ERCC3相关基因可能有助于肝细胞癌的预后和免疫治疗分析

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Chen Yang, Yao Chen, Tao Tao, Ping Xu, Miaomiao Li, Bicheng Deng, Sihan Lu, Minfeng Yang, Weijie Wang, Jinghan Wang, Songbai Liu
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引用次数: 0

摘要

背景:肝细胞癌(HCC)在全球的发病率和死亡率都很高。切除修复交叉补体 3(ERCC3)是核苷酸切除修复(NER)途径中的一个关键功能基因,在癌症中通常发生突变或过度表达,被认为是导致 HCC 发生的一个关键基因。ERCC3及其相关关键基因在HCC中介导的免疫细胞浸润全球肿瘤微环境(TME)的特征仍不清楚。本研究旨在整合ERCC3相关关键基因在评估HCC患者TME细胞浸润特征、免疫治疗疗效和预后中的作用。这项研究为研究 HCC 的免疫机制和预后预测提供了理论依据:来自TCGA数据库的HCC队列包括50个正常样本和374个肿瘤样本,比较肝脏肿瘤组织和正常肝脏组织之间ERCC3相关基因表达和预后的差异,并通过单样本基因组富集分析(ssGSEA)量化24个细胞的相对丰度,分析不同基因对TME细胞的浸润程度。利用最小绝对收缩和选择算子(LASSO)Cox回归模型构建了与ERCC3基因相关的风险评分:结果:与正常肝组织相比,11个ERCC3相关基因在HCC肿瘤组织中的表达明显上调,这些基因的高表达与HCC患者的不良预后明显相关。这些关键基因(11 个 ERCC3 相关基因)与核酸代谢中的核酸还原信号通路和病毒致癌通路密切相关,提示这些关键基因可能在肿瘤细胞增殖、迁移和侵袭以及病毒相关性 HCC 的发病机制中发挥作用。此外,TME 免疫细胞在正常组织和肿瘤组织中的浸润特征也不同。肿瘤组织的免疫和间质活性明显低于健康肝组织。该研究发现,关键基因与CTLA4呈显著正相关,并在中心记忆CD4 T细胞、效应记忆CD4 T细胞、活化CD4 T细胞和2型T辅助细胞中富集。回归分析构建的预后模型能更好地将患者区分为高危和低危两组,生存分析表明,高危评分亚型患者的生存时间明显低于低危评分患者,且高危组含有较高水平的免疫抑制细胞,这可能是免疫逃逸的介质。此外,多变量分析表明,风险评分特征是评估 HCC 患者预后的可靠、无偏见的生物标志物,其在预测免疫治疗结果方面的价值也得到了证实:这项研究揭示了一种新的遗传特征,它与HCC患者的TME细胞浸润和预后密切相关。该研究表明,与 ERCC3 相关的多个关键基因的联合作用在形成 TME 细胞浸润的多样性和复杂性方面起着至关重要的作用。评估与ERCC3相关的多个关键基因的综合特征有助于预测患者的免疫治疗结果,并为HCC的免疫个体化治疗研究提供新的潜在靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ERCC3-Related Genes May Aid in the Prognostic and Immunotherapeutic Analysis of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) has high morbidity and mortality worldwide. Excision repair cross-complement 3 (ERCC3), a key functional gene in the nucleotide excision repair (NER) pathway, is commonly mutated or overexpressed in cancers and is thought to be a key gene contributing to the development of HCC. The characteristics of immune cell infiltration in the global tumor microenvironment (TME) mediated by ERCC3 and its related key genes in HCC are still unclear. The aim of this study was to integrate the role of ERCC3-related key genes in assessing the TME cell infiltration characteristics, immunotherapy efficacy, and prognosis of HCC patients. This study provides a theoretical basis for the study of immunological mechanisms and prognosis prediction in HCC.

Methods: The HCC cohort from the TCGA database included 50 normal samples and 374 tumor samples to compare the differences in ERCC3-related gene expression and prognosis between liver tumor tissues and normal liver tissues and to analyze the extent to which different genes infiltrated TME cells by quantifying the relative abundance of 24 cells through single-sample genome enrichment analysis (ssGSEA). A risk score associated with the ERCC3 gene was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression model.

Results: The expression of 11 ERCC3-related genes was significantly upregulated in HCC tumor tissues compared to normal liver tissues, and high expression of these genes was significantly associated with poor prognosis in HCC patients. The key genes (11 ERCC3-related genes) were closely associated with the nucleic acid reduction signaling pathway in nucleic acid metabolism and the viral oncogenic pathway, suggesting that these key genes may play a role in tumor cell proliferation, migration, and invasion, as well as in the pathogenesis of virus-associated HCC. In addition, the infiltration characteristics of TME immune cells in normal and tumor tissues were different. Immune and mesenchymal activity was significantly lower in tumor tissues than in healthy liver tissues. This study revealed that key genes were significantly positively correlated with CTLA4 and enriched in central memory CD4 T cells, effector memory CD4 T cells, activated CD4 T cells, and type 2 T helper cells. The prognostic model constructed by regression analysis could better distinguish patients into high-risk and low-risk groups, and the survival analysis showed that the survival time of patients with high-risk score subtypes was significantly lower than that of patients with low-risk scores and that the high-risk group contained higher levels of immune-suppressive cells, which may be a mediator of immune escape. Moreover, multivariate analyses showed that the risk score profile is a reliable and unbiased biomarker for assessing the prognosis of HCC patients, and its value in predicting the outcome of immunotherapy was also confirmed.

Conclusion: This study revealed a novel genetic signature that is significantly associated with TME cell infiltration and prognosis in HCC patients. It demonstrated that the combined action of multiple key genes associated with ERCC3 plays a crucial role in shaping the diversity and complexity of TME cell infiltrates. Evaluating the combined characteristics of multiple key genes associated with ERCC3 can help predict the outcome of immunotherapy in patients and provide new potential targets for immuno-individualized therapeutic studies on HCC.

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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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