ArticleBMC medical genomics2023
Analysis and validation of aging-related genes in prognosis and immune function of glioblastoma.
Article in BMC medical genomics, 2023. 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.
- Towards a personalized perspective on gliomas: an epigenetic-immuno-inflammatory aging framework.Frontiers in immunology · 2026Review
- Integrative analysis of single-cell and bulk transcriptome data reveals age-related immune cell alterations in primary glioblastoma associated with prognosis.Cancer immunology, immunotherapy : CII · 2025Article
- Immunosenescence and cancer: molecular hallmarks, tumor microenvironment remodeling, and age-specific immunotherapy challenges.Journal of hematology & oncology · 2025Review
- Profiling with senescence-associated secretory phenotype score identifies GDC-0879 as a small molecule sensitizing glioblastoma to anti-PD1.Cell death & disease · 2025Article
- Aging and head and neck cancer insights from single cell and spatial transcriptomic analyses.Discover oncology · 2024Article
- Identification and validation of a novel robust glioblastoma prognosis model based on bioinformatics.Heliyon · 2024Article
- A systematic computational analysis of the endosomal recycling pathway in glioblastoma.Biochemistry and biophysics reports · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundGlioblastoma (GBM) is a common malignant brain tumor with poor prognosis and high mortality. Numerous reports have identified the correlation between aging and the prognosis of patients with GBM. The purpose of this study was to establish a prognostic model for GBM patients based on aging-related gene (ARG) to help determine the prognosis of GBM patients.
methods143 patients with GBM from The Cancer Genomic Atlas (TCGA), 218 patients with GBM from the Chinese Glioma Genomic Atlas (CGGA) of China and 50 patients from Gene Expression Omnibus (GEO) were included in the study. R software (V4.2.1) and bioinformatics statistical methods were used to develop prognostic models and study immune infiltration and mutation characteristics.
resultsThirteen genes were screened out and used to establish the prognostic model finally, and the risk scores of the prognostic model was an independent factor (P < 0.001), which indicated a good prediction ability. In addition, there are significant differences in immune infiltration and mutation characteristics between the two groups with high and low risk scores.
conclusionThe prognostic model of GBM patients based on ARGs can predict the prognosis of GBM patients. However, this signature requires further investigation and validation in larger cohort studies.
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