Evidence map›Paper›PMID 40640380›Full record

ArticleScientific reports2025

Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics.

Yi Ding, Zhaiyue Xu, Wenjing Hu, Peng Deng, Mian Ma, Jiandong Wu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Review
  6. The functional imperative in high-grade glioma.Experimental & molecular medicine · 2026
    Review
  7. Review
  8. Multi-Omics Integration for Advancing Glioma Precision Medicine.Annals of clinical and translational neurology · 2026
    Review
  9. Review
  10. Review
  11. Article
  12. Article
  13. Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025
    Review
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yi DingDepartment of Neurosurgery, Nanjing Medical University Affiliated Suzhou Hospital, 26 Daoqian Street, Suzhou, 215002, Jiangsu Province, People's Republic of China.
Zhaiyue XuMedical Faculty, Heidelberg University, Heidelberg, 69120, Baden-Württemberg, Germany.
Wenjing HuMedical Record Department, Nanjing Medical University Affiliated Suzhou Hospital, 26 Daoqian Street, Suzhou, 215002, Jiangsu Province, People's Republic of China.
Peng DengDepartment of Neurosurgery, Nanjing Medical University Affiliated Suzhou Hospital, 26 Daoqian Street, Suzhou, 215002, Jiangsu Province, People's Republic of China.
Mian MaDepartment of Neurosurgery, Nanjing Medical University Affiliated Suzhou Hospital, 26 Daoqian Street, Suzhou, 215002, Jiangsu Province, People's Republic of China. 343739595@qq.com.
Jiandong WuDepartment of Neurosurgery, Nanjing Medical University Affiliated Suzhou Hospital, 26 Daoqian Street, Suzhou, 215002, Jiangsu Province, People's Republic of China. Wujiandong1967@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioma is a highly heterogeneous and aggressive brain tumour that demands an integrated understanding of its molecular and immunological landscape. We collected multi-omics data from 575 TCGA diffuse-glioma patients (156 IDH-wild-type WHO-grade 4 glioblastomas and 419 IDH-mutant WHO-grade 2/3 diffuse gliomas) together with two validation cohorts (CGGA n = 970; GEO n = 110). Using the MOVICS framework, we derived three integrative molecular subtypes-CS1, CS2 and CS3. Ten machine-learning algorithms in MIME were benchmarked, and the Lasso + SuperPC combination yielded an eight-gene GloMICS (Glioma Multi-Omics Consensus Signature) prognostic score. The subtypes display discrete biology: CS1 (astrocyte-like) is characterized by glial lineage features, immune-regulatory signaling, and relatively favorable prognosis; CS2 (basal-like/mesenchymal) shows epithelial-mesenchymal transition, stromal activation, and high immune infiltration, including PD-L1 expression; CS3 (proneural-like/IDH-mut metabolic) exhibits metabolic reprogramming (OXPHOS, hypoxia) and an immunologically cold tumour microenvironment (TME). CS2 is associated with the worst overall survival, whereas CS1 confers the most favourable outcome. Dual checkpoint blockade or T-cell-rejuvenation strategies may benefit CS2 tumours, while metabolic inhibitors could prove effective in CS3. The eight-gene GloMICS score outperformed 95 published prognostic models (C-index 0.74-0.66 across TCGA, CGGA and GEO). TME deconvolution, immune checkpoint profiling and TIDE analysis indicate that high-risk GloMICS tumours harbour immunosuppressive fibroblast-rich niches and exhausted CD8⁺ T cells. Connectivity-map screening nominated dabrafenib, irinotecan and three additional CTRP/PRISM compounds as candidate agents for the high-risk group. Our study establishes robust glioma subtypes and a transferable prognostic signature, offering a blueprint for biomarker-guided therapy. Future work should include single-cell and immunohistochemical validation of subtype hallmarks and prospective trials stratified by GloMICS score.

Indexed as

Brain NeoplasmsGliomaMachine LearningPrecision MedicineBiomarkers, TumorGene Expression Regulation, NeoplasticGenomicsHumansMultiomicsMutationPrognosisTumor MicroenvironmentBiomarkers, Tumor

Identifiers

PMID40640380
PMCPMC12246227

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.