Current insights on transglutaminase 2: Exploring its functions, mechanisms, and therapeutic potential in glioblastoma
Bibliographic record
Abstract
Glioblastoma (GBM) is the most aggressive and lethal primary brain tumor in adults, marked by diffuse infiltration, extensive heterogeneity, and resistance to standard therapies. Despite advances in surgery, radiation, and chemotherapy, GBM remains incurable, with a median survival of ∼15 months. Tumor recurrence, driven by therapy-resistant glioma stem cells and adaptive molecular mechanisms, presents a significant challenge to treatment. Identifying regulators of GBM progression and resistance is crucial for developing more effective interventions. Transglutaminase 2 (TGM2), a ubiquitously expressed enzyme with both Ca 2+ ‐dependent and -independent activities, has emerged as a pivotal yet underexplored context-specific contributor to various malignancies. Aberrant TGM2 expression has been linked to hallmark features of GBM, including stemness, invasion, epithelial-to-mesenchymal transition, and chemo-radioresistance. However, its multifunctionality, conformational flexibility, and widespread subcellular localization have complicated efforts to delineate precise oncogenic mechanisms. Conflicting data suggest TGM2 may promote both survival and apoptosis, underscoring the need for nuanced investigation. This review provides an overview of TGM2’s structural features, biochemical functions, and regulatory mechanisms, with a focus on its role in GBM progression and resistance to chemo-radiotherapy. Emerging evidence implicates TGM2 in enhancing DNA repair, promoting cellular plasticity, and evading apoptosis, all of which contribute to tumor survival. Targeting TGM2 has shown promise in preclinical studies, especially inhibitors that exhibit the potential to cross the blood-brain barrier, addressing a major challenge in effective GBM therapy. By integrating molecular and translational insights, this review highlights TGM2 as a promising therapeutic target for overcoming resistance and advancing combined precision strategies for GBM treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".