PDF(Pubmed)
Abstract：
UNASSIGNED: Hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC). Programmed cell death (PCD) is a critical process in suppressing tumor growth, and alterations in PCD-related genes may contribute to the progression of HBV-HCC. This study aims to develop a prognostic model that incorporates genomic and clinical information based on PCD-related genes, providing novel insights into the molecular heterogeneity of HBV-HCC through bioinformatics analysis and experimental validation.
UNASSIGNED: In this study, we analyzed 139 HBV-HCC samples from The Cancer Genome Atlas (TCGA) and validated them with 30 samples from the Gene Expression Omnibus (GEO) database. Various bioinformatics tools, including differential expression analysis, gene set variation analysis, and machine learning algorithms were used for comprehensive analysis of RNA sequencing data from HBV-HCC patients. Furthermore, among the PCD-related genes, we ultimately chose DLAT for further research on tissue chips and patient cohorts. Besides, immunohistochemistry, qRT-PCR and Western blot analysis were conducted.
UNASSIGNED: The cluster analysis identified three distinct subgroups of HBV-HCC patients. Among them, Cluster 2 demonstrated significant activation in DNA replication-related pathways and tumor-related processes. Analysis of copy number variations (CNVs) of PCD-related genes also revealed distinct patterns in the three subgroups, which may be associated with differences in pathway activation and survival outcomes. DLAT in tumor tissues of HBV-HCC patients is upregulated.
UNASSIGNED: Based on the PCD-related genes, we developed a prognostic model that incorporates genomic and clinical information and provided novel insights into the molecular heterogeneity of HBV-HCC. In our study, we emphasized the significance of PCD-related genes, particularly DLAT, which was examined in vitro to explore its potential clinical implications.
UNASSIGNED: In this study, we analyzed 139 HBV-HCC samples from The Cancer Genome Atlas (TCGA) and validated them with 30 samples from the Gene Expression Omnibus (GEO) database. Various bioinformatics tools, including differential expression analysis, gene set variation analysis, and machine learning algorithms were used for comprehensive analysis of RNA sequencing data from HBV-HCC patients. Furthermore, among the PCD-related genes, we ultimately chose DLAT for further research on tissue chips and patient cohorts. Besides, immunohistochemistry, qRT-PCR and Western blot analysis were conducted.
UNASSIGNED: The cluster analysis identified three distinct subgroups of HBV-HCC patients. Among them, Cluster 2 demonstrated significant activation in DNA replication-related pathways and tumor-related processes. Analysis of copy number variations (CNVs) of PCD-related genes also revealed distinct patterns in the three subgroups, which may be associated with differences in pathway activation and survival outcomes. DLAT in tumor tissues of HBV-HCC patients is upregulated.
UNASSIGNED: Based on the PCD-related genes, we developed a prognostic model that incorporates genomic and clinical information and provided novel insights into the molecular heterogeneity of HBV-HCC. In our study, we emphasized the significance of PCD-related genes, particularly DLAT, which was examined in vitro to explore its potential clinical implications.
摘要：
■乙型肝炎病毒（HBV）感染是肝细胞癌（HCC）的主要危险因素。程序性细胞死亡（PCD）是抑制肿瘤生长的关键过程，PCD相关基因的改变可能有助于HBV-HCC的进展。这项研究旨在开发一种基于PCD相关基因的整合基因组和临床信息的预后模型，通过生物信息学分析和实验验证，为HBV-HCC的分子异质性提供新的见解。
■在这项研究中，我们分析了来自癌症基因组图谱（TCGA）的139个HBV-HCC样本，并使用来自基因表达综合（GEO）数据库的30个样本进行了验证。各种生物信息学工具，包括差异表达分析，基因集变异分析，和机器学习算法用于从HBV-HCC患者的RNA测序数据的综合分析。此外，在PCD相关基因中，我们最终选择DLAT进行组织芯片和患者队列的进一步研究.此外,免疫组织化学，进行qRT-PCR和Western印迹分析。
■聚类分析确定了三个不同的HBV-HCC患者亚组。其中,簇2在DNA复制相关途径和肿瘤相关过程中显示出显著的激活。对PCD相关基因的拷贝数变异（CNVs）的分析也揭示了三个亚组中的不同模式，这可能与通路激活和生存结局的差异有关。HBV-HCC患者肿瘤组织中的DLAT上调。
■基于PCD相关基因，我们开发了一个结合了基因组和临床信息的预后模型，并为HBV-HCC的分子异质性提供了新的见解。在我们的研究中,我们强调了PCD相关基因的重要性，特别是DLAT，对其进行了体外检查，以探索其潜在的临床意义。
■在这项研究中，我们分析了来自癌症基因组图谱（TCGA）的139个HBV-HCC样本，并使用来自基因表达综合（GEO）数据库的30个样本进行了验证。各种生物信息学工具，包括差异表达分析，基因集变异分析，和机器学习算法用于从HBV-HCC患者的RNA测序数据的综合分析。此外，在PCD相关基因中，我们最终选择DLAT进行组织芯片和患者队列的进一步研究.此外,免疫组织化学，进行qRT-PCR和Western印迹分析。
■聚类分析确定了三个不同的HBV-HCC患者亚组。其中,簇2在DNA复制相关途径和肿瘤相关过程中显示出显著的激活。对PCD相关基因的拷贝数变异（CNVs）的分析也揭示了三个亚组中的不同模式，这可能与通路激活和生存结局的差异有关。HBV-HCC患者肿瘤组织中的DLAT上调。
■基于PCD相关基因，我们开发了一个结合了基因组和临床信息的预后模型，并为HBV-HCC的分子异质性提供了新的见解。在我们的研究中,我们强调了PCD相关基因的重要性，特别是DLAT，对其进行了体外检查，以探索其潜在的临床意义。
