Predictive Prognostic Model for Hepatocellular Carcinoma Based on Seven Genes Participating in Arachidonic Acid Metabolism

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2024-11-14 DOI:10.1002/cam4.70284

Xinyu Gu, Jing Wang, Jun Guan, Guojun Li, Xiao Ma, Yanli Ren, Shanshan Wu, Chao Chen, Haihong Zhu

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引用次数: 0

Abstract

Background

The occult onset and rapid progression of hepatocellular carcinoma (HCC) lead to an unsatisfactory overall survival (OS) rate. Established prognostic predictive models based on tumor-node-metastasis staging and predictive factors do not report satisfactory predictive efficacy. Arachidonic acid plays pivotal roles in biological processes including inflammation, regeneration, immune modulation, and tumorigenesis. We, therefore, constructed a prognostic predictive model based on seven genes linked to arachidonic acid metabolism, using samples of HCC patients from databases to analyze the genomic profiles. We also assessed the predictive stability of the constructed model.

Methods

Sample data of 365 patients diagnosed with HCC were extracted from The Cancer Genome Atlas (TCGA, training set) and HCCDB18, GSE14520, and GSE76427 databases (validation sets). Patient samples were clustered using ConsensusClusterPlus analysis based on the expression levels of 12 genes involved in arachidonic acid metabolism that were significantly associated with HCC prognosis. Differentially expressed genes (DEGs) within different clusters were distinguished and compared using WebGestaltR. Immunohistochemistry (IHC) analysis was performed using a human HCC tissue microarray (TMA). Tumor immune microenvironment assessment was performed using ESTIMATE, ssGSEA, and TIDE.

Results

Samples of patients with HCC were classified into three clusters, with significant differences in OS. Cluster 2 showed the best prognosis, whereas cluster 1 presented the worst. The three clusters showed significant differences in immune infiltration. We then performed Cox and LASSO regression analyses, which revealed CYP2C9, G6PD, CDC20, SPP1, PON1, TRNP1, and ADH4 as prognosis-related hub genes, making it a simplified prognostic model. TMA analysis for the seven target genes showed similar results of regression analyses. The high-risk group showed a significantly worse prognosis and reduced immunotherapy efficacy. Our model showed stable prognostic predictive efficacy.

Conclusions

This seven-gene–based model showed stable outcomes in predicting HCC prognosis as well as responses to immunotherapy.

Abstract Image

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基于参与花生四烯酸代谢的七个基因的肝细胞癌预测预后模型

背景：肝细胞癌（HCC）起病隐匿、进展迅速，导致总生存率（OS）不尽人意。基于肿瘤-结节-转移分期和预测因素的既有预后预测模型并没有令人满意的预测效果。花生四烯酸在炎症、再生、免疫调节和肿瘤发生等生物过程中发挥着关键作用。因此，我们基于与花生四烯酸代谢相关的七个基因构建了一个预后预测模型，并利用数据库中的 HCC 患者样本对基因组图谱进行了分析。我们还评估了所建模型的预测稳定性：从癌症基因组图谱（TCGA，训练集）和HCCDB18、GSE14520和GSE76427数据库（验证集）中提取了365名确诊为HCC患者的样本数据。根据花生四烯酸代谢过程中与 HCC 预后显著相关的 12 个基因的表达水平，使用 ConsensusClusterPlus 分析法对患者样本进行聚类。使用 WebGestaltR 对不同聚类中的差异表达基因（DEGs）进行区分和比较。使用人类 HCC 组织芯片（TMA）进行了免疫组化（IHC）分析。使用ESTIMATE、ssGSEA和TIDE对肿瘤免疫微环境进行评估：结果：HCC 患者样本被分为三组，OS 存在显著差异。第 2 组预后最好，而第 1 组预后最差。三个群组在免疫浸润方面存在显著差异。我们随后进行了Cox和LASSO回归分析，结果显示CYP2C9、G6PD、CDC20、SPP1、PON1、TRNP1和ADH4是预后相关的枢纽基因，从而简化了预后模型。对七个靶基因的 TMA 分析显示了类似的回归分析结果。高风险组的预后明显较差，免疫治疗效果也有所下降。我们的模型显示出稳定的预后预测效果：结论：这一基于七个基因的模型在预测 HCC 预后和免疫治疗反应方面显示出稳定的结果。

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

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.