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通过加权基因共表达网络分析鉴定肝细胞癌中与免疫细胞浸润相关的关键基因

Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis.

作者信息

Wang Dengchuan, Liu Jun, Liu Shengshuo, Li Wenli

机构信息

Office of Medical Ethics, Shenzhen Longhua District Central Hospital, Shenzhen, China.

Departments of Clinical Laboratory, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.

出版信息

Front Genet. 2020 Apr 24;11:342. doi: 10.3389/fgene.2020.00342. eCollection 2020.

Abstract

The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.

摘要

肝细胞癌(HCC)预后不佳主要是由于早期诊断率低、进展迅速和复发率高。HCC迫切需要有价值的预后生物标志物。在本研究中,从GSE14520、GSE22058、国际癌症基因组联盟(ICGC)和癌症基因组图谱(TCGA)下载了微阵列数据。在GSE14520、GSE22058和ICGC数据库中鉴定出差异表达基因(DEG)。使用加权基因共表达网络分析(WGCNA)建立DEG的基因共表达模块,并在ICGC队列中使用单变量Cox回归检查关键模块的基因以鉴定枢纽基因。确定表达水平以及时间依赖性受试者工作特征(ROC)和曲线下面积(AUC)以评估枢纽基因的预后能力。这些枢纽基因也在基因表达谱交互式分析(GEPIA)和TCGA数据库中得到验证。应用TIMER算法和GSCALite数据库分析枢纽基因与免疫细胞浸润的关联及其通路富集情况。总共鉴定出276个DEG,WGCNA描述了一个独特的、与DEG显著相关的共表达模块,包含148个基因,在ICGC队列中通过单变量Cox回归选择了10个枢纽基因(BIRC5、FOXM1、CENPA、KIF4A、DTYMK、PRC1、IGF2BP3、KIF2C、TRIP13和TPX2)。除IGF2BP3外,大多数基因在GEPIA数据库中得到验证。多变量Cox回归分析结果表明,上述枢纽基因均为HCC的独立预测因子。使用TIMER算法还证实这10个基因与免疫细胞浸润有关。此外,开发了一个四基因特征,包括BIRC5、CENPA、FOXM1、DTYMK。这些枢纽基因和模型显示出很强的预后能力,很可能成为HCC的治疗靶点。此外,这些基因与免疫细胞浸润的关联增进了我们对HCC发生发展的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80ae/7193721/d6879b094d8b/fgene-11-00342-g001.jpg

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