Identification of Cuproptosis-Related Genes for Molecular Subtyping: Predicting Prognostic and Therapeutic Response in Glioma.

IF 2.7 4区医学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY OncoTargets and therapy Pub Date : 2024-11-18 eCollection Date: 2024-01-01 DOI:10.2147/OTT.S481443

Xiaochun Xia, Xiaoying Huang, Longxiang Wu, Pengqin Xu, Peng Li

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Abstract

Background: Cuproptosis, a metal-ion-dependent form of regulated cell death induced by copper overload, is emerging as a potential mechanism in high-grade glioma (HGG). Despite its significance, the role of cuproptosis in predicting the prognostic and therapeutic response in HGG remains poorly understood.

Methods: We performed unsupervised clustering to stratify patients with HGG in the Chinese Glioma Genome Atlas (CGGA) according to the expression of 14 cuproptosis-related genes (CRGs) and validated in The Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to explore the biological processes and pathways involved within distinct groups. We constructed the CupScore model to predict the responsiveness to immune checkpoint inhibitors (ICIs) therapy and chemotherapy in patients with HGG. Additionally, in vivo and in vitro experiments were performed to investigate the potential biological function of CDKN2A in HGG.

Results: We identified two cuproptosis-related molecular subgroups with significantly different survival probabilities. Patients with HGG in cluster 1 were characterized as immune-desert phenotype with higher CupScore and lower expression of MHC complex, interferons, chemokines, interleukins, and immune checkpoints. In contrast, cluster 2 showed an immune-inflamed signature. We screened PI-103 as the most promising candidate for patients with higher CupScore and confirmed its experimental evidence and clinical trial status. Patients with lower CupScore showed higher response rates to anti-PD-L1 and anti-PD1 combined with anti-CTLA4 ICI therapy. Furthermore, in vivo and in vitro experiments revealed that CDKN2A enhanced the malignant phenotype of HGG.

Conclusion: Cuproptosis has the ability to reprogram the tumor microenvironment (TME) in HGG, leading to the stratification of patients into two distinct molecular subgroups. The CupScore model emerged as a robust metric for predicting the prognostic and therapeutic benefits, as well as may therefore facilitate personalized treatment strategies for patients with HGG.

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为分子亚型鉴定杯突相关基因：预测胶质瘤的预后和治疗反应

背景：铜中毒是铜超载诱导的一种金属离子依赖性细胞死亡调节形式，正在成为高级别胶质瘤（HGG）的一种潜在机制。尽管其意义重大，但人们对杯突症在预测 HGG 预后和治疗反应中的作用仍知之甚少：我们根据 14 个杯突相关基因（CRGs）的表达情况，对中国胶质瘤基因组图谱（CGGA）中的 HGG 患者进行了无监督聚类分层，并在癌症基因组图谱（TCGA）中进行了验证。我们应用基因本体（GO）和京都基因组百科全书（KEGG）分析来探索不同组别中涉及的生物学过程和通路。我们构建了CupScore模型来预测HGG患者对免疫检查点抑制剂（ICIs）治疗和化疗的反应性。此外，我们还进行了体内和体外实验，研究 CDKN2A 在 HGG 中的潜在生物学功能：结果：我们发现了两个与杯突症相关的分子亚群，它们的生存概率存在显著差异。第1群组的HGG患者表现为免疫凋亡表型，CupScore较高，MHC复合体、干扰素、趋化因子、白细胞介素和免疫检查点的表达较低。相比之下，第 2 组显示出免疫炎症特征。我们筛选出 PI-103 作为 CupScore 较高患者最有希望的候选药物，并确认了其实验证据和临床试验状态。CupScore较低的患者对抗PD-L1和抗PD1联合抗CTLA4 ICI治疗的反应率较高。此外，体内和体外实验显示，CDKN2A增强了HGG的恶性表型：结论：Cuproptosis能重塑HGG的肿瘤微环境（TME），从而将患者分为两个不同的分子亚组。CupScore模型是预测预后和治疗效果的可靠指标，因此有助于为HGG患者制定个性化治疗策略。

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

OncoTargets and therapy BIOTECHNOLOGY & APPLIED MICROBIOLOGY-ONCOLOGY

CiteScore

9.70

自引率

0.00%

发文量

221

审稿时长

1 months

期刊介绍： OncoTargets and Therapy is an international, peer-reviewed journal focusing on molecular aspects of cancer research, that is, the molecular diagnosis of and targeted molecular or precision therapy for all types of cancer. The journal is characterized by the rapid reporting of high-quality original research, basic science, reviews and evaluations, expert opinion and commentary that shed novel insight on a cancer or cancer subtype. Specific topics covered by the journal include: -Novel therapeutic targets and innovative agents -Novel therapeutic regimens for improved benefit and/or decreased side effects -Early stage clinical trials Further considerations when submitting to OncoTargets and Therapy: -Studies containing in vivo animal model data will be considered favorably. -Tissue microarray analyses will not be considered except in cases where they are supported by comprehensive biological studies involving multiple cell lines. -Biomarker association studies will be considered only when validated by comprehensive in vitro data and analysis of human tissue samples. -Studies utilizing publicly available data (e.g. GWAS/TCGA/GEO etc.) should add to the body of knowledge about a specific disease or relevant phenotype and must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Bioinformatics studies must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Single nucleotide polymorphism (SNP) studies will not be considered.