Mathematical biosciences and engineering : MBE · 2022
A novel defined risk signature of interferon response genes predicts the prognosis and correlates with immune infiltration in glioblastoma.
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Xiao Yong, Wang Zhen, Zhao Mengjie, Ji Wei, Xiang Chong, Li Taiping, Wang Ran, Yang Kun, Qian Chunfa, Tang Xianglong, Xiao Hong, Zou Yuanjie, Liu Hongyi
Аннотация
BACKGROUND: Interferons (IFNs) have been implemented as anti-tumor immunity agents in clinical trials of glioma, but only a subset of glioblastoma (GBM) patients profits from it. The predictive role of IFNs stimulated genes in GBM needs further exploration to investigate the clinical role of IFNs.
METHODS: This study screened 526 GBM patients from three independent cohorts. The transcriptome data with matching clinical information were analyzed using R. Immunohistochemical staining data from the Human Protein Atlas and DNA methylation data from MethSurv were used for validation in protein and methylation level respectively.
RESULTS: We checked the survival effect of all 491 IFNs response genes, and found 54 genes characterized with significant hazard ratio in overall survival (OS). By protein-protein interaction analysis, 10 hub genes were selected out for subsequent study. And based on the expression of these 10 genes, GBM patients could be divided into two subgroups with significant difference in OS. Furthermore, the least absolute shrinkage and selection operator cox regression model was utilized to construct a multigene risk signature, including STAT3, STAT2 and SOCS3, which could serve as an independent prognostic predictor for GBM. The risk model was validated in two independent GBM cohorts. The GBM patients with high risk scores mainly concentrated in the GBM Mesenchymal subtype. The higher risk group was enriched in hypoxia, angiogenesis, EMT, glycolysis and immune pathways, and had increased Macrophage M2 infiltration and high expression of immune checkpoint CD274 (namely PD-L1).
CONCLUSIONS: Our findings revealed the three-gene risk model could be an independent prognostic predictor for GBM, and they were crucial participants in immunosuppressive microenvironment of GBM.
METHODS: This study screened 526 GBM patients from three independent cohorts. The transcriptome data with matching clinical information were analyzed using R. Immunohistochemical staining data from the Human Protein Atlas and DNA methylation data from MethSurv were used for validation in protein and methylation level respectively.
RESULTS: We checked the survival effect of all 491 IFNs response genes, and found 54 genes characterized with significant hazard ratio in overall survival (OS). By protein-protein interaction analysis, 10 hub genes were selected out for subsequent study. And based on the expression of these 10 genes, GBM patients could be divided into two subgroups with significant difference in OS. Furthermore, the least absolute shrinkage and selection operator cox regression model was utilized to construct a multigene risk signature, including STAT3, STAT2 and SOCS3, which could serve as an independent prognostic predictor for GBM. The risk model was validated in two independent GBM cohorts. The GBM patients with high risk scores mainly concentrated in the GBM Mesenchymal subtype. The higher risk group was enriched in hypoxia, angiogenesis, EMT, glycolysis and immune pathways, and had increased Macrophage M2 infiltration and high expression of immune checkpoint CD274 (namely PD-L1).
CONCLUSIONS: Our findings revealed the three-gene risk model could be an independent prognostic predictor for GBM, and they were crucial participants in immunosuppressive microenvironment of GBM.