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Updated: May 28, 2026

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
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A Ten-Gene Transcriptomic Biomarker Panel for Glioma Classification and Prognosis Identified via Integrative

Ömer Akgüller1,2, Mehmet Ali Balcı1, Gabriela Cioca3

  • 1Department of Mathematics, Faculty of Science, Mugla Sitki Kocman University, Muğla 48000, Turkey.

Cancers
|May 27, 2026
PubMed
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This summary is machine-generated.

A novel ten-gene biomarker panel accurately distinguishes glioblastoma (GBM) from lower-grade glioma (LGG) using a topology-aware framework. This discovery supports improved diagnostic workflows for brain tumors.

Area of Science:

  • Neuro-oncology
  • Biomarker Discovery
  • Computational Biology

Background:

  • Current biomarkers for distinguishing glioblastoma (GBM) from lower-grade glioma (LGG) lack clinical actionability and platform consistency.
  • Existing transcriptomic signatures are often confounded by batch effects and platform heterogeneity, hindering translation to clinical practice.

Purpose of the Study:

  • To develop and validate a robust, platform-agnostic biomarker discovery framework for differentiating GBM from LGG.
  • To identify a minimal gene panel capable of accurate single-patient classification across different expression platforms.

Main Methods:

  • Developed a topology-aware biomarker discovery framework integrating ANOVA ranking, hypergraph co-expression analysis, and rough set reduct optimization.
  • Trained a Random Forest classifier on a single-platform Affymetrix microarray cohort (GSE16011).
Keywords:
glioblastomahypergraph co-expression networklower-grade gliomarough set theorytranscriptomic biomarker

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  • Validated the biomarker panel on independent RNA-sequencing cohorts (CGGA-325, CGGA-693) using rigorous statistical methods including bootstrap correction and DeLong testing.

    • Main Results:

    • Identified a ten-gene biomarker panel (CSMD3, CHI3L1, PLP2, FRY, FCHSD2, ADM, MCUB, ANXA1, DUSP26, HK2) with high cross-validation and external validation accuracy (AUROC ~0.83-0.90).
    • The biomarker risk score demonstrated independent prognostic value in predicting patient survival.
    • Performance improved when aligned with WHO CNS5 (2021) molecular criteria, particularly for IDH-wild-type glioblastoma.

    Conclusions:

    • The novel methodology, including topology-aware hypergraph construction and rough set selection, is platform-agnostic and applicable to future molecularly defined glioma studies.
    • The identified ten-gene panel offers a promising tool for accurate GBM/LGG classification and prognostic assessment.
    • The framework supports the development of future glioma signatures within integrated histopathological and molecular diagnostic paradigms.