ABSTRACT
OBJECTIVES: Currently, an increasing number of studies are focusing on the impact of m7G modification in cancer. This study aims to investigate the prognostic value of m7G-related genes in low-grade glioma (LGG). METHODS: LGG samples were obtained from the CGGA database, and normal samples were obtained from GTEx. Differentially expressed m7G-related genes were identified, and genes highly associated with macrophage M2 in LGG patients were identified by immuno-infiltration and WGCNA analysis. The intersection of differentially expressed m7G-related genes and macrophage M2-associated genes yielded candidate genes, and hub genes were identified using 5 algorithms in CytoHubba. Enrichment analysis verified the relevant pathways of hub genes, and their performance in tumor classification was evaluated. RESULTS: A total of 3329 differentially expressed m7G-related genes were identified. 1289 genes were highly associated with macrophage M2 in LGG patients. The intersection of m7G-related genes and results in WGCNA yielded 840 candidate genes, and six hub genes (STXBP1, CPLX1, PAB3A, APBA1, RIMS1, and GRIN2B) were identified. Hub genes were enriched in synaptic transmission-related pathways and showed good performance for tumor classification. There were significant differences in survival levels between clusters. CONCLUSIONS: The identified m7G-related genes may provide new insight into the treatment and prognosis of LGG.
ABSTRACT
BACKGROUND: Glioblastoma multiforme (GBM) is a common primary intracranial tumor with poor prognosis. Common indicators in the clinical diagnosis of glioma include MGMT promoter methylation, isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion, and TERT mutation. Among these, IDH mutation is extremely important for GBM diagnosis and treatment. METHODS: The Chinese Glioma Population Database (CGGA) and Gene Expression Omnibus (GEO) data (GSE131273) related to glioma in the Chinese population were used for differential analysis (DGA) and weighted gene coexpression network analysis (WGCNA). The expression levels of hub genes between the IDH1 wild-type and mutant GBM cell lines were detected by RT-qPCR. Kaplan-Meier (KM) plotter was used to analyze hub gene expression levels and prognostic values. RESULTS: Eight hub genes were identified by WGCNA and different expression genes (DEG) analysis, namely, one upregulated gene (CRYAB) and seven downregulated genes (EFEMP2, RBP1, TAGLN2, CBR1, MSN, ALDH7A1, and MT1M). Four of these genes (ALDH7A1, MSN, CBR1, and MTM1) showed significant differences between IDH-wild-type and IDH-mutant GBM, verified at the cellular level. Moreover, the high expression of CBR1 was significantly correlated with poor overall survival (OS) in patients with IDH-mutant GBM, and we finally identified CBR1 as a specific prognostic factor in IDH-mutant GBM. CONCLUSION: Results revealed different gene expressions between IDH-wild-type and IDH-mutant GBM. These genes may help monitor the occurrence and development of glioma. CBR1 can be used as a prognostic marker to identify IDH-mutant glioblastoma patients.
