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Article Open Access

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

  • Authors:
    • Zhenzhe Li
    • Liuyue Zhang
    • Xiaopeng Li
    • Peng Luo
    • Xingbo Liang
    • Tao Wen
    • Jieqin Yao
    • Qingwang Yu
    • Qianshuo Zhong
  • View Affiliations / Copyright

    Affiliations: Department of Neurosurgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong 524000, P.R. China
    Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 9
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    Published online on: November 24, 2025
       https://doi.org/10.3892/mco.2025.2918
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Abstract

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.
View Figures

Figure 1

Screening of DEGs and model
construction. (A) Volcano plot of significantly upregulated (red)
and downregulated (blue) genes in the TCGA dataset (|LogFC|>1,
adjusted P<0.05). (B) Volcano plot of DEGs in the CGGA dataset
(|LogFC|>1, adjusted P<0.05). (C) Venn diagram showing 29
shared senescence-related genes from TCGA and CGGA datasets. (D)
Heatmap of hierarchical clustering of 29 genes, separating LGG and
GBM samples. (E) Partial likelihood deviance plot from LASSO
regression identifying 12 genes at the optimal lambda. (F)
Coefficient profiles of 12 genes across lambda values in LASSO
analysis. (G) Forest plot from multivariate Cox regression of 8
genes significantly associated with prognosis, displaying HRs and
P-values. DEGs, differentially expressed genes; TCGA, The Cancer
Genome Atlas; CGGA, Chinese Glioma Genome Atlas; LGG, low-grade
glioma; GBM, glioblastoma; HR, hazard ratio; CI, confidence
interval.

Figure 2

Evaluation of the prognostic
predictive ability of the 8-gene risk score model. (A-C)
Kaplan-Meier survival curves showing significantly lower survival
in the high-risk group across the (A) training set, (B) test set
and (C) entire dataset (P<0.001). (D-F) Receiver operating
characteristic curves for 1-, 3- and 5-year survival predictions in
the (D) training set, (E) test set and (F) entire dataset,
demonstrating high predictive accuracy. (G-I) Risk score
distribution and survival status plots illustrating shorter
survival and higher mortality in high-risk patients in the (G)
training set, (H) test set and (I) entire dataset.

Figure 3

Validation of the 8-gene risk score
model in the CGGA dataset. (A) Kaplan-Meier survival curves showing
lower survival in the high-risk group (Hazard ratio=3.72;
P<0.001). (B) Receiver operating characteristic curves for 1-,
3-, and 5-year survival predictions with area under the curve
values of 0.750, 0.788 and 0.785, indicating favorable performance.
(C) Risk score distribution and survival status plot illustrating
shorter survival and higher mortality in the high-risk group. CGGA,
Chinese Glioma Genome Atlas.

Figure 4

Prognostic ability of the risk score
model in clinical and molecular subgroups. (A-C) Kaplan-Meier
curves for age groups under 40 years (A), 40-60 years (B), and over
60 years (C), showing worse survival in high-risk patients. (D and
E) Survival stratification by sex, with high-risk groups having
worse outcomes for both males and females. (F-H) Stratification by
tumor grade (G2-G4), where high-risk patients consistently had
shorter survival. (I-L) Stratification by histological type,
showing significant differences for astrocytoma (I),
oligoastrocytoma (K) and glioblastoma (L), but not for
oligodendroglioma (J). (M and N) (M and N) Kaplan-Meier curves for
IDH wild-type (M) and IDH-mutant (N) patients, revealing worse
survival in the high-risk group. (O and P) Stratification by 1p/19q
status, with significant differences in patients with (O) and
without (P) codeletion. (Q and R) Stratification by the MGMT
methylation status, with worse survival in the high-risk group for
both methylated (Q) and unmethylated (R) patients.

Figure 5

Construction and evaluation of the
nomogram. (A) Multivariate Cox regression analysis identifying
tumor grade, age, IDH status, and risk score as independent
prognostic factors. (B) Nomogram predicting 1-, 3- and 5-year
survival probabilities based on clinicopathological factors and
risk score. (C) Calibration plot showing strong alignment between
predicted and actual survival outcomes. (D) Time-dependent receiver
operating characteristic curves with area under the curve values of
0.882, 0.934 and 0.902 for 1-, 3- and 5-year survival predictions,
demonstrating high model accuracy.

Figure 6

Biological Function Differences
between patients with high-risk and low-risk glioma. (A) Gene
Ontology enrichment analysis showing involvement in the cell cycle,
nuclear division, and protein kinase activity. (B) Kyoto
Encyclopedia of Genes and Genomes pathway analysis highlighting
enrichment in the cell cycle, senescence, p53 and FoxO signaling
pathways. (C) Gene set variation analysis showing the upregulation
of KRAS signaling and the inflammatory response in the high-risk
group and oxidative phosphorylation and DNA repair in the low-risk
group.

Figure 7

Immune microenvironment in patients
with high-risk and low-risk glioma. (A) Proportions of immune
cells, with M2 macrophages and monocytes being the most abundant in
both groups. (B) High-risk group showed increased CD8+ T
cells, Tregs and macrophage infiltration, while low-risk group had
higher memory B cells, monocytes and neutrophils. (C-F) High-risk
group exhibited higher ImmuneScore and StromalScore, but lower
tumor purity. (G-J) Survival analysis showed worse outcomes with
greater M2 macrophage and neutrophil infiltration, and improved
outcomes with higher eosinophil infiltration
(*P<0.05, **P<0.01,
***P<0.001).

