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.
Keywords
glioblastoma - hypoxia - The Cancer Genome Atlas - LINC00957 - pan-cancer
