Pyroptosis-related prognosis model, immunocyte infiltration characterization, and competing endogenous RNA network of glioblastoma

Abstract

Background: Glioblastoma (GBM) has a high incidence rate, invasive growth, and easy recurrence, and the current therapeutic effect is less than satisfying. Pyroptosis plays an important role in morbidity and progress of GBM. Meanwhile, the tumor microenvironment (TME) is involved in the progress and treatment tolerance of GBM. In the present study, we analyzed prognosis model, immunocyte infiltration characterization, and competing endogenous RNA (ceRNA) network of GBM on the basis of pyroptosis-related genes (PRGs). Methods: The transcriptome and clinical data of 155 patients with GBM and 120 normal subjects were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). Lasso (Least absolute shrinkage and selection operator) Cox expression analysis was used in predicting prognostic markers, and its predictive ability was tested using a nomogram. A prognostic risk score formula was constructed, and CIBERSORT, ssGSEA algorithm, Tumor IMmune Estimation Resource (TIMER), and TISIDB database were used in evaluating the immunocyte infiltration characterization and tumor immune response of differential risk samples. A ceRNA network was constructed with Starbase, mirtarbase, and lncbase, and the mechanism of this regulatory axis was explored using Gene Set Enrichment Analysis (GSEA). Results: Five PRGs (CASP3, NLRP2, TP63, GZMB, and CASP9) were identified as the independent prognostic biomarkers of GBM. Prognostic risk score formula analysis showed that the low-risk group had obvious survival advantage compared with the high-risk group, and significant differences in immunocyte infiltration and immune related function score were found. In addition, a ceRNA network of messenger RNA (CASP3, TP63)–microRNA (hsa-miR-519c-5p)–long noncoding RNA (GABPB1-AS1) was established. GSEA analysis showed that the regulatory axis played a considerable role in the extracellular matrix (ECM) and immune inflammatory response. Conclusions: Pyroptosis and TME-related independent prognostic markers were screened in this study, and a prognosis risk score formula was established for the first time according to the prognosis PRGs. TME immunocyte infiltration characterization and immune response were assessed using ssGSEA, CIBERSORT algorithm, TIMER, and TISIDB database. Besides a ceRNA network was built up. This study not only laid foundations for further exploring pyroptosis and TME in improving prognosis of GBM, but also provided a new idea for more effective guidance on clinical immunotherapy to patients and developing new immunotherapeutic drugs.