A new prognostic model for glioblastoma multiforme based on coagulation-related genes.

BACKGROUND

Glioblastoma multiforme (GBM) is the most aggressive, common, and lethal type of primary brain tumor. Multiple cancers have been associated with abnormalities in the coagulation system that facilitate tumor invasion and metastasis. In GBM, the prognostic value and underlying mechanism of coagulation-related genes (CRGs) have not been explored.

METHODS

RNA sequencing (RNA-seq) and clinical information on GBM were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. Following the identification of differentially expressed CRGs (DECRGs) between GBM and control samples, the survival-related DECRGs were selected via univariate and multivariate Cox regression analyses to establish a prognostic signature. The prognostic performance and clinical utility of the prognostic signature were assessed by the Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve analysis, and a nomogram was constructed. The signature genes-related underlying mechanisms were analyzed according to gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-cell analysis. Finally, the difference in immune cell infiltration, stromal score, immune score, and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) score were compared between different risk groups.

RESULTS

A 5-gene prognostic signature () was established for overall survival (OS) prediction of GBM patients. The predicted efficiency of the prognostic signature was confirmed in TGGA-GBM dataset and validated in the CGGA-GBM dataset, revealing that it could differentiate GBM patients from controls well, and high risk score was accompanied with poor prognosis. Moreover, biological process (BP) and signaling pathway analyses showed that signature genes were mainly enriched in the functions of blood coagulation and tumor invasion and metastasis. Moreover, high-risk patients exhibited higher levels of immune cell infiltration, stromal score, immune score, and ESTIMATE score than that of low-risk patients.

CONCLUSIONS

An analysis of coagulation-related prognostic signatures was conducted in this study, as well as how signature genes may affect GBM progress, providing information that might provide new ideas for the development of GBM-related molecular targeted therapies.