Establishment of an Endocytosis-Related Prognostic Signature for Patients With Low-Grade Glioma - Wang, Dawei; Liu, Shiguang; Wang, Guangxin (Frontiers in Genetics, 2021)
Background: Low-grade glioma (LGG) is a heterogeneous tumor that might develop into high-grade malignant glioma, which markedly reduces patient survival time. Endocytosis is a cellular process responsible for the internalization of cell surface proteins or external materials into the cytosol. Dysregulated endocytic pathways have been linked to all steps of oncogenesis, from initial transformation to late invasion and metastasis. However, endocytosis-related gene (ERG) signatures have not been used to study the correlations between endocytosis and prognosis in cancer. Therefore, it is essential to develop a prognostic model for LGG based on the expression profiles of ERGs. Methods: The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database were used to identify differentially expressed ERGs in LGG patients. GO, KEGG, and GSEA methodologies were adopted for functional analysis. A protein-protein interaction (PPI) network was constructed and hub genes were identified based on the Search Tool for the Retrieval of Interacting Proteins (STRING) database. Univariate and multivariate Cox regression analyses were used to develop an ERG signature to predict the overall survival of LGG patients. Finally, the association between the ERG signature and gene mutation status was further analyzed, especially regarding the mutation status of the IDH1 and CIC genes. Results: Thirty ERGs showed distinct mRNA expression patterns between normal brain tissues and low-grade glioma tissues. Functional analysis indicated that these ERGs were strikingly enriched in endosomal trafficking pathways. The PPI network indicated that EGFR was the most central protein. We then built a six-gene signature, dividing patients into high-risk and low-risk groups with significantly different overall survival times. The prognostic performance of the six-gene signature was validated in two Chinese Glioma Genome Atlas (CGGA) cohorts. Additionally, we found that IDH1 and CIC gene mutations were significantly correlated with the endocytosis-related prognostic signature. Finally, a clinical nomogram with a concordance index of 0.838 predicted the survival probability of LGG patients by integrating clinicopathologic features and ERG signatures. Conclusion: Our ERG-based prediction models could serve as an independent prognostic tool to accurately predict the outcomes of LGG.