Transmembrane protein 71 (TMEM71) is implicated in multiple cellular physiological functions and has been demonstrated to be crucial in the advancement of different cancerous growths. However, its specific function in low-grade glioma (LGG) remains unclear. We examined the expression patterns and prognostic importance of TMEM71 in various types of cancer by using pan-cancer analysis. The analyses of the correlations between TMEM71 expression and clinicopathological characteristics, prognosis, biological functions, immune characteristics, and genomic variations in LGG were conducted based on its expression patterns. Finally, the expression level and biological function of TMEM71 in LGG were verified by executing in vitro studies. Abnormal elevation of TMEM71 expression level was associated with poor prognosis of many tumors including LGG. Both multivariate and univariate Cox regression analyses indicated that the expression of TMEM71 served as a standalone prognostic biomarker for LGG. The levels of TMEM71 expression were associated with immune-related characteristics, infiltration of immune cells, immune checkpoint genes (ICPGs) expression, and tumor mutation burden (TMB) in patients with LGG. In laboratory studies, elevated levels of TMEM71 were found to be involved in activating the JAK2/STAT3 pathway, promoting CCAAT/Enhancer Binding Protein D (CEBPD) expression, and ultimately affecting the growth and motility of LGG cells. TMEM71 is an independent prognostic indicator for LGG and is strongly associated with the growth of LGG cells, positioning it as a potential novel target for treatment.

Dysregulation of protein glycosylation plays a crucial role in the development of glioma. Long noncoding RNA (lncRNAs), functional RNA molecules without protein-coding ability, regulate gene expression and participate in malignant glioma progression. However, it remains unclear how lncRNAs are involved in glycosylation glioma malignancy. Identification of prognostic glycosylation-related lncRNAs in gliomas is necessary. We collected RNA-seq data and clinicopathological information of glioma patients from the cancer genome atlas and Chinese glioma genome atlas. We used the “limma” package to explore glycosylation-related gene and screened related lncRNAs from abnormally glycosylated genes. Using univariate Cox analyses Regression and least absolute shrinkage and selection operator analyses, we constructed a risk signature with 7 glycosylation-related lncRNAs. Based on the median risk score (RS), patients with gliomas were divided into low- and high-risk subgroups with different overall survival rates. Univariate and multivariate Cox analyses regression analyses were performed to assess the independent prognostic ability of the RS. Twenty glycosylation-related lncRNAs were identified by univariate Cox regression analyses. Two glioma subgroups were identified using consistent protein clustering, with the prognosis of the former being better than that of the latter. Least absolute shrinkage and selection operator analysis identified 7 survival RSs for glycosylation-related lncRNAs, which were identified as independent prognostic markers and predictors of glioma clinicopathological features. Glycosylation-related lncRNAs play an important role in the malignant development of gliomas and may help guide treatment options.

Background: Glioma is a lethal intracranial tumor, and inflammation plays an important role in the initiation and development of glioma. Hence, there is an urgent need to conduct a bioinformatics analysis of immune-related genes (IRGs) for glioma. The present study aims to explore the association of the risk score with clinical outcomes and predict the prognosis with glioma. Methods: In The Cancer Genome Atlas (TCGA) database, 462 low grade glioma (LGG) samples and 166 glioblastoma (GBM) samples were reviewed, and IRGs correlated with the prognosis were selected by performing a survival analysis and establishing a Cox regression model. The potential molecular mechanism of these IRGs were also explored with assistance of computational biology. The risk score based on seven survival-associated IRGs was determined with the help of the multivariable Cox analysis, the patients were divided into two subgroups according to their risk score. Results: It was found that these differentially expressed IRGs were involved with the cytokine-cytokine receptor through functional enrichment analysis. The risk score based on the seven IRGs (SSTR5、CXCL10、CCL13、SAA1、CCL21、CCL27 and HTR1A) performed well in predicting patient’s the overall survival (OS), and correlated with age, 1p/19q codeletion status, IDH status, and WHO grades, both in the training (TCGA) datasets and the validation ((Chinese Glioma Genome Atlas) CGGA) datasets. The risk score also could reflect infiltration through several types of immune cells. Conclusions: This present study screened some IRGs associated with the patient’s clinical characteristic and prognosis, connect to the immune repertoire, demonstrated the importance of the risk score as a promising biomarker for estimating the clinical prognosis of glioma.