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 develo** new immunotherapeutic drugs.
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Background
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system [1]. It is characterized by invasive growth and easy recurrence [2]. Recently, despite continuous developments in treatment methods, such as surgery, radiotherapy, and chemotherapy have been reported, the median survival time of patients remains 12–15 months, and no substantial improvement has been achieved yet [29]. Changes in lncRNA expression and its mutation can promote the occurrence and metastasis of tumors [30]. lncRNA plays a key role in TME intracellular signal transduction [31]. GABPB1-AS1 is an lncRNA in the cytoplasm. Recently, many studies have pointed out that lncRNA is a poor prognosis marker of glioma [32], cervical cancer [33], breast cancer [34], and prostate cancer [35]. According to this study, significant differences in GABPB1-AS1 expression was found between tumor and normal tissues. Similarly, Li et al. [36] found high expression of GABPB1-AS1 in the glioma tissues, and in vitro and in vivo experiments have demonstrated that GABPB1-AS1 knockdown reduced the proliferation and invasiveness of glioma cells. According to our results, Hsa-miR-519c-5p can be regulated by GABPB1-AS1 specifically, thus regulating the transcriptional levels of CASP3 and TP63. On this basis, Hsa-miR-519c-5p can control ECM-related signaling pathways (e.g., ECM receptor interaction, adherens junction, and TGFβ signaling pathways) and immune-related signaling pathways (e.g., Toll-like receptor and NOD-like receptor signaling pathways). This demonstrated that ECM and immune inflammation reactions played the key role in occurrence, invasion and metastasis of GBM. The TGF-β in the tumor tissues can inhibit immune cells, decrease local inflammation, and promote the reconstruction of ECM [37]. ECM components and high expression levels on the corresponding cell surface receptors are closely related with the progression of GBM [38]. Toll-like receptors (TLRs) can recognize non-self-molecules and activate the inflammation process. TLR expression had been observed in various samples, such as GBM cases and GBM cell lines, indicating that TLR plays an important role in tumor invasion and metastasis [39]. Therefore, we speculated that the mechanism by which GABPB1-AS1 regulates the transcriptional levels of CASP3 and TP63 and relevant signaling pathways through Hsa-miR-519c-5p might play a crucial role in the occurrence and development of GBM.
Conclusions
In conclusion, pyroptosis and TME-related independent prognostic markers were screened, and a prognosis risk score formula was established for the first time on the basis of prognosis PRGs. TME immunocyte infiltration characterization and immune response were assessed with ssGSEA, CIBERSORT algorithm, TIMER, and TISIDB database. A ceRNA network was then constructed. This study not only laid the foundation for the further exploration of pyroptosis and TME for the improvement of GBM prognosis but also provided novel insights for clinical immunotherapy and development of novel immunotherapeutic drugs. Deep experimental and clinical studies are still needed to elucidate the molecular mechanism of GBM and clinical applications of biomarkers and immunotherapy.
Availability of data and materials
The datasets generated and/or analysed during the current study are available in the TCGA (https://portal.gdc.cancer.gov/), GTEx (https://www.gtexportal.org/), GSEA-MSigDB (http://www.gsea-msigdb.org/gsea/login.jsp), Reactome (https://reactome.org/), Harmonizome (https://maayanlab.cloud/Harmonizome/), R software (version 4.1.1, https://www.r-project.org), STRING (version 11.5, https://string-db.org/), Cytoscape software (version 3.8.2, https://cytoscape.org), Gene Ontology (GO) http://geneontology.org/), KEGG (https://www.kegg.jp/kegg/kegg1.html), TIMER (http://timer.cistrome.org/), TISIDB (http://cis.hku.hk/TISIDB), Starbase (http://starbase.sysu.edu.cn/), MirTarbase (http://mirtarbase.cuhk.edu.cn/), Oncolnc (http://www.oncolnc.org/), Lncbase (https://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=lncbasev2/) and GEPIA2 (http://gepia2.cancer-pku.cn/).
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This study was supported by Medical Innovation Research Program of Shanghai Municipality (grant number 21Y11920900), and Scientific and Technological Innovation Projects of Longhua Hospital (grant number CX202052).
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HMA and BH designed the study, coordinated technical support and funding. BH revised the manuscript. MRD performed the study and drafted the manuscript. YJQ, XP, JFC, MXZ and TZ participated the study. All authors read and approved the final manuscript.
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Additional file 1: Table S1.
Correlation analysis of prognostic PRGs expression with immunocyte levels in TIMER database.
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Ding, MR., Qu, YJ., Peng, X. et al. Pyroptosis-related prognosis model, immunocyte infiltration characterization, and competing endogenous RNA network of glioblastoma. BMC Cancer 22, 611 (2022). https://doi.org/10.1186/s12885-022-09706-x
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DOI: https://doi.org/10.1186/s12885-022-09706-x