Figure 8

Weighted gene co-expression network
analysis and functional enrichment. (A) Soft threshold at 12
ensures scale-free network topology. (B) Gene dendrogram
identifying 20 co-expression modules. (C) Correlation heatmap
showing that the MEblue module is positively associated with M2
macrophages and Tregs and negatively associated with activated NK
cells. (D) The risk score was positively correlated with the MEblue
module (R=0.718, ***P<0.001). (E) Gene Ontology
analysis of the MEblue module highlights enrichment in
extracellular matrix organization and cell adhesion. (F) Kyoto
Encyclopedia of Genes and Genomes analysis revealed enrichment of
the PI3K-Akt, focal adhesion and MAPK signaling pathways.

Figure 9

Associations of the risk score with
immune checkpoints, immunotherapy and chemotherapy sensitivity. (A)
Increased expression of immune checkpoint molecules (PD-1, PD-L1
and CTLA4) in the high-risk group. (B) Positive correlation between
the risk score and immune checkpoint expression. (C-F) High-risk
patients have higher TIDE, immune exclusion and microsatellite
instability scores, indicating stronger immune evasion. (G-K)
High-risk patients have reduced sensitivity to chemotherapy, with
higher IC50 values for multiple drugs. (L-N) Higher risk
scores are linked to IDH wild-type, 1p/19q non-codeletion and MGMT
unmethylated status (***P<0.001).

Figure 10

Single-cell differential expression
analysis of GADD45G in M1 and M2 macrophages. (A) UMAP plot showing
17 cell clusters identified from glioblastoma single-cell data. (B)
Cell type annotation via ‘SingleR’, identifying subpopulations such
as monocytes, macrophages, endothelial cells, neutrophils and
natural killer cells. (C) Expression distribution of eight model
genes across subpopulations, with GADD45G highly expressed in
macrophages and endothelial cells. (D) UMAP plot showing
predominant GADD45G expression in macrophages. (E) Manual
annotation of M1 and M2 macrophages. (F) GADD45G expression is
significantly greater in M2 macrophages than in M1 macrophages.
GADD45G, growth arrest and DNA damage inducible gamma
(***P<0.001).

Figure 11

RT-qPCR validation of 8-gene
expression in normal astrocytes (HA) and glioblastoma cell lines
(U251 and U87). (A-H) RT-qPCR analysis of eight genes: (A) NTN4,
(B) RBP1, (C) TWIST1, (D) GADD45G, (E) NUAK2, (F) GRIK2, (G) WEE1
and (H) RRM2. *P<0.05. RT-qPCR, reverse
transcription-quantitative PCR; NTN4, netrin-4; RBP1,
retinol-binding protein 1; TWIST1, Twist Family BHLH Transcription
Factor 1; GADD45G, growth arrest and DNA damage inducible gamma;
RRM2, ribonucleotide reductase regulatory subunit; GRIK2, glutamate
ionotropic receptor kainate type subunit 2; ns, not
significant.
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Copy and paste a formatted citation
Spandidos Publications style
Li Z, Zhang L, Li X, Luo P, Liang X, Wen T, Yao J, Yu Q and Zhong Q: A prognostic gene signature derived from aging‑related genes predicts survival, immune landscape and therapy response in glioma. Mol Clin Oncol 24: 9, 2026.
APA
Li, Z., Zhang, L., Li, X., Luo, P., Liang, X., Wen, T. ... Zhong, Q. (2026). A prognostic gene signature derived from aging‑related genes predicts survival, immune landscape and therapy response in glioma. Molecular and Clinical Oncology, 24, 9. https://doi.org/10.3892/mco.2025.2918
MLA
Li, Z., Zhang, L., Li, X., Luo, P., Liang, X., Wen, T., Yao, J., Yu, Q., Zhong, Q."A prognostic gene signature derived from aging‑related genes predicts survival, immune landscape and therapy response in glioma". Molecular and Clinical Oncology 24.1 (2026): 9.
Chicago
Li, Z., Zhang, L., Li, X., Luo, P., Liang, X., Wen, T., Yao, J., Yu, Q., Zhong, Q."A prognostic gene signature derived from aging‑related genes predicts survival, immune landscape and therapy response in glioma". Molecular and Clinical Oncology 24, no. 1 (2026): 9. https://doi.org/10.3892/mco.2025.2918
Copy and paste a formatted citation
x
Spandidos Publications style
Li Z, Zhang L, Li X, Luo P, Liang X, Wen T, Yao J, Yu Q and Zhong Q: A prognostic gene signature derived from aging‑related genes predicts survival, immune landscape and therapy response in glioma. Mol Clin Oncol 24: 9, 2026.
APA
Li, Z., Zhang, L., Li, X., Luo, P., Liang, X., Wen, T. ... Zhong, Q. (2026). A prognostic gene signature derived from aging‑related genes predicts survival, immune landscape and therapy response in glioma. Molecular and Clinical Oncology, 24, 9. https://doi.org/10.3892/mco.2025.2918
MLA
Li, Z., Zhang, L., Li, X., Luo, P., Liang, X., Wen, T., Yao, J., Yu, Q., Zhong, Q."A prognostic gene signature derived from aging‑related genes predicts survival, immune landscape and therapy response in glioma". Molecular and Clinical Oncology 24.1 (2026): 9.
Chicago
Li, Z., Zhang, L., Li, X., Luo, P., Liang, X., Wen, T., Yao, J., Yu, Q., Zhong, Q."A prognostic gene signature derived from aging‑related genes predicts survival, immune landscape and therapy response in glioma". Molecular and Clinical Oncology 24, no. 1 (2026): 9. https://doi.org/10.3892/mco.2025.2918
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