing a weighted correlation network.

METHODS

The gene co-expression network was constructed by the WGCNA package based on GC samples and clinical data from the TCGA database. The module of interest that was highly related to clinical traits, including stage, grade and overall survival (OS), was identified. GO and KEGG pathway enrichment analyses were performed using the clusterprofiler package in R. Cytoscape software was used to identify the 10 hub genes. Differential expression and survival analyses were performed on GEPIA web resources and verified by four GEO datasets and our clinical gastric specimens. The receiver operating characteristic (ROC) curves of hub genes were plotted using the pROC package in R. The potential pathogenic mechanisms of hub genes were analysed using gene set enrichment analysis (GSEA) software.

RESULTS

A total of ten modules were detected, and the magenta module was identified as highly related to OS, stage and grade. Enrichment analysis of magenta module indicated that ECM-receptor interaction, focal adhesion, PI3K-Akt pathway, proteoglycans in cancer were significantly enriched. The PPI network identified ten hub genes, namely COL1A1, COL1A2, FN1, POSTN, THBS2, COL11A1, SPP1, MMP13, COMP, and SERPINE1. Three hub genes (FN1, COL1A1 and SERPINE1) were finally identified to be associated with carcinogenicity and poor prognosis of GC, and all were independent risk factors for GC. The area under the curve (AUC) values of FN1, COL1A1 and SERPINE1 for the prediction of GC were 0.702, 0.917 and 0.812, respectively. GSEA showed that three hub genes share 15 common upregulated biological pathways, including hypoxia, epithelial mesenchymal transition, angiogenesis, and apoptosis.

CONCLUSION

We identified FN1, COL1A1 and SERPINE1 as being associated with the progression and poor prognosis of GC.

摘要

背景

胃癌（GC）是全球最致命的肿瘤之一，其预后仍然很差。

目的

本研究旨在通过构建加权相关网络来识别和验证与GC进展及预后相关的枢纽基因。

方法

基于来自TCGA数据库的GC样本和临床数据，使用WGCNA软件包构建基因共表达网络。识别出与包括分期、分级和总生存期（OS）等临床特征高度相关的感兴趣模块。使用R语言中的clusterProfiler软件包进行GO和KEGG通路富集分析。使用Cytoscape软件识别10个枢纽基因。在GEPIA网络资源上进行差异表达和生存分析，并通过四个GEO数据集和我们的临床胃标本进行验证。使用R语言中的pROC软件包绘制枢纽基因的受试者工作特征（ROC）曲线。使用基因集富集分析（GSEA）软件分析枢纽基因的潜在致病机制。

结果

共检测到10个模块，品红色模块被确定与OS、分期和分级高度相关。品红色模块的富集分析表明，细胞外基质受体相互作用、粘着斑、PI3K - Akt通路、癌症中的蛋白聚糖显著富集。蛋白质 - 蛋白质相互作用（PPI）网络确定了10个枢纽基因，即COL1A1、COL1A2、FN1、POSTN、THBS2、COL11A1、SPP1、MMP13、COMP和SERPINE1。最终确定三个枢纽基因（FN1、COL1A1和SERPINE1）与GC的致癌性和不良预后相关，且均为GC的独立危险因素。FN1、COL1A1和SERPINE1预测GC的曲线下面积（AUC）值分别为0.702、0.917和0.812。GSEA显示，三个枢纽基因共有15条共同上调的生物学通路，包括缺氧、上皮 - 间质转化、血管生成和凋亡。