AUTHOR=Yu Ziye , Yang Huan , Song Kun , Fu Pengfei , Shen Jingjing , Xu Ming , Xu Hongzhi 

TITLE=Construction of an immune-related gene signature for the prognosis and diagnosis of glioblastoma multiforme

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.938679

DOI=10.3389/fonc.2022.938679

ISSN=2234-943X

ABSTRACT=Background: Increasing evidence has suggested that inflammation is related to tumorigenesis and tumor progression. However, the roles of immune-related genes in the occurrence, development and prognosis of glioblastoma multiforme (GBM) remain to be studied.

Methods: The GBM-related RNA-seq, survival and clinical data were acquired from the The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) databases. Immune-related genes were obtained from the Molecular Signatures Database (MsigDB) database. Differently expressed immune-related genes (DE-IRGs) between GBM and normal samples were identified. Prognostic genes associated with GBM were selected by Kaplan-Meier survival analysis, the Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis and multivariate Cox analysis. An immune-related gene signature was developed and validated in the TCGA and CGGA databases, separately. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore biological functions of the signature. Correlation between immune cell infiltration and the signature was analyzed by single sample Gene Set Enrichment Analysis (ssGSEA), and the diagnostic value was investigated. The Gene Set Enrichment Analysis (GSEA) was performed to explore the potential function of the signature genes in GBM, and the protein-protein interaction analysis (PPI) network was constructed.

Results: Three DE-IRGs (PTX3, TNFSF9, and BMP2) were used to construct an immune-related gene signature. Received operating characteristic (ROC) curves and Cox analyses confirmed that the 3-gene-based prognostic signature was a good independent prognostic factor for GBM patients. We found that the signature was mainly involved in immune-related biological processes and pathways, and multiple immune cells were disordered between the high- and low-risk groups. GSEA suggested PTX3 and TNFSF9 were mainly correlated with IL-17 signaling pathway, NF-κB signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, and PPI network presented that they could interact directly or indirectly with inflammatory pathway proteins. Quantitative Real-Time PCR (qRT-PCR) indicated that the three genes were significantly different between target tissues.

Conclusion: The signature with three immune-related genes might be an independent prognostic factor for GBM patients and could was associated with the immune cell infiltration of GBM patients.