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基于TCGA数据库构建肝癌铜死亡的预后模型

Construction of Prognostic Model for Cuprotosis in Hepatocellular Carcinoma Based on TCGA Database

  • 摘要:
    目的 构建肝细胞癌(HCC)患者铜死亡相关基因(CRGs)的预后模型。
    方法 基于TCGA数据库HCC患者的mRNA数据集,分析CRGs在HCC患者中的表达,对CRGs及相关基因进行GO和KEGG富集分析。Kaplan-Meier生存分析曲线评估CRGs的生存预后价值,分析其与免疫细胞浸润的相关性。单因素Cox回归分析筛选出与HCC患者生存预后显著相关的CRGs,Lasso回归和多因素Cox回归分析构建预后模型。根据风险值对患者进行分组并进行生存分析,ROC曲线评估预后模型,单因素和多因素Cox回归分析风险评分及临床因素与预后的关系。
    结果 分析得到HCC中差异表达CRGs共11个,CRGs及其相关基因主要富集的GO条目为氧化还原酶活性,作用于供体的醛基或氧基,主要富集的KEGG信号通路为碳代谢。CRGs的表达水平与浆细胞样滤泡树突细胞、T辅助细胞等免疫细胞的浸润显著相关(P < 0.05)。筛选并构建3个CRGs的预后模型,包括CDKN2A、DLAT和LIPT1。高风险组和低风险组的生存时间存在显著差异(P < 0.001)。风险评分是预后不良的独立危险因素(P < 0.001)。
    结论 基于TCGA数据库数据构建CRGs的HCC患者预后模型,可为评估HCC患者的预后提供思路。

     

    Abstract:
    Objective To construct a prognostic model for cuprotosis-related genes (CRGs) in patients with hepatocellular carcinoma (HCC).
    Methods Differential expression of CRGs in HCC was analyzed on the basis of datasets from the TCGA database. The potential mechanisms of CRGs and their related genes in HCC were explored through GO and KEGG enrichment analyses. The prognostic value of the CRGs was evaluated through Kaplan-Meier survival analysis, and the relationship between CRG expression and immune cell infiltration was investigated. CRGs significantly correlated with prognosis in patients with HCC were identified. A prognostic model was established through univariate, Lasso regression, and multivariate Cox regression analyses. The patients were divided into two groups by risk score. ROC curve was used in evaluating the prognostic model. The relationship of risk score or clinical factors with prognosis was analyzed through univariate and multivariate Cox regression analyses.
    Results A total of 11 differentially expressed CRGs in HCC were obtained. The main enriched GO item of CRGs and their related genes was oxidoreductase activity, acting on the aldehyde or oxo group of donors, and the main enriched KEGG pathway was carbon metabolism. The expression of CRGs was significantly correlated with pDC, T helper cells and other immune cells (P < 0.05). Three CRGs (CDKN2A, DLAT, and LIPT1) were screened and a prognostic model was constructed. There was significant difference in overall survival between the high- and low-risk groups (P < 0.001). The risk score is an independent risk factor for poor prognosis (P < 0.001).
    Conclusion The prognostic model for CRGs in patients with HCC is constructed using TCGA database data. This model may be used in evaluating patient prognosis.

     

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