Comprehensive Analysis of Angiogenesis and Ferroptosis Genes for Predicting the Survival Outcome and Immunotherapy Response of Hepatocellular Carcinoma.

IF 4.2 3区 医学 Q2 ONCOLOGY Journal of Hepatocellular Carcinoma Pub Date : 2024-09-29 eCollection Date: 2024-01-01 DOI:10.2147/JHC.S483647
Peng Wang, Guilian Kong
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引用次数: 0

Abstract

Background: Angiogenesis and ferroptosis are both linked to hepatocellular carcinoma (HCC) development, recurrence, and medication resistance. As a result, a thorough examination of the link between genes associated with angiogenesis and ferroptosis and immunotherapy efficacy is required to improve the dismal prognosis of HCC patients.

Methods: The molecular subtypes were found using a non-negative matrix factorization technique (NMF) based on the genes associated with angiogenesis and ferroptosis. Based on the differentially expressed genes (DEGs) screed between different molecular subtypes, an angiogenesis and ferroptosis-related prognostic stratification model was built using LASSO-COX regression, random forest technique, and extreme gradient boosting (XGBoost), which was further validated in the ICGC and GSE14520 databases. The impact of this model on tumor microenvironment (TME) and immunotherapy sensitivity was also investigated. The expression levels of candidate genes were detected and validated by Real-Time PCR and immunohistochemistry between liver cancer tissues and adjacent non-tumor liver tissues.

Results: Both angiogenesis and ferroptosis-related genes can significantly divide HCC patients into two subgroups with different survival outcomes, mutation profiles, and immune microenvironments. We screened six core genes (SLC10A1, PAEP, DPYSL4, MSC, NQO1, and CD24) for the construction of prognostic models by three machine learning methods after intersecting DEGs between angiogenesis and ferroptosis-related subgroups. In both the TCGA, ICGC, and GSE14520 datasets, the model exhibits high prediction efficiency based on the analysis of KM survival curves and ROC curves. Immunomodulatory genes analysis suggested that the model could be used to predict which patients are most likely to benefit from immunotherapy. Furthermore, the transcriptional expression levels of SLC10A1 in the validation experiment matched the outcomes derived from public datasets.

Conclusions: We identified a new angiogenesis and ferroptosis-related signature that might offer the molecular characteristic information needed for an efficient prognostic assessment and perhaps tailored treatment for HCC patients.

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全面分析血管生成和铁蛋白沉积基因,预测肝细胞癌的生存结果和免疫治疗反应
背景:血管生成和高铁血症都与肝细胞癌(HCC)的发展、复发和耐药性有关。因此,需要深入研究血管生成和高铁血症相关基因与免疫疗法疗效之间的联系,以改善 HCC 患者的不良预后:方法:利用非负矩阵因式分解技术(NMF),根据血管生成和高铁血症相关基因发现分子亚型。基于不同分子亚型之间的差异表达基因(DEGs),利用LASSO-COX回归、随机森林技术和极端梯度提升(XGBoost)建立了血管生成和铁突变相关的预后分层模型,并在ICGC和GSE14520数据库中进行了进一步验证。研究还探讨了该模型对肿瘤微环境(TME)和免疫疗法敏感性的影响。通过实时定量PCR和免疫组化技术检测并验证了肝癌组织与邻近非肿瘤肝组织之间候选基因的表达水平:结果:血管生成和铁突变相关基因可将HCC患者分为两个亚组,这两个亚组的生存结果、突变情况和免疫微环境各不相同。我们筛选了六个核心基因(SLC10A1、PAEP、DPYSL4、MSC、NQO1和CD24),通过三种机器学习方法在血管生成和铁突变相关亚组之间交叉DEG后构建预后模型。在TCGA、ICGC和GSE14520数据集中,根据KM生存曲线和ROC曲线分析,该模型都表现出较高的预测效率。免疫调节基因分析表明,该模型可用于预测哪些患者最有可能从免疫疗法中获益。此外,验证实验中SLC10A1的转录表达水平与公共数据集得出的结果相符:我们发现了一种新的血管生成和铁突变相关特征,它可能为高效预后评估提供所需的分子特征信息,或许还能为 HCC 患者提供量身定制的治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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
期刊最新文献
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