Development of Prognostic Indicator Based on AU-Rich Elements-Related Genes in Glioblastoma

-BACKGROUND: AREs (AU-rich elements) are important cis-acting short sequences in the 3'UTR (3'-untranslated region) that affect messenger RNA stability and translation. However, there were no systematic researches about AREs-related genes to predict the survival of patients with GBM (glioblastoma). -METHODS: Differentially expressed genes were acquired from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Differentially expressed AREsrelated genes were filtered by overlapping differentially expressed genes and AREs-related genes. The prognostic genes were selected to construct a risk model. Patients with GBM were categorized into 2 risk groups depending on the medium value of risk score. Gene Set Enrichment Analysis was performed to explore the potential biological pathways. We explored the correlation between the risk model and immune cells. The chemotherapy sensitivity was predicted in different risk groups. -RESULTS: A risk model was constructed by 10 differentially expressed AREs-related genes (GNS, ANKH, PTPRN2, NELL1, PLAUR, SLC9A2, SCARA3, MAPK1, HOXB2, and EN2), and it could accurately predict the prognosis of patients with GBM. Higher risk scores for patients with GBM had alower survival probability. The predictive power of risk model was decent. The risk score and treatment type were regarded as independent prognostic indicators. The mainly Gene Set Enrichment Analysis enrichment pathways were primary immunodeficiency and chemokine signaling pathway. Six immune cells were significant different in the 2 risk groups. There were higher abundance of macrophages M2 and neutrophils and higher sensitivity of 11 chemotherapy drugs in the high-risk group.-CONCLUSIONS: The 10 biomarkers might be important prognostic markers and potential therapeutic targets for patients with GBM.

Automated summary

A risk model constructed by 10 differentially expressed AREs-related genes accurately predicts the prognosis of patients with GBM. Higher risk scores are associated with lower survival probability in GBM patients. The risk model has decent predictive power and the risk score and treatment type are independent prognostic indicators. The 10 biomarkers identified in this study may serve as important prognostic markers and potential therapeutic targets for GBM patients.