Identification and validation of a 17-gene signature to improve the survival prediction of gliomas - Tong, Shiao; Xia, Minqi; Xu, Yang; Sun, Qian; Ye, Liguo; Cai, Jiayang; Ye, Zhang; Tian, Daofeng (Frontiers in Immunology, 2022)
Gliomas are one of the most frequent types of nervous system tumors, having a significant morbidity and mortality rate. As a result, it is critical to fully comprehend the molecular mechanism of glioma to predict prognosis and target gene therapy. The goal of this research is to discover the hub genes of glioma and investigate their prognostic and diagnostic usefulness. In this study, we collected mRNA expression profiles and clinical information from glioma patients in TCGA, GTEx, GSE68848, and GSE4920 databases. WGCNA and differential expression analysis identified 170 DEGs in the collected datasets. GO analysis and KEGG pathway analysis showed that DEGs were mainly enriched in gliogenesis and extracellular matrix. LOSSA construct prognostic signatures in the TCGA cohort and 17 genes were used to build risk models and were validated in the CGGA database. The ROC curve confirmed the accuracy of the prognostic signature. Univariate and multivariate COX regression analysis showed that all independent risk factors for glioma except gender. Next, we performed ssGSEA to demonstrate a high correlation between risk scores and immunity. Subsequently, 7 hub genes were identified by the PPI network and found to have great drug targeting potential. Finally, RPL39, as one of the hub genes, was closely related to the prognosis of glioma patients. Knockdown of RPL39 in vitro significantly inhibited the proliferation and migration of glioma cells, whereas overexpression of RPL39 had the opposite effect. In conclusion, our new prognostic-related model provides more potential therapeutic strategies for glioma patients.