J Neurol Surg A Cent Eur Neurosurg 2024; 85(04): 378-388
DOI: 10.1055/a-2070-3715
Original Article

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

Yue Qin
1   Department of Radiation Oncology, Southern Medical University, Guangzhou, China
,
Xiaonan Zhang
1   Department of Radiation Oncology, Southern Medical University, Guangzhou, China
,
Yulei Chen
1   Department of Radiation Oncology, Southern Medical University, Guangzhou, China
,
Wan Zhang
1   Department of Radiation Oncology, Southern Medical University, Guangzhou, China
,
Shasha Du
2   Department of Radiation Oncology, Guangdong Provincial People's Hospital, Guangzhou, Guangdong, China
,
Chen Ren
1   Department of Radiation Oncology, Southern Medical University, Guangzhou, China
› Author Affiliations
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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.

Supplementary Material



Publication History

Received: 17 August 2022

Accepted: 03 April 2023

Accepted Manuscript online:
06 April 2023

Article published online:
31 July 2023

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