A Metabolic Plasticity-Based Signature for Molecular Classification and Prognosis of Lower-Grade Glioma

Background: Glioma is one of the major health problems worldwide. Biomarkers for predicting the prognosis of Glioma are still needed. Methods: The transcriptome data and clinic information on Glioma were obtained from the CGGA, TCGA, GDC, and GEO databases. The immune infiltration status in the clusters was compared. The genes with differential expression were identified, and a prognostic model was developed. Several assays were used to detect RPH3A's role in Glioma cells, including CCK-8, colony formation, wound healing, and transwell migration assay. Results: Lower Grade Glioma (LGG) was divided into two clusters. The immune infiltration difference was observed between the two clusters. We screened for genes that differed between the two groups. WGCNA was used to construct a co-expressed network using the DEGs, and four co-expressed modules were identified, which are blue, green, grey, and yellow modules. High-risk patients have a lower overall survival rate than low-risk patients. In addition, the risk score is associated with histological subtypes. Finally, the role of RPH3A was detected. The overexpression of RPH3A in LGG cells can significantly inhibit cell proliferation and migration and regulate EMT-regulated proteins. Conclusion: Our study developed a metabolic-related model for the prognosis of Glioma cells. RPH3A is a potential therapeutic target for Glioma.