Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis.
Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis.
复制标题
通过综合生物信息学分析鉴定肝细胞癌的枢纽基因和生物学通路
作者：
Zhao Q;Zhang Y;Shao S;Sun Y;Lin Z
背景肝细胞癌（Hepatocellular carcinoma，HCC）是人类肝癌的主要类型，是世界上最常见、最致命的恶性肿瘤之一。本研究的目的是通过整合生物信息学分析来确定枢纽基因和关键生物学通路。方法建立基于基因共表达网络（GCN）分析的生物信息学管道，对肝癌基因表达谱进行分析。首先，通过Pearson相关分析，筛选差异表达基因，构建GCN。采用3种不同的社区检测算法对基因模块进行识别，并对基因模块与临床指标进行相关性分析。此外，我们使用搜索工具检索的相互作用基因（STRING）数据库构建蛋白质蛋白质相互作用（PPI）的关键基因模块的网络，我们确定了枢纽基因使用9个拓扑分析算法在这个PPI网络。此外，我们使用Oncomine分析，生存分析，GEO数据集和随机森林算法来验证枢纽基因在肝癌中的重要作用。最后，我们使用另一个GEO数据（GSE 73003）探索了枢纽基因的甲基化变化。结果首先，在表达谱中，共鉴定出4 130个上调基因和471个下调基因。然后，采用模块化程度最高的多层次算法将GCN划分为9个基因模块。此外，一个关键基因模块（m1）被确定。GO富集m1的生物学过程主要包括有丝分裂和减数分裂过程以及催化和脱氧核糖核酸外切酶活性的作用。此外，这些基因在细胞周期和有丝分裂途径中富集。此外，我们还鉴定了11个在HCC中起关键作用的枢纽基因，即MCM3、TRMT6、AURKA、CDC 20、TOP2A、ECT 2、TK 1、MCM2、FEN 1、NCAPD 2和KPNA 2。多种验证方法的结果表明，这11个枢纽基因对区分肿瘤和正常组织具有较高的诊断效率。CDC20、TOP2A、TK1、FEN1基因甲基化在肝癌组织中的差异有统计学意义（P < 0.05）。结论MCM3、TRMT6、AURKA、CDC20、TOP2A、ECT2、TK1、MCM2、FEN1、NCAPD2和KPNA2可能是HCC潜在的生物标志物或治疗靶点。同时，代谢途径、细胞周期和有丝分裂途径可能在肝癌的发生发展中起重要作用。
Background Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis. Methods A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003). Results Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05). Conclusion MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.
登录
查看更多内容
影响因子：
2.7
作者：
Chen J;Wang X;Hu B;He Y;Qian X;Wang W
通讯作者：
Wang W
影响因子：
19
作者：
Feng Z;Levine AJ
通讯作者：
Levine AJ
影响因子：
64.5
作者：
Goodarzi H;Liu X;Nguyen HC;Zhang S;Fish L;Tavazoie SF
通讯作者：
Tavazoie SF
DOI：
10.1016/j.bbrc.2017.03.075
发表时间：
2017-04-29
影响因子：
3.1
作者：
Chen, Chenlin;Song, Guangyuan;Zhan, Yinchu
通讯作者：
Zhan, Yinchu
影响因子：
5.2
作者：
Hua, Shengni;Ji, Zhonghua;Lu, Ligong
通讯作者：
Lu, Ligong