基于加权基因共表达网络分析筛选结直肠癌潜在的预后生物标志物

doi:10.3969/j.issn.1673-8640.2023.09.004

摘要/Abstract

摘要：

目的采用生物信息学分析方法挖掘结直肠癌（CRC）关键基因，作为潜在的预后生物标志物。方法从基因表达综合（GEO）数据库获取与CRC相关的GSE33113数据集。采用R软件中的limma程序包获取CRC组织与癌旁组织的差异表达基因，采用DAVID在线工具对差异表达基因进行基因本体论（GO）和京都基因与基因组数据库（KEGG）富集分析。采用加权基因共表达网络分析（WGCNA）输出关键模块基因，取模块关键基因与差异表达基因的交集作为候选关键基因。采用GEPIA数据库进行检索，将CRC组织与癌旁组织中表达存在显著差异，且与疾病预后相关的基因作为关键基因。选用不同数据集验证关键基因在CRC中的表达情况，采用GEPIA数据库验证关键基因在CRC中的表达情况和预后价值。采用受试者工作特征（ROC）曲线评估关键基因对CRC的诊断价值。采用基因集富集分析（GSEA）探索关键基因在CRC中的生物学意义。采用实时荧光定量聚合酶链反应（RT-qPCR）和免疫印迹法检测关键基因在人正常肠上皮细胞系HIEC和CRC细胞系HCT116、HCT15中的表达情况。结果 GSE33113数据集共包含90例CRC样本和6例癌旁组织样本，共筛出差异表达基因1 211个，其中上调505个、下调706个，多分布于细胞外间隙和胞外区，调控趋化因子介导的信号通路、炎症应答、细胞分裂等。WGCNA获取临床特征最显著相关的模块分别有24和96个关键模块基因，分别获得24和62个候选关键基因。经GEPIA数据库筛选，共获取6个关键基因（AQP8、PBK、EXO1、CCNB1、DEPDC1B和KPNA2）。选取EXO1进行验证，其在不同数据集中的CRC组织样本中表达均上调，GEPIA数据库分析结果显示，CRC组织中EXO1 mRNA呈高表达，CRC患者EXO1的表达与患者总生存期（OS）显著相关。ROC曲线分析结果显示，EXO1对CRC具有较高的诊断价值。GSEA结果提示EXO1可能通过影响细胞周期和DNA复制来调控CRC。RT-qPCR和免疫印迹法结果显示，EXO1在CRC细胞中的表达显著上调。结论采用WGCNA发现6个与CRC相关的关键基因，其中EXO1可作为CRC潜在的预后生物标志物。

关键词: 差异表达基因, 加权基因共表达网络分析, EXO1, 基因集富集分析, 结直肠癌

Abstract:

Objective To investigate hub genes related to colorectal cancer（CRC） as potential prognostic biomarkers by bioinformatics analysis. Methods The GSE33113 dataset related to CRC were obtained from Gene Expression Omnibus（GEO） database. Differentially expressed genes from CRC tissues and adjacent tissues were obtained using limma program package in R software，and Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analysis of differentially expressed genes were performed with the DAVID online tool. Weighted gene co-expression network analysis（WGCNA） was used to output key module genes，and the intersection of key module genes and differentially expressed genes was taken as candidate hub genes. GEPIA database was used to verify the expression and prognostic value of hub genes in CRC. The diagnostic value of the hub genes for CRC was evaluated by receiver operating characteristic（ROC） curve. Gene set enrichment analysis（GSEA） was performed to explore the biological significance of hub genes in CRC. Human normal intestinal epithelial cell HIEC cell line，CRC cell line，HCT116 and HCT15 were used to deterimine the expression of hub genes by real-time quantitative polymerase chain reaction（RT-qPCR） and western blotting. Results GSE33113 dataset contains 90 CRC tissues and 6 adjacent tissues. A total of 1 211 differentially expressed genes were screened，including 505 up-regulated cases and 706 down-regulated cases，which were mostly distributed in extracellular space and extracellular region，regulating chemokine mediated signaling pathways，inflammatory response，cell division and so on. WGCNA analysis revealed 24 and 96 key module genes were most significantly correlated with clinical features，and 24 and 62 candidate hub genes were obtained，respectively. A total of 6 hub genes（AQP8，PBK，EXO1，CCNB1，DEPDC1B and KPNA2） were acquired after screening in GEPIA database. EXO1 was selected for validation，and its expression was up-regulated in CRC tissues of different datasets. GEPIA database analysis results showed that the expression level of EXO1 mRNA was high in CRC tissues，the expression level of EXO1 in CRC patients was correlated with overall survival（OS），and ROC curve analysis showed that EXO1 had high diagnostic value for CRC. The results of GSEA analysis suggested that EXO1 may regulate CRC by affecting cell cycle and DNA replication. RT-qPCR and western blotting results revealed that the expression of EXO1 was up-regulated in CRC cells. Conclusions Totally，6 hub genes related to CRC have been identified by WGCNA，among which EXO1 may serve as a potential prognostic biomarker for CRC.

Key words: Differentially expressed gene, Weighted gene co-expression network analysis, EXO1, Gene set enrichment analysis, Colorectal cancer

中图分类号:

R446.7

李梁珊, 赵虎, 王诗雯. 基于加权基因共表达网络分析筛选结直肠癌潜在的预后生物标志物[J]. 检验医学, 2023, 38(9): 825-832.

LI Liangshan, ZHAO Hu, WANG Shiwen. Screening potential prognostic biomarkers of colorectal cancer based on weighted gene co-expression network analysis[J]. Laboratory Medicine, 2023, 38(9): 825-832.

图/表 8

图1 GSE33113数据集DEGs火山图注：红色代表上调基因，绿色代表下调基因。

图2 差异表达基因涉及的生物学功能注：（a）GO富集分析；（b）KEGG富集分析。

图3 WGCNA的构建注：（a）样本聚类和性状热图；（b）网络拓扑分析；（c）基因聚类树状图和模块划分；（d）样本特征与模块的相关性分析[r值（P值）]；（e）模块1中基因GS和MM散点图；（f）模块2中基因GS和MM散点图。

图4 癌组织和癌旁组织6个关键基因相对表达量的比较注：（a）AQP8；（b）PBK；（c）EXO1；（d）CCNB1；（e）DEPDC1B；（f）KPNA2；癌组织；癌旁组织。

图5 关键基因表达CRC与OS患者的关系注：高表达组；低表达组。

图6 关键基因EXO1验证注：（a）EXO1处于数据集GSE33113、GSE44076和GSE110224的DEGs的交集中；（b）EXO1与CRC患者DFS的关系； EXO1低表达， EXO1高表达；（c）EXO1与CRC患者TNM分期的相关性；（d）EXO1诊断CRC的ROC曲线。

图7 EXO1高表达GSEA富集分析结果注：（a）细胞周期；（b）DNA复制。

图8 CRC细胞中EXO1的表达情况

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