A prognostic gene signature derived from aging‑related genes predicts survival, immune landscape and therapy response in glioma

Gliomas, the most common primary brain tumors, show diverse prognostic outcomes. Differences in gene expression between low-grade gliomas and glioblastoma and the role of aging-related genes highlight the need for robust prognostic models. The present study identified differentially expressed genes (DEGs) and developed a predictive risk model. Using The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, 29 overlapping aging-related DEGs were identified (|LogFC|>1, adjusted P<0.05). Cox and LASSO regression analyses selected 8 genes for a risk scoring model, validated across datasets and subgroups. Functional and single-cell analyses explored immune microenvironments and drug sensitivities. Additionally, reverse transcription-quantitative PCR (RT-qPCR) was performed to validate the differential expression of these genes in normal astrocytes (HA) and glioblastoma (GBM) cell lines (U251 and U87). The 8-gene model (Netrin-4, retinol-binding protein 1, Twist Family BHLH Transcription Factor 1, growth arrest and DNA damage inducible gamma (GADD45G), NUAK2, glutamate ionotropic receptor kainate type subunit 2, WEE1 and ribonucleotide reductase regulatory subunit) stratified patients into high- and low-risk groups, with high-risk patients showing significantly poorer survival (TCGA, HR=6.84; CGGA, HR=3.72; P<0.001). High-risk tumors were enriched in cell cycle and senescence pathways and exhibited elevated immune checkpoint expression and reduced chemotherapeutic sensitivity. Single-cell analysis revealed differential GADD45G expression in M1 and M2 macrophages, suggesting a role in immune evasion. RT-qPCR results further confirmed differential expression patterns of the 8 genes between normal and GBM cells, supporting their involvement in GBM pathogenesis. This 8-gene risk model effectively predicts glioma prognosis and supports personalized treatment strategies by highlighting immune microenvironment differences and drug sensitivities between risk groups.