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BACKGROUND: To analyze the expression of TXLNA in brain gliomas and its clinical significance. METHODS: Gene Expression Profiling Interactive Analysisï¼GEPIAï¼and Chinese Glioma Genome Atlasï¼CGGAï¼databases were retrieved as the methods. To assess the disparity between TXLNA expression in glioma and normal brain tissue. The Kaplan-Meier survival curve was employed to preliminarily evaluate the survival curves of the high and low expression groups, this was done for investigate the correlation between TXLNA expression level and the survival and prognosis of glioma. A Cox proportional regression risk model of multivariate nature was employed to evaluate the elements impacting the survival and prognosis of glioma. Gene pool enrichment analysisï¼GSEAï¼was used to investigate the related function of TXLNA in glioma. A Pearson correlation test and co-expression analysis were employed to identify the genes most associated with TXLNA expression. RESULT: The enrichment analysis results were observably enriched in signal pathways for instance the cell cycle and completion and coordination cascade pathways, and it is evident that high expression of TXLNA in gliomas is related to a poor survival and a bad patient prognosis, thus making it an independent prognostic factor for gliomas. Genes such as STK40 and R1MS1 are significantly correlated with TXLNA, playing a synergistic or antagonistic role. CONCLUSIONS: The prognosis of GBM patients is strongly linked to the high expression of TXLNA, which may be a viable therapeutic target for curbing cancer progression and creating new immunotherapies for GBM.
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BACKGROUND: Low-grade gliomas (LGG) are WHO grade II tumors presenting as the most common primary malignant brain tumors in adults. Currently, LGG treatment involves either or a combination of surgery, radiation therapy, and chemotherapy. Despite the knowledge of constitutive genetic risk factors contributing to gliomas, the role of single genes as diagnostic and prognostic biomarkers is limited. The aim of the current study is to discover the predictive and prognostic genetic markers for LGG. METHODS: Transcriptome data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We first performed the tumor microenvironment (TME) survival analysis using the Kaplan-Meier method. An analysis was undertaken to screen for differentially expressed genes. The function of these genes was studied by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Following which a protein-protein interaction network (PPI) was constructed and visualized. Univariate and multivariate COX analyses were performed to obtain the probable prognostic genes. The key genes were selected by an intersection of core and prognostic genes. A clinical correlation analysis of single-gene expression was undertaken. GSEA enrichment analysis was performed to identify the function of key genes. Finally, a single gene-related correlation analysis was performed to identify the core immune cells involved in the development of LGG. RESULTS: A total of 529 transcriptome data and 515 clinical samples were obtained from the TCGA. Immune cells and stromal cells were found to be significantly increased in the LGG microenvironment. The top five core genes intersected with the top 38 prognostically relevant genes and two key genes were identified. Our analysis revealed that a high expression of HLA-DRA was associated with a poor prognosis of LGG. Correlation analysis of immune cells showed that HLA-DRA expression level was related to immune infiltration, positively related to macrophage M1 phenotype, and negatively related to activation of NK cells. CONCLUSIONS: HLA-DRA may be an independent prognostic indicator and an important biomarker for diagnosing and predicting survival in LGG patients. It may also be associated with the immune infiltration phenotype in LGG.
RESUMO
Glioma is the most common and fatal primary brain tumor in humans. A significant role for long non-coding RNA (lncRNA) in glioma is the regulation of gene expression and chromatin recombination, and immunotherapy is a promising cancer treatment. Therefore, it is necessary to identify necroptosis-related lncRNAs in glioma. In this study, we collected and evaluated the RNA-sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA, https://www.ncbi.nlm.nih.gov/, Data Release 32.0, March 29, 2022) glioma patients, and necroptosis-related lncRNAs were screened. Cox regression and least absolute shrinkage and selection operator (LASSO) analysis were performed to construct a risk score formula to explore the different overall survival between high- and low-risk groups in TCGA. Gene Ontology (GO) and pathway enrichment analysis (Kyoto Encyclopedia of Genes and Genomes (KEGG)) were performed to identify the function of screened genes. The immune correlation analysis showed that various immune cells and pathways positively associated with a patient's risk score. Furthermore, the analysis of the tumor microenvironment indicated many immune cells and stromal cells in the tumor microenvironment of glioma patients. Six necroptosis-related lncRNAs were concerned to be involved in survival and adopted to construct the risk score formula. The results showed that patients with high-risk scores held poor survival in TCGA. Compared with current clinical data, the area under the curve (AUC) of different years suggested that the formula had better predictive power. We verified that necroptosis-related lncRNAs play a significant role in the occurrence and development of glioma, and the constructed risk model can reasonably predict the prognosis of glioma. The results of these studies added some valuable guidance to understanding glioma pathogenesis and treatment, and these necroptosis-related lncRNAs may be used as biomarkers and therapeutic targets for glioma prevention.