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Background <p>The intrinsic heterogeneity and invasiveness of diffuse gliomas complicate accurate prognosis. Existing approaches are largely constrained by subtype specificity or limited analytical dimensions. To address this gap, a multi- dimension–based prognostic framework encompassing the full glioma spectrum was developed, accompanied by an analysis of the associated immune microenvironment.</p> Methods <p>A total of 3,323 glioma samples from the SEER (<i>n</i> = 2181), CGGA (<i>n</i> = 807), and TCGA (<i>n</i> = 335) datasets were integrated. Differentially expressed genes were screened using the limma package, and a Lasso-Cox–based prognostic signature (Glioma-GDPM) was established. Clinical variables such as age, grade, and IDH mutation status were harmonized through propensity score matching to construct a multi-omics prognostic model (Glioma-GCDPM). GSEA, CIBERSORT-based immune infiltration analysis, and TIDE scoring were used to investigate the biological characteristics of different risk subgroups.</p> Results <p>Eleven key prognostic genes (such as <i>PRAMEF2</i> and <i>FADS1</i>) and four clinical factors (age, tumor grade, IDH mutation, and 1p/19q codeletion) were identified. Glioma-GCDPM demonstrated favorable predictive ability in both the internal test cohort (AUC 0.81–0.86) and external validation sets (AUC 0.59–0.83). High-risk tumors exhibited greater invasiveness, with significant enrichment in cell cycle and proliferation-associated pathways. Additionally, a suppressed immune microenvironment was observed, reflected by elevated M2 macrophage infiltration and increased T cell dysfunction scores.</p> Conclusion <p>The multi-omics model established in this study enables precise stratification of prognostic risk in diffuse glioma patients and reveals immunosuppressive features in high-risk individuals, providing a new basis for personalized treatment strategies.</p>

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Multilayered insights into the full spectrum of diffuse gliomas: a novel prognostic evaluation model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Xu Cao,
  • Mingfan Liu,
  • Dongmei Zou,
  • Jing Zhang,
  • Jing Huang,
  • Ke Li

摘要

Background

The intrinsic heterogeneity and invasiveness of diffuse gliomas complicate accurate prognosis. Existing approaches are largely constrained by subtype specificity or limited analytical dimensions. To address this gap, a multi- dimension–based prognostic framework encompassing the full glioma spectrum was developed, accompanied by an analysis of the associated immune microenvironment.

Methods

A total of 3,323 glioma samples from the SEER (n = 2181), CGGA (n = 807), and TCGA (n = 335) datasets were integrated. Differentially expressed genes were screened using the limma package, and a Lasso-Cox–based prognostic signature (Glioma-GDPM) was established. Clinical variables such as age, grade, and IDH mutation status were harmonized through propensity score matching to construct a multi-omics prognostic model (Glioma-GCDPM). GSEA, CIBERSORT-based immune infiltration analysis, and TIDE scoring were used to investigate the biological characteristics of different risk subgroups.

Results

Eleven key prognostic genes (such as PRAMEF2 and FADS1) and four clinical factors (age, tumor grade, IDH mutation, and 1p/19q codeletion) were identified. Glioma-GCDPM demonstrated favorable predictive ability in both the internal test cohort (AUC 0.81–0.86) and external validation sets (AUC 0.59–0.83). High-risk tumors exhibited greater invasiveness, with significant enrichment in cell cycle and proliferation-associated pathways. Additionally, a suppressed immune microenvironment was observed, reflected by elevated M2 macrophage infiltration and increased T cell dysfunction scores.

Conclusion

The multi-omics model established in this study enables precise stratification of prognostic risk in diffuse glioma patients and reveals immunosuppressive features in high-risk individuals, providing a new basis for personalized treatment strategies.