Background: Gliomas are the most common malignant tumors of the central nervous system, with extremely bad prognoses. Cuproptosis is a novel form of regulated cell death. The impact of cuproptosis-related genes on glioma development has not been reported.Methods: The TCGA, GTEx, and CGGA databases were used to retrieve transcriptomic expression data. We employed Cox’s regressions to determine the associations between clinical factors and cuproptosis-related gene expression. Overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were evaluated using the Kaplan-Meier method. We also used the least absolute shrinkage and selection operator (LASSO) regression technique.Results: The expression levels of all 10 CRGs varied considerably between glioma tumors and healthy tissues. In glioma patients, the levels of CDKN2A, FDX1, DLD, DLAT, LIAS, LIPT1, and PDHA1 were significantly associated with the OS, disease-specific survival, and progression-free interval. We used LASSO Cox’s regression to create a prognostic model; the risk score was (0.882340) *FDX1 expression + (0.141089) *DLD expression + (–0.333875) *LIAS expression + (0.356469) *LIPT1 expression + (–0.123851) *PDHA1 expression. A high-risk score/signature was associated with poor OS (hazard ratio = 3.50, 95% confidence interval 2, –4.55, log-rank p < 0.001). Cox’s regression revealed that the FDX1 level independently predicted prognosis; FDX1 may control immune cell infiltration of the tumor microenvironment.Conclusion: The CRG signature may be prognostic in glioma patients, and the FDX1 level may independently predict glioma prognosis. These data may afford new insights into treatment.

BackgroundSeveral studies have suggested that anti-silencing function 1 B (ASF1B) can serve as a good potential marker for predicting tumor prognosis. But the values of ASF1B in gliomas have not been elucidated and further confirmation is needed.MethodsTranscriptomic and clinical data were downloaded from The Cancer Genome Atlas database (TCGA), genotypic tissue expression (GTEx), and the Chinese Gliomas Genome Atlas database (CGGA). Univariate and multivariate Cox regression analyses were used to investigate the link between clinical variables and ASF1B. Survival analysis was used to assess the association between ASF1B expression and overall survival (OS). The relationship between ASF1B expression and OS was studied using survival analysis. To investigate the probable function and immunological infiltration, researchers used gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and single-sample GSEA (ssGSEA).ResultsIn glioma tissues, ASF1B expression was considerably higher than in normal tissues. The survival analysis found that increased ASF1B expression was linked with a poor prognosis in glioma patients. ASF1B demonstrated a high diagnostic value in glioma patients, according to a Receiver Operating Characteristic (ROC) analysis. ASF1B was found to be an independent predictive factor for OS in a Cox regression study (HR = 1.573, 95% CI: 1.053–2.350, p = 0.027). GO, KEGG, and GSEA functional enrichment analysis revealed that ASF1B was associated with nuclear division, cell cycle, m-phase, and cell cycle checkpoints. Immuno-infiltration analysis revealed that ASF1B was positively related to Th2 cells, macrophages, and aDC and was negatively related to pDC, TFH, and NK CD56 bright cells.ConclusionA high level of ASF1B mRNA expression was correlated with a poor prognosis in glioma patients in this study, implying that it could be a reliable prognostic biomarker for glioma patients.

Background Gliomas are the most frequent type of central nervous system tumor, accounting for more than 70% of all malignant CNS tumors. Recent research suggests that the hyaluronan-mediated motility receptor (HMMR) could be a novel potential tumor prognostic marker. Furthermore, mounting data has highlighted the important role of ceRNA regulatory networks in a variety of human malignancies. The complexity and behavioural characteristics of HMMR and the ceRNA network in gliomas, on the other hand, remained unknown. Methods Transcriptomic expression data were collected from TCGA, GTEx, GEO, and CGGA database.The relationship between clinical variables and HMMR was analyzed with the univariate and multivariate Cox regression. Kaplan–Meier method was used to assess OS. TCGA data are analyzed and processed, and the correlation results obtained were used to perform GO, GSEA, and ssGSEA. Potentially interacting miRNAs and lncRNAs were predicted by miRWalk and StarBase. Results HMMR was substantially expressed in gliomas tissues compared to normal tissues. Multivariate analysis revealed that high HMMR expression was an independent predictive predictor of OS in TCGA and CGGA. Functional enrichment analysis found that HMMR expression was associated with nuclear division and cell cycle. Base on ssGSEA analysis, The levels of HMMR expression in various types of immune cells differed significantly. Bioinformatics investigation revealed the HEELPAR-hsa-let-7i-5p-RRM2 ceRNA network, which was linked to gliomas prognosis. And through multiple analysis, the good predictive performance of HELLPAR/RRM2 axis for gliomas patients was confirmed. Conclusion This study provides multi-layered and multifaceted evidence for the importance of HMMR and establishes a HMMR-related ceRNA (HEELPAR-hsa-let-7i-5p-RRM2) overexpressed network related to the prognosis of gliomas.