Proteomics in IDH-mutated diffuse lower-grade glioma: a scoping review
Bibliographic record
Abstract
Background: Therapeutic options and biomarkers for isocitrate dehydrogenase-mutated (IDHmut) diffuse lower-grade glioma (dLGG), WHO grade 2-3, are limited. Global quantitative proteomics has aided the discovery of novel markers and drug targets across various pathologies. This review aimed to summarize current proteomic findings in IDHmut dLGG. Methods: PubMed, Embase, and Scopus were searched following PRISMA-ScR guidelines. Studies examining quantitative proteomics in IDHmut dLGG with liquid chromatography-mass spectrometry in adult human samples were included. Studies with only high-grade gliomas, without IDHmut, using xenografts, or cell line samples, and reviews were excluded. Results: In total, 1,902 records were identified; 85 full-texts were retrieved, and 13 met the inclusion criteria. Twelve studies were cross-sectional and one longitudinal. Two studies used cerebrospinal fluid samples, while seven used fresh frozen and five formalin-fixed paraffin-embedded (FFPE) tissue samples. There was a large heterogeneity in aims, sample types, and analytical techniques. The most recurrent finding was altered energy metabolism, mostly related to the tricarboxylic acid cycle, compared to IDH-wildtype gliomas. IDHmut dLGG proteomic profile was distinct from other brain tumors, including IDH-wildtype glioblastoma, IDHmut grade 4 astrocytomas, and grade 1 gliomas or normal brain. Conclusions: IDHmut dLGG has a unique proteome that may be leveraged for biomarkers and therapeutic discovery. Proteomic findings indicate a particular dependency on glutamate metabolism to sustain the citric acid cycle and energy production. Although current proteomic knowledge is limited and fragmented, technological advancements present an opportunity for large-scale studies using FFPE samples, advancing proteomic knowledge and precision medicine in IDHmut dLGG.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".