Identification and validation of a risk signature based on extracellular matrix-related genes in gliomas

Gliomas have the highest incidence among primary brain tumors, and the extracellular matrix (ECM) plays a vital role in tumor progression. We constructed a risk signature using ECM-related genes to predict the prognosis of patients with gliomas. mRNA and clinical data from glioma patients were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Chinese Glioma Genome Atlas (CGGA) databases. Differentially expressed ECM-related genes were screened, and a risk signature was built using least absolute shrinkage and selection operator (LASSO) Cox regression. Cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to assess immune infiltration in different risk groups. Gene set enrichment analysis (GSEA) was performed to explore the molecular mechanisms of the genes employed in the risk score. Differentially expressed ECM-related genes were identified, and their associated regulatory mechanisms were predicted via analysis of protein–protein interaction (PPI), transcription factor (TF) regulatory and TF coexpression networks. The established risk signature considered 17 ECM-related genes. The prognosis of the high-risk group was significantly worse than that of the low-risk group. We used the CGGA database to validate the signature. CIBERSORT indicated that the levels of naive B cells, activated memory CD4 T cells, regulatory T cells, gamma delta T cells, activated NK cells, monocytes, activated dendritic cells and activated mast cells were higher in the high-risk group. The levels of plasma cells, CD8 T cells, naive CD4 T cells, resting memory CD4 T cells, M0 macrophages, M1 macrophages, resting mast cells, and neutrophils were lower in the high-risk group. Ultimately, GSEA showed that the terms intestinal immune network for IgA production, primary immunodeficiency, and ECM receptor interaction were the top 3 terms enriched in the high-risk group. The terms Wnt signaling pathway, ErbB signaling pathway, mTOR signaling pathway, and calcium signaling pathway were enriched in the low-risk group. We built a risk signature to predict glioma prognosis using ECM-related genes. By evaluating immune infiltration and biofunctions, we gained a further understanding of this risk signature. This risk signature could be an effective tool for predicting glioma prognosis. This study did not require ethical approval. We will disseminate our findings by publishing results in a peer-reviewed journal.