| 引用本文: | 张思嘉,蔡挺,张顺.基于SNP共表达网络肝癌分子分型及预后分析[J].生物信息学,2022,20(4):247-256. |
| ZHANG Sijia,CAI Ting,ZHANG Shun.Molecular typing of HCC based on SNP co-expression network and prognosis analysis[J].Chinese Journal of Bioinformatics,2022,20(4):247-256. |
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| 摘要: |
| 基于SNP突变数据与mRNA表达谱关联分析,构建一种肝癌分子分型方法并对比不同分型预后的差异,并对不同分型肝癌的发生发展机制进一步研究。首先通过TCGA数据库收集359例肝细胞癌患者的SNP突变数据和mRNA表达数据,采用Wilcoxon秩和检验,筛选突变后差异表达基因,并通过生物信息学工具String和Cytoscape 构建差异表达基因的蛋白互作网络,筛选连接度最高的10个Hub基因。利用Consensus Cluster Plus软件包,基于Hub基因mRNA表达水平构建NMF分子分型模型,再结合生存数据评估各分型患者的预后。最后利用加权基因共表达网络分析(WGCNA),识别与肝癌分子分型相关的模块,并针对关键模块的基因进行通路富集,从而对不同分型肝癌的基因表达谱进行比较。结果:NMF模型将肝癌分为高危、低危2个分型,其中CDKN2A和FOXO1基因对分型贡献度高。生存分析显示低危组患者的生存情况显著优于高危组,高危组富集多个与肿瘤细胞侵蚀、转移、复发过程相关的信号通路,低危组则与细胞周期和胰液分泌相关。本研究在无先验性信息的前提下,基于突变后显著差异表达的Hub基因表达水平构建的肝癌分子分型对肝癌患者预后评估具有一定的指导意义,其中CDKN2A和FOXO1突变是肝癌患者的不良预后因素,针对二者的靶向药研发,可能为肝癌患者提供新的治疗策略。 |
| 关键词: 肝癌 分子分型 共表达网络 SNP |
| DOI：10.12113/202108002 |
| 分类号:Q42 |
| 文献标识码:A |
| 基金项目:华美研究基金项目(No.2019HMKY60)；浙江省消化系统肿瘤诊治及研究重点实验室基金项目(No.2019E10020)；宁波市消化系统肿瘤临床医学研究中心基金项目(No.2019A21003). |
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| Molecular typing of HCC based on SNP co-expression network and prognosis analysis |
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ZHANG Sijia, CAI Ting, ZHANG Shun
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(Department of Experimental Medical Science, Hwamei Hospital, University of Chinese Academy of Sciences, Ningbo 315100,Zhejiang, China)
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| Abstract: |
| A molecular classification method of hepatic carcinoma was constructed based on the correlation analysis between single nucleotide polymorphism(SNP) mutation data and mRNA expression profile, and the difference of prognosis between different subtypes was compared.The occurrence and development mechanism of different molecular classifications of hepatocellular carcinoma(HCC) were investigated. The SNP mutation data and mRNA expression profile data of 359 HCC patients were collected from the TCGA database, and differentially expressed genes after mutations were screened by the Wilcoxon rank sum test, Then, two bioinformatics tools (String, Cytoscape) were applied to construct protein-protein interaction (PPI) network and identify the top ten hub genes with high centrality degree. The non negative matrix factorization(NMF) molecular classification model was built with Consensus Cluster Plus package based on hub gene mRNA expression profile, and the prognosis of different molecular subgroups of HCC patients was assessed combined with survival data. Finally, weighted gene co-expression network analysis (WGCNA) was used to identify co-expression gene modules associated with HCC molecular subtypes. Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis was performed for the comparison of gene expression profiles of HCC patients from different molecular subtypes. Results showed that according to the molecular classification based on NMF, HCC was divided into two subgroups, high-risk and low-risk, and CDKN2Aand FOXO1were two major contributing genes for the subgroups. Survival analysis showed that the overall survival rate of low-risk patients was significantly better than that of high-risk group. High-risk subgroup was enriched in several pathways related to tumor invasion, metastasis, and recurrence. While low-risk subgroup was enriched in cell cycle and pancreatic secretion. Without any prior information, the molecular subtypes of HCC based on deferentially expressed hub gene expression after mutation can be used for treatment decision and prognostic prediction. In this research, mutations of CDKN2Aand FOXO1are considered as two adverse prognostic factors for HCC patients, and targeted drug development for these two genes may provide new therapeutic strategies for HCC patients. |
| Key words: Hepatic carcinoma Molecular classification Co-expression network SNP |
