Expression of TXLNA in brain gliomas and its clinical significance: a bioinformatics analysis

BACKGROUND: To analyze the expression of TXLNA in brain gliomas and its clinical significance. METHODS: Gene Expression Profiling Interactive Analysis（GEPIA）and Chinese Glioma Genome Atlas（CGGA）databases 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 analysis（GSEA）was 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.