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Glycosylation is currently considered to be an important hallmark of cancer. However, the characterization of glycosylation-related gene sets has not been comprehensively analyzed in glioma, and the relationship between glycosylation-related genes and glioma prognosis has not been elucidated. Here, we firstly found that the glycosylation-related differentially expressed genes in glioma patients were engaged in biological functions related to glioma progression revealed by enrichment analysis. Then seven glycosylation genes (BGN, C1GALT1C1L, GALNT13, SDC1, SERPINA1, SPTBN5 and TUBA1C) associated with glioma prognosis were screened out by consensus clustering, principal component analysis, Lasso regression, and univariate and multivariate Cox regression analysis using the TCGA-GTEx database. A glycosylation-related prognostic signature was developed and validated using CGGA database data with significantly accurate prediction on glioma prognosis, which showed better capacity to predict the prognosis of glioma patients than clinicopathological factors do. GSEA enrichment analysis based on the risk score further revealed that patients in the high-risk group were involved in immune-related pathways such as cytokine signaling, inflammatory responses, and immune regulation, as well as glycan synthesis and metabolic function. Immuno-correlation analysis revealed that a variety of immune cell infiltrations, such as Macrophage, activated dendritic cell, Regulatory T cell (Treg), and Natural killer cell, were increased in the high-risk group. Moreover, functional experiments were performed to evaluate the roles of risk genes in the cell viability and cell number of glioma U87 and U251 cells, which demonstrated that silencing BGN, SDC1, SERPINA1, TUBA1C, C1GALT1C1L and SPTBN5 could inhibit the growth and viability of glioma cells. These findings strengthened the prognostic potentials of our predictive signature in glioma. In conclusion, this prognostic model composed of 7 glycosylation-related genes distinguishes well the high-risk glioma patients, which might potentially serve as caner biomarkers for disease diagnosis and treatment.
Glycosylation is currently considered to be an important hallmark of cancer. However, the characterization of glycosylation-related gene sets has not been comprehensively analyzed in glioma, and the relationship between glycosylation-related genes and glioma prognosis has not been elucidated. Here, we firstly found that the glycosylation-related differentially expressed genes in glioma patients were engaged in biological functions related to glioma progression revealed by enrichment analysis. Then seven glycosylation genes (BGN, C1GALT1C1L, GALNT13, SDC1, SERPINA1, SPTBN5 and TUBA1C) associated with glioma prognosis were screened out by consensus clustering, principal component analysis, Lasso regression, and univariate and multivariate Cox regression analysis using the TCGA-GTEx database. A glycosylation-related prognostic signature was developed and validated using CGGA database data with significantly accurate prediction on glioma prognosis, which showed better capacity to predict the prognosis of glioma patients than clinicopathological factors do. GSEA enrichment analysis based on the risk score further revealed that patients in the high-risk group were involved in immune-related pathways such as cytokine signaling, inflammatory responses, and immune regulation, as well as glycan synthesis and metabolic function. Immuno-correlation analysis revealed that a variety of immune cell infiltrations, such as Macrophage, activated dendritic cell, Regulatory T cell (Treg), and Natural killer cell, were increased in the high-risk group. Moreover, functional experiments were performed to evaluate the roles of risk genes in the cell viability and cell number of glioma U87 and U251 cells, which demonstrated that silencing BGN, SDC1, SERPINA1, TUBA1C, C1GALT1C1L and SPTBN5 could inhibit the growth and viability of glioma cells. These findings strengthened the prognostic potentials of our predictive signature in glioma. In conclusion, this prognostic model composed of 7 glycosylation-related genes distinguishes well the high-risk glioma patients, which might potentially serve as caner biomarkers for disease diagnosis and treatment.
Objective: To detect the role of long non-coding RNA (lncRNA) CROCC Pseudogene 2 (CROCCP2)/miR-5584-5p /Baculoviral IAP Repeat Containing 5 (BIRC5) network in glioma growth. Methods: The Cancer Genome Atlas (TCGA) database was accessed to obtain the gene datasets associated with glioma growth. Bioinformatics techniques was employed to analyze the key network and construct the regulatory network of lncRNA CROCCP2/miR-5584-5p targeting BIRC5. Subsequently, the quantitative Polymerase Chain Reaction (qPCR) experiment was conducted to validate the expression levels of LncRNA CROCCP2, miR-5584-5p, and BIRC5 in both glioma tissues and normal brain tissues. Furthermore, we harnessed RNA interference technology to knock down BIRC5 in U251 cells, and flow cytometry was utilized to assess cell apoptosis. Results: LncRNA CROCCP2 is implicated in the binding of miR-5584-5p, and targeting BIRC5. PCR analysis revealed an elevated expression level of CROCCP2 and BIRC5 in glioma tissues, accompanied by a low expression of miR-5584-5p. Moreover, knockdown of BIRC5 results in an induction of apoptosis. Conclusions: LncRNA CROCCP2 could absorb miR-5584-5p targeting BIRC5 to activate cell apoptosis, so as to inhibit glioma development.
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