Construction of an Extracellular Matrix-Related Risk Model to Analyze the Correlation Between Glioblastoma and Tumor Immunity

Background: Abnormalities in the extracellular matrix (ECM) have been shown to play a crucial role in promoting the formation, progression, and metastasis of glioblastoma multiforme (GBM). Therefore, our study is aimed at constructing a prognostic model based on ECM-related factors, to predict the prognosis of patients with GBM. Methods: We employed single-sample gene set enrichment analysis (ssGSEA) to establish the ECM index of GBM. The identification of candidate genes was achieved by differential analysis conducted between ECM index groups, as well as through the utilization of weighted gene coexpression network analysis (WGCNA) and gene enrichment analysis. We conducted functional validation to confirm the significance of five biomarkers that were tested in the U251 cell line. The screening of prognostic genes was conducted using least absolute shrinkage and selection operator (LASSO) and univariate Cox analysis. The predictive relevance of the risk score model was assessed by using Kaplan-Meier (KM) survival curves in both The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts. In addition, we conducted immunological studies, created and verified a nomogram, and constructed a network involving long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA). Results: We identified 45 candidate genes by overlapping the 59 WGCNA core genes with the 855 differentially expressed genes (DEGs) between ECM index groups. These candidate genes were significantly enriched in 254 biological processes (BPs), 18 cellular components (CCs), 27 molecular functions (MFs), and 11 KEGG pathways. We identified a prognostic ECM-related five-gene signature using these candidate genes and constructed a risk model. Furthermore, we generated a nomogram model with excellent predictive ability. We also found significant differences between risk groups in six cell types and 29 immune checkpoints. Finally, we constructed a lncRNA-miRNA-mRNA network consisting of 27 lncRNAs, 73 miRNAs, and 5 model mRNAs. Conclusion: Our study developed a prognostic model based on the ECM-related five-gene signature, which can serve as a valuable reference for the treatment and prophetic prediction of GBM.