首页|应用加权基因共表达网络分析识别肝细胞癌发生和进展过程中关键通路和基因作用研究

应用加权基因共表达网络分析识别肝细胞癌发生和进展过程中关键通路和基因作用研究

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目的 探讨肝细胞癌(HCC)发生进展过程中功能富集通路和关键基因表达。方法 从基因表达数据库(GEO)下载HBV感染不同阶段和正常对照肝脏转录组数据,构建基因网络并使用加权基因共表达网络分析(WGCNA)将基因分类为不同的模块,对相关模块中的基因进行富集分析。应用GEO数据集进一步验证重要基因水平。结果 共6145 个组间差异基因参与构建加权基因共表达网络,被分为9 个模块,进一步描绘了从肝脏早期病变到肿瘤的进化轨迹,从正常组织到癌组织过程中的细胞增殖、DNA损伤修复和细胞衰老相关通路线性激活;随疾病进展,脂质代谢和凝血等肝脏功能相关通路逐渐受到抑制;在肿瘤发生前,慢性炎症期免疫相关通路被激活,后期逐渐趋向于抑制状态;共鉴定出 3个重要衰老相关基因,即CCNA2、UBE2C和ANAPC1,并在外部数据集验证了这3 个基因水平变化;进一步分析显示上述3 个基因水平与肝癌患者不良预后密切相关(P<0。05)。结论 通过生物信息学分析,我们初步确定了肝癌发生和进展过程中潜在途径和重要参与基因,为诊断和治疗干预提供了潜在的靶标。
Identification of key pathways and genes involved in hepatocarcinogenesis by weighted gene co-expression network analysis
Objective This study was conducted to explore the functional enrichment pathways and key genes in hepatocarcinogenesis.Methods We downloaded liver transcriptome data from the Gene Expression Database(GEO)at different stages of hepatitis B infection to hepatocellular carcinoma occurrence.Genes were categorized into different modules by weighted gene co-expression network analysis(WGCNA),and genes in different modules were enriched and analyzed.Important gene levels were further validated by GEO dataset.Results A total of 6145 differential genes were involved in the construction of WGCNA,which categorized genes into nine modules.The evolutionary trajectory from early liver lesions to tumorigenesis was further analyzed,e.g.,a linear activation of pathways related to cell proliferation,DNA damage repair,and cellular senescence during the process from normal tissues to oncogenesis;a gradual suppression of pathways related to liver function,such as lipid metabolism and coagulation was found with disease progression;and activation of immune-related pathways was also revealed during the period of chronic inflammation prior to tumors,with a gradual convergence to an inhibitory state in the later stage;Three important senescence-related genes,e.g.,CCNA2,UBE2C and ANAPC1,were identified,and the levels of the 3 genes were validated in an external dataset.Our further analysis demonstrated that the levels of the 3 genes were strongly associated with poor prognosis of patients with hepatocellular carcinoma.Conclusion By through bioinformatics analysis,we identify potential pathways and important genes involved in hepatocarcinogenesis,which might provide potential targets for diagnosis and therapeutic intervention in the future.

HepatomaWeighted gene co-expression network analysisGene set enrichment analysisTumorigenesis

徐思洁、秦浩、张振华

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230601 合肥市 安徽医科大学第二附属医院感染肝病科

合肥市第三人民医院感染性疾病科

肝细胞癌 加权基因共表达网络分析 基因集富集分析 癌发生机制

安徽省自然科学基金资助项目安徽省高校科学研究计划项目安徽医科大学学科建设项目

2108085MH298KJ2021A03232021lcxk027

2024

实用肝脏病杂志
中华医学会安徽分会

实用肝脏病杂志

CSTPCD
影响因子:1.362
ISSN:1672-5069
年,卷(期):2024.27(4)