Prognostic Analysis of a Hypoxia-Associated lncRNA Signature in Glioblastoma and its Pan-Cancer Landscape.

IF 0.9 4区医学 Q4 CLINICAL NEUROLOGY

Journal of neurological surgery. Part A, Central European neurosurgery Pub Date : 2024-07-01 Epub Date: 2023-04-06 DOI:10.1055/a-2070-3715

Yue Qin, Xiaonan Zhang, Yulei Chen, Wan Zhang, Shasha Du, Chen Ren

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引用次数: 0

Abstract

Background: Hypoxia is an important clinical feature of glioblastoma (GBM), which regulates a variety of tumor processes and is inseparable from radiotherapy. Accumulating evidence suggests that long noncoding RNAs (lncRNAs) are strongly associated with survival outcomes in GBM patients and modulate hypoxia-induced tumor processes. Therefore, the aim of this study was to establish a hypoxia-associated lncRNAs (HALs) prognostic model to predict survival outcomes in GBM patients.

Methods: LncRNAs in GBM samples were extracted from The Cancer Genome Atlas database. Hypoxia-related genes were downloaded from the Molecular Signature Database. Co-expression analysis of differentially expressed lncRNAs and hypoxia-related genes in GBM samples was performed to determine HALs. Six optimal lncRNAs were selected for building HALs models by univariate Cox regression analysis.

Results: The prediction model has a good predictive effect on the prognosis of GBM patients. Meanwhile, LINC00957 among the six lncRNAs was selected and subjected to pan-cancer landscape analysis.

Conclusion: Taken together, our findings suggest that the HALs assessment model can be used to predict the prognosis of GBM patients. In addition, LINC00957 included in the model may be a useful target to study the mechanism of cancer development and design individualized treatment strategies.

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胶质母细胞瘤缺氧相关 lncRNA 标志及其泛癌症景观的预后分析

背景：缺氧是胶质母细胞瘤（GBM）的一个重要临床特征，它调控着多种肿瘤过程，与放疗密不可分。越来越多的证据表明，长非编码 RNA（lncRNA）与 GBM 患者的生存结果密切相关，并能调节缺氧诱导的肿瘤过程。因此，本研究旨在建立缺氧相关lncRNAs（HALs）预后模型，以预测GBM患者的生存结果：从癌症基因组图谱数据库中提取 GBM 样本中的 LncRNAs。缺氧相关基因从分子特征数据库中下载。对GBM样本中差异表达的lncRNA和缺氧相关基因进行共表达分析，以确定HALs。通过单变量Cox回归分析，筛选出6个最佳lncRNA用于建立HALs模型：结果：该预测模型对GBM患者的预后具有良好的预测效果。结果：该预测模型对 GBM 患者的预后有很好的预测作用，同时，6 个 lncRNA 中的 LINC00957 被选中并进行了泛癌症景观分析：综上所述，我们的研究结果表明，HALs评估模型可用于预测GBM患者的预后。此外，模型中的 LINC00957 可能是研究癌症发展机制和设计个体化治疗策略的有用靶点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of neurological surgery. Part A, Central European neurosurgery CLINICAL NEUROLOGY-SURGERY

CiteScore

2.30

自引率

0.00%

发文量

期刊介绍： The Journal of Neurological Surgery Part A: Central European Neurosurgery (JNLS A) is a major publication from the world''s leading publisher in neurosurgery. JNLS A currently serves as the official organ of several national neurosurgery societies. JNLS A is a peer-reviewed journal publishing original research, review articles, and technical notes covering all aspects of neurological surgery. The focus of JNLS A includes microsurgery as well as the latest minimally invasive techniques, such as stereotactic-guided surgery, endoscopy, and endovascular procedures. JNLS A covers purely neurosurgical topics.