AUTHOR=Ma Shuai , Wang Fang , Wang Nan , Jin Jiaqi , Ba Yixu , Ji Hang , Du Jianyang , Hu Shaoshan 

TITLE=Multiomics Data Analysis and Identification of Immune-Related Prognostic Signatures With Potential Implications in Prognosis and Immune Checkpoint Blockade Therapy of Glioblastoma

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.886913

DOI=10.3389/fneur.2022.886913

ISSN=1664-2295

ABSTRACT=Background: In recent years, glioblastoma multiforme (GBM) has been a concern of many researchers, as it is one of the main drivers of cancer-related deaths worldwide.
Methods: To uncover any further informative prognostic signatures, we explored the immune-related distinction in the genetic or epigenetic features of the three types (expression profile, somatic mutation, and DNA methylation). 28 immune-related hub genes were identified by Weighted Gene Co-Expression Network Analysis (WGCNA). Three genes (IL1R1, TNFSF12, and VDR) were identified to construct an immune-related prognostic model (IRPM) by lasso regression. Then, we used three hub genes to construct an IRPM for GBM and clarify the immunity, mutation, and methylation characteristics. Survival analysis of patients undergoing anti-PD-1 therapy showed that overall survival was superior in the low-risk group than in the high-risk group. 
Results: The combined results showed that the high-risk group had an association with EMT, high immune cell infiltration, immune activation, a low mutation number, and high methylation, and the low-risk group showed an adverse status. 
Conclusions: In conclusion, IRPM is a promising biomarker to distinguish prognosis and molecular and immune characteristics, and the corresponding riskscore can be used to predict patient sensitivity to checkpoint inhibitor blockade therapy. Thus, the assessment of IRPM will guide us in improving treatment strategies and developing objective diagnostic tools.