Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis.
Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis.
复制标题
使用稳健排序聚合结合加权基因共表达网络分析来鉴定与肝细胞癌相关的 Hub 基因
DOI：
10.3389/fgene.2020.00895
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发表时间：
2020
影响因子：
3.7
通讯作者：
Ni C
中科院分区：
文献类型：
--
作者：
Song H;Ding N;Li S;Liao J;Xie A;Yu Y;Zhang C;Ni C
背景生物信息学为探讨肝细胞癌（HCC）的分子发病机制提供了一个有价值的工具。为了改善患者的预后，鉴定与HCC致病途径相关的生物标志物仍然是迫切的研究重点。方法采用Robust Rank Aggregation方法对Gene Expression Omnibus中9个符合要求的HCC数据集进行整合。筛选了肿瘤和正常组织样品之间的一组稳健的差异表达基因（DEG）。应用加权基因共表达网络分析对DEG进行聚类分析，确定与临床性状相关的关键模块。基于网络拓扑分析，挖掘出关键模块中的新风险基因，并进行生物学验证。借助miRNA-mRNA调控网络进一步探索这些风险基因的潜在功能。最后，通过构建临床预测模型评估这些基因的预后能力。结果两个关键模块与临床特征显著相关。结合蛋白质相互作用分析，共鉴定出29个hub基因。在这些基因中，来自一个模块的19个基因在HCC中显示出上调的模式，并且与肿瘤淋巴结转移阶段相关，而来自另一个模块的10个基因显示出相反的趋势。生存分析表明，所有这些基因与患者的预后显着相关。基于miRNA-mRNA调控网络，确定了29个与肿瘤活性密切相关的基因。值得注意的是，ABAT、DAO、PCK 2、SLC 27 A2和HAO 1这五个新的风险基因在以前的研究中很少报道。基因集富集分析揭示了每个基因在HCC增殖和预后中的调控作用。最小绝对收缩和选择运算符回归分析进一步验证了DAO、PCK 2和HAO 1作为外部HCC数据集中的预后因素。结论多数据集结合全球网络信息的分析为揭示肝癌复杂的生物学机制提供了一条成功的途径。更重要的是，这种新的整合策略有助于识别风险中心基因作为HCC的候选生物标志物，这可以有效地指导临床治疗。
Background Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority. Methods We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model. Results Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset. Conclusion Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.