A Gluconeogenesis-Related Genes Model for Predicting Prognosis, Tumor Microenvironment Infiltration, and Drug Sensitivity in Hepatocellular Carcinoma.

IF 4.2 3区 医学 Q2 ONCOLOGY Journal of Hepatocellular Carcinoma Pub Date : 2024-10-05 eCollection Date: 2024-01-01 DOI:10.2147/JHC.S483664
Xilong Tang, Jianjin Xue, Jie Zhang, Jiajia Zhou
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Abstract

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy within the digestive system, known for its poor prognosis. Gluconeogenesis, a critical metabolic pathway, is responsible for the synthesis of glucose in the normal liver. This study aimed to examine the role of gluconeogenesis-related genes (GRGs) in HCC and evaluate their impact on the tumor microenvironment infiltration and drug sensitivity in HCC.

Methods: We retrieved gene expression and clinical pathological data of HCC from The Cancer Genome Atlas (TCGA) database. This dataset was utilized to develop a prognosis model. The data from The International Cancer Genome Consortium (ICGC) served as an independent validation cohort. A least absolute shrinkage and selection operator (LASSO) regression analysis was applied to a curated panel of GRGs to construct and validate the predictive model. Furthermore, unsupervised consensus clustering, based on the expression levels of GRGs, categorized HCC patients into distinct subgroups.

Results: A four-gene prognostic model, referred to as GRGs, has been successfully developed with high accuracy and stability for the prediction of HCC patient prognosis. This model enables the stratification of patients into high or low risk groups based on individual risk scores, revealing significant differences in immune infiltration patterns and anti-tumor drug responses. Unsupervised consensus clustering analysis delineated four distinct subgroups of patients, each characterized by a unique prognosis and tumor immune microenvironment (TIME).

Conclusion: This study is the first to develop a prognostic model incorporating 4-GRGs that effectively predicts the prognosis, tumor microenvironment infiltration, and drug sensitivity in HCC patients. The model based on 4 GRGs may contribute to predict the prognosis, immunotherapy and chemotherapy response of HCC patients.

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用于预测肝细胞癌预后、肿瘤微环境浸润和药物敏感性的糖元生成相关基因模型
背景:肝细胞癌(HCC)是消化系统中一种常见的恶性肿瘤,以预后不良而闻名。糖元生成是一条重要的代谢途径,负责在正常肝脏中合成葡萄糖。本研究旨在探讨糖元生成相关基因(GRGs)在 HCC 中的作用,并评估其对 HCC 中肿瘤微环境浸润和药物敏感性的影响:我们从癌症基因组图谱(TCGA)数据库中检索了HCC的基因表达和临床病理数据。我们利用该数据集建立了一个预后模型。国际癌症基因组联盟(ICGC)的数据作为独立验证队列。我们将最小绝对收缩和选择算子(LASSO)回归分析应用于经过策划的GRGs面板,以构建和验证预测模型。此外,基于GRGs表达水平的无监督共识聚类将HCC患者分为不同的亚组:结果:我们成功建立了一个四基因预后模型(简称 GRGs),该模型在预测 HCC 患者预后方面具有较高的准确性和稳定性。该模型可根据个体风险评分将患者分为高风险组和低风险组,揭示了免疫浸润模式和抗肿瘤药物反应的显著差异。无监督共识聚类分析划分出四个不同的患者亚组,每个亚组都有独特的预后和肿瘤免疫微环境(TIME):本研究首次建立了一个包含 4 个 GRGs 的预后模型,该模型能有效预测 HCC 患者的预后、肿瘤微环境浸润和药物敏感性。基于4个GRGs的模型可能有助于预测HCC患者的预后、免疫治疗和化疗反应。
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来源期刊
CiteScore
0.50
自引率
2.40%
发文量
108
审稿时长
16 weeks
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