基于肝细胞癌的基因芯片数据分析差异共表达基因。

Analysis of differentially co-expressed genes based on microarray data of hepatocellular carcinoma.

出版信息

Neoplasma. 2017;64(2):216-221. doi: 10.4149/neo_2017_207.

DOI:10.4149/neo_2017_207

Abstract

Hepatocellular carcinoma (HCC) is the third leading cause of cancer related death worldwide. Although great progress in diagnosis and management of HCC have been made, the exact molecular mechanisms remain poorly understood. The study aims to identify potential biomarkers for HCC progression, mainly at transcription level. In this study, chip data GSE 29721 was utilized, which contains 10 HCC samples and 10 normal adjacent tissue samples. Differentially expressed genes (DEGs) between two sample types were selected by t-test method. Following, the differentially co-expressed genes (DCGs) and differentially co-expressed Links (DCLs) were identified by DCGL package in R with the threshold of q < 0.25. Afterwards, pathway enrichment analysis of the DCGs was carried out by DAVID. Then, DCLs were mapped to TRANSFAC database to reveal associations between relevant transcriptional factors (TFs) and their target genes. Quantitative real-time RT-PCR was performed for TFs or genes of interest. As a result, a total of 388 DCGs and 35,771 DCLs were obtained. The predominant pathways enriched by these genes were Cytokine-cytokine receptor interaction, ECM-receptor interaction and TGF-β signaling pathway. Three TF-target interactions, LEF1-NCAM1, EGR1-FN1 and FOS-MT2A were predicted. Compared with control, expressions of the TF genes EGR1, FOS and ETS2 were all up-regulated in the HCC cell line, HepG2; while LEF1 was down-regulated. Except NCAM1, all the target genes were up-regulated in HepG2. Our findings suggest these TFs and genes might play important roles in the pathogenesis of HCC and may be used as therapeutic targets for HCC management.

摘要

肝细胞癌（HCC）是全球癌症相关死亡的第三大主要原因。尽管在 HCC 的诊断和治疗方面取得了重大进展，但确切的分子机制仍知之甚少。本研究旨在鉴定 HCC 进展的潜在生物标志物，主要在转录水平上。在这项研究中，使用了芯片数据 GSE29721，其中包含 10 个 HCC 样本和 10 个正常相邻组织样本。通过 t 检验方法选择两种样本类型之间的差异表达基因（DEG）。然后，通过 R 中的 DCGL 包，以 q<0.25 的阈值确定差异共表达基因（DCG）和差异共表达链接（DCL）。随后，通过 DAVID 对 DCG 进行途径富集分析。然后，将 DCL 映射到 TRANSFAC 数据库，以揭示相关转录因子（TF）及其靶基因之间的关联。对 TF 或感兴趣的基因进行定量实时 RT-PCR。结果，获得了 388 个 DCG 和 35771 个 DCL。这些基因富集的主要途径是细胞因子-细胞因子受体相互作用、ECM-受体相互作用和 TGF-β 信号通路。预测了三个 TF-靶基因相互作用，LEF1-NCAM1、EGR1-FN1 和 FOS-MT2A。与对照相比，在 HCC 细胞系 HepG2 中，TF 基因 EGR1、FOS 和 ETS2 的表达均上调，而 LEF1 下调。除 NCAM1 外，所有靶基因在 HepG2 中均上调。我们的研究结果表明，这些 TF 和基因可能在 HCC 的发病机制中发挥重要作用，可作为 HCC 管理的治疗靶点。