ArticleFrontiers in genetics2021
Establishment of an Endocytosis-Related Prognostic Signature for Patients With Low-Grade Glioma.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Uncovering G Protein-Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis.Annals of clinical and translational neurology · 2026Article
- Molecular subtyping and prognostic model construction based on endosome-related genes in colorectal cancer.Journal of gastrointestinal oncology · 2026Article
- A systematic computational analysis of the endosomal recycling pathway in glioblastoma.Biochemistry and biophysics reports · 2024Article
- Establishment and Validation of a Four-stress Granule-related Gene Signature in Hepatocellular Carcinoma.Journal of clinical and translational hepatology · 2024Article
- Endocytosis in cancer and cancer therapy.Nature reviews. Cancer · 2023Review
- Signaling pathways in brain tumors and therapeutic interventions.Signal transduction and targeted therapy · 2023Review
- P-Rex1 Signaling Hub in Lower Grade Glioma Patients, Found byFrontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLow-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.
methodsThe Cancer Genome Atlas and the Genotype-Tissue Expression database were used to identify differentially expressed ERGs in LGG patients. Gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene set enrichment analysis 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 database. Univariate and multivariate Cox regression analyses were used to develop an ERG signature to predict the overall survival (OS) of LGG patients. Finally, the association between the ERG signature and gene mutation status was further analyzed.
resultsSixty-two ERGs showed distinct mRNA expression patterns between normal brain tissues and LGG 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 29-gene signature, dividing patients into high-risk and low-risk groups with significantly different OS times. The prognostic performance of the 29-gene signature was validated in another LGG cohort. Additionally, we found that the mutation scores calculated based on the
conclusionOur ERG-based prediction models could serve as an independent prognostic tool to accurately predict the outcomes of LGG.
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