Comprehensive analysis to identify long non-coding RNA HCP5/MICA axis as a prognostic biomarker in glioblastoma

Abstract

Abstract Purpose To explore the lncRNA-mRNA network based on the IDH1 mutation status and construct a prognostic model for GBM. Methods The data of expression, somatic mutation, methylation, immune microenvironment were obtained from TCGA database, CGGA database and GTEx database. The common differential expressed genes (DEGs) between IDH1 mutant GBM and IDH1 wildtype GBM were used to perform a series of bioinformatics analysis to identify the hub gene. LASSO cox regression was used to screen a risk signature and nomogram was used to construct a prognostic model for GBM. Results LncRNA HCP5/MICA axis was downregulated in IDH1 mutant GBM and associated with the overall survival (OS) of GBM patients. Further analysis revealed that MICA expression was positively correlated with immune infiltration as well as the expression of several immune checkpoint gene in GBM. We screened a risk signature using MICA-associated genes by LASSO cox regression and the risk score performed well in predicting the OS. Furthermore, the risk score was used to construct a nomogram prognostic model combined with age, gender, IDH mutation, TP53 mutation, radiation and chemotherapy to predict the OS at 1-year, 2-year and 5-year. Conclusions This study constructed a prognostic model for GBM with MICA and clinical data for the first time and provided an insight into the molecular therapy of GBM. Further studies are required to uncover the mechanism of regulation between HCP5 and MICA in GBM.