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基于生物信息学分析预测和分析肝细胞癌中的加权基因。

Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis.

机构信息

Department of Hepatobiliary Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China.

Department of Anesthesia, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, P.R. China.

出版信息

Mol Med Rep. 2019 Apr;19(4):2479-2488. doi: 10.3892/mmr.2019.9929. Epub 2019 Feb 4.

Abstract

The aim of the present study was to identify the differentially expressed genes (DEGs) between primary tumor tissue and adjacent non‑tumor tissue of hepatocellular carcinoma (HCC) samples in order to investigate the mechanisms of HCC. The microarray data of the datasets GSE76427, GSE84005 and GSE57957 were downloaded from the Gene Expression Omnibus database. DEGs were identified using the limma package in the R programming language. Following the intersection of the DEGs screened from the three datasets, 218 genes were selected for further study. A protein‑protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes database. The construction and analysis of modules were performed using Cytoscape and the module with the highest score was selected for further analysis. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted for genes involved in the PPI network and the selected subnetwork. The network of the enriched pathways and their associated genes was constructed using Cytoscape. For the genes in the global PPI network, metabolism‑associated pathways were significantly enriched; whereas, for the genes in the subnetwork, 'cell cycle', 'oocyte meiosis' and 'DNA replication' pathways were significantly enriched. To demonstrate the portability and repeatability of the prognostic value of the weighted genes, a validation cohort was obtained from datasets of The Cancer Genome Atlas and Kaplan‑Meier survival analysis was conducted. Evidence is presented that the expression levels of aldehyde dehydrogenase 2 family member, cytochrome P450 family 2 subfamily C member 8, alcohol dehydrogenase 4 (class II), pi polypeptide, alcohol dehydrogenase 1B (class I), β polypeptide and cytochrome P450 family 2 subfamily C member 9 were associated with the overall survival of patients with HCC and that the expression levels of pituitary tumor‑transforming 1, cell division cycle 20, DNA topoisomerase II α and cyclin B2 were negatively associated with the overall survival of patients with HCC. In conclusion, 9 weighted genes, involved in the development and progression of HCC, were identified using bioinformatics and survival analyses.

摘要

本研究旨在鉴定原发性肝癌 (HCC) 肿瘤组织和相邻非肿瘤组织样本中的差异表达基因 (DEGs),以探讨 HCC 的发病机制。从基因表达综合数据库中下载数据集 GSE76427、GSE84005 和 GSE57957 的微阵列数据。使用 R 编程语言中的 limma 包鉴定 DEGs。对三个数据集筛选的 DEGs 进行交集后,选择 218 个基因进行进一步研究。使用 Search Tool for the Retrieval of Interacting Genes 数据库构建蛋白质-蛋白质相互作用 (PPI) 网络。使用 Cytoscape 构建和分析模块,并选择得分最高的模块进行进一步分析。对 PPI 网络和选定子网中的基因进行基因本体论富集分析和京都基因与基因组百科全书通路富集分析。使用 Cytoscape 构建富集通路及其相关基因的网络。在全局 PPI 网络的基因中,代谢相关通路显著富集;而在子网的基因中,“细胞周期”、“卵母细胞减数分裂”和“DNA 复制”通路显著富集。为了证明加权基因预后价值的可移植性和可重复性,从癌症基因组图谱数据集获得验证队列,并进行 Kaplan-Meier 生存分析。结果表明,醛脱氢酶 2 家族成员、细胞色素 P450 家族 2 亚家族 C 成员 8、醇脱氢酶 4(类 II)、pi 多肽、醇脱氢酶 1B(类 I)、β 多肽和细胞色素 P450 家族 2 亚家族 C 成员 9 的表达水平与 HCC 患者的总生存率相关,而垂体肿瘤转化 1、细胞分裂周期 20、DNA 拓扑异构酶 IIα 和细胞周期蛋白 B2 的表达水平与 HCC 患者的总生存率呈负相关。总之,通过生物信息学和生存分析鉴定了 9 个与 HCC 发生发展相关的加权基因。

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