铜质增生相关基因标记:胶质瘤治疗的预后见解。

IF 3.7 Q1 CLINICAL NEUROLOGY
Neuro-oncology advances Pub Date : 2024-12-23 eCollection Date: 2025-01-01 DOI:10.1093/noajnl/vdae233
Toni Rose Jue, Joseph Descallar, Vu Viet Hoang Pham, Jessica Lilian Bell, Tyler Shai-Hee, Riccardo Cazzolli, Sumanth Nagabushan, Eng-Siew Koh, Orazio Vittorio
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引用次数: 0

摘要

背景:成人型弥漫性胶质瘤占中枢神经系统原发性肿瘤的近四分之一,包括星形细胞瘤、少突胶质细胞瘤和胶质母细胞瘤。组织病理学肿瘤分级和分子特征明显影响患者的生存。尽管治疗取得了进展,但复发性胶质瘤患者的临床预后非常差,因此需要改进风险分层来确定治疗干预措施。各种研究表明,铜是一种显著的微量元素,对生物过程至关重要,并已被证明在癌症,特别是胶质瘤中具有促肿瘤功能。方法:采用TCGA低级别胶质瘤和胶质母细胞瘤(GBM)队列,采用差异基因表达、Cox回归、最小绝对收缩和选择算子回归来鉴定19个铜稳态相关基因特征。GLASS联盟数据集被用作独立验证队列。富集分析揭示了该信号参与各种癌症相关途径和生物学过程。利用这个CHRG特征,开发了一个风险评分模型和nomogram来预测胶质瘤患者的生存。结果:我们的预后CHRG特征将患者分为高风险和低风险组,显示出强大的预测性能。高危人群的生存结果较差。结合CHRG特征和临床特征的nomogram模型准确预测了训练集和测试集的1年、3年和5年生存率。结论:鉴定出的19个基因CHRG标记有望作为一种预后工具,实现胶质瘤患者的准确风险分层和生存预测。将这一特征与临床特征相结合可以提高预后的准确性,强调其在优化治疗策略和胶质瘤管理患者护理方面的潜在临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cuproplasia-related gene signature: Prognostic insights for glioma therapy.

Background: Adult-type diffuse gliomas encompass nearly a quarter of all primary tumors found in the CNS, including astrocytoma, oligodendroglioma, and glioblastoma. Histopathological tumor grade and molecular profile distinctly impact patient survival. Despite treatment advancements, patients with recurrent glioma have a very poor clinical outcome, warranting improved risk stratification to determine therapeutic interventions. Various studies have shown that copper is a notable trace element that is crucial for biological processes and has been shown to display pro-tumorigenic functions in cancer, particularly gliomas.

Methods: Differential gene expression, Cox regression, and least absolute shrinkage and selection operator regression were used to identify 19 copper-homeostasis-related gene signatures using TCGA lower-grade glioma and glioblastoma (GBM) cohorts. The GLASS Consortium dataset was used as an independent validation cohort. Enrichment analysis revealed the involvement of the signature in various cancer-related pathways and biological processes. Using this CHRG signature, a risk score model and a nomogram were developed to predict survival in glioma patients.

Results: Our prognostic CHRG signature stratified patients into high- and low-risk groups, demonstrating robust predictive performance. High-risk groups showed poorer survival outcomes. The nomogram model integrating CHRG signature and clinical features accurately predicted 1-, 3-, and 5-year survival rates in both training and test sets.

Conclusions: The identified 19-gene CHRG signature holds promise as a prognostic tool, enabling accurate risk stratification and survival prediction in glioma patients. Integrating this signature with clinical characteristics enhances prognostic accuracy, underscoring its potential clinical utility in optimizing therapeutic strategies and patient care in glioma management.

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