Autophagy related DNA methylation signature predict clinical prognosis and immune microenvironment in low-grade glioma

Abstract

Background: Low-grade glioma (LGG) is a common malignant tumor of the central nervous system.The clinical prognosis of different patients varies greatly, so exploring appropriate markers that affect the prognosis and treatment of LGG is important. The purpose of this study was to identify the potential effect of autophagy-related DNA methylation on the prognosis and immune microenvironment in LGG. Methods:The methylation profile, transcription data and corresponding clinical information of 451 patients with LGG were obtained from The Cancer Genome Atlas (TCGA). Another methylation data and clinical information of 110 patients with LGG from Chinese Glioma Genome Atlas (CGGA) were used as the validation set. Through univariate and multivariate COX regression analysis, we identified the autophagy-related genes (ARGs) associated with methylation levels and prognosis, and established a risk assessment signature. The receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival curve were used to verify the model's effectiveness in predicting prognosis. Patients were divided into low-and high-risk groups based on risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to explore the differences in biological functions between the two groups. ESTIMATE and CIBERSORT algorithms were used to explore differences in immune infiltration and immunotherapy sites. Pearson correlation analysis was used to analyze the relative relationship between methylated cg sites and corresponding genes.Results: A total of 6 ARGs (ARSB, CFLAR, WIPI2, RB1, ERN1, RAB24) were selected that were associated with methylation levels and prognosis. The area under the curve (AUC) =0.96, and the KM survival curve P<0.0001, which proves that the risk assessment model has a good effect in predicting the prognosis ofLGG. GO and KEGG enrichment analysis showed that the model mainly involved major histocompatibility complex (MHC) II receptors, antigen processing and presentation, and immune cell differentiation. In addition, we also found differences in immune infiltration and immune checkpoints between high-and lowrisk groups. Conclusions:The methylation levels of these 6 ARGs have a strong predictive potential for LGG, and the methylation regulation of ARGs has an important impact on the immune microenvironment of LGGs.

Identification of an energy metabolism-related six-gene signature for distinguishing and forecasting the prognosis of low-grade gliomas

“…The significant increase of glycolysis metabolism increases the growth rate, invasion and metastasis ability of LGG cells, and promotes the progress of LGG disease (21). Research shows that the factors causing tumor occurrence, such as the deletion of tumor suppressor gene and the activation of proto oncogene, can cause metabolic changes of tumor cells, and lead to aerobic glycolysis disorder, which can lead to tumor progression (22). As low-grade gliomas are mainly characterized by their IDH mutations, IDH1 and IDH2 are NADP+-dependent enzymes that catalyze the oxidative decarboxylation of isocitrate in the citric acid cycle to α-Ketoglutarate (α-KG).…”

PLEKHA4 is a novel prognostic biomarker that reshapes the tumor microenvironment in lower-grade glioma

“…It has been confirmed that biomarkers including S100A, TEAD4, SRSF9, METTL7B, CSPP1, and CKS2 have significant relevance to the diagnosis and prognosis of LGG patients ( 9 – 14 ). In addition, iron metabolism-related genes, cuproptosis-related genes, necroptosis-related gene, autophagy-related signatures, as well as autophagy related DNA methylation signature, are also important prognostic factors for LGG ( 15 – 19 ). However, these clinical pathologic and genetic factors used for predicting LGG in clinical practice is poor, which limits their early diagnosis and treatment.…”