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

机译:加权基因共表达网络分析鉴定与肝细胞癌免疫细胞浸润相关的关键基因

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摘要

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.
机译:肝细胞癌(HCC)的可怕预后主要是由于早期诊断率低,进展快速和高复发率。 HCC迫切需要宝贵的预后生物标志物。在这项研究中,从GSE14520,GSE22058,国际癌症基因组联盟(ICGC)和癌症基因组(TCGA)下载了微阵列数据。在GSE14520,GSE22058和ICGC数据库中鉴定了差异表达的基因(DEG)。加权基因共表达网络分析(WGCNA)用于建立参数的基因共表达模块,检查关键模块的基因以鉴定ICGC队列中的单变量COX回归的枢纽基因。确定表达水平和时间依赖的接收器操作特性(ROC)和曲线)下的区域和区域以估计轮毂基因的预后能力。在基因表达分析互动分析(Gepia)和TCGA数据库中也验证了这些轮毂基因。采用定时器算法和Gscalite数据库,分析中心基因与免疫细胞浸润的关联及其途径富集。完全,鉴定了276℃,并且WGCNA描述了含有148个基因的独特且显着的接受相关的共表达模块,其中在ICGC队列(Birc5,Foxm1,CenPa,KIF4A,DTYMK,PRC1中,由单变量COX回归选择10个枢纽基因。 IGF2BP3,KIF2C,TRIP13和TPX2)。除IGF2BP3之外,大多数基因在Gepia数据库中验证。多元COX回归分析结果表明,上述枢纽基因是HCC的所有独立预测因子。还证实10基因使用定时器算法与免疫细胞浸润有关。此外,开发了四种基因签名,包括Birc5,CenPa,Foxm1,Dtymk。这些轮毂基因和该模型证明了强的预后能力,并且可能是HCC的治疗靶标。此外,这些基因与免疫细胞浸润的关联提高了我们对HCC的发生和发展的理解。

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