Metabolic reprogramming in glioblastoma: a rare case of recurrence to scalp metastasis
Bibliographic record
Abstract
BACKGROUND: Glioblastoma (GB), an aggressive brain malignancy with a poor prognosis of 1.5-2 years, rarely exhibits extracranial metastasis (ECM). However, metabolic reprogramming has emerged as a key driver of GB progression and invasiveness. This study presents a rare case of recurrent GB with scalp metastasis, exploring how metabolic shifts enable GB cells to evade treatment and adapt to hostile environments, offering insights for developing innovative therapies. METHODS: Tandem mass spectrometry (MS/MS) was employed to analyze amino acid profiles in both the recurrent and metastatic stages of GB. Systems biology approaches were used to uncover genetic alterations and metabolic reprogramming associated with the progression from recurrence to metastasis. RESULTS: Our analysis revealed distinct amino acid utilization patterns in a patient with a molecular phenotype of wild-type IDH-1&2, TERT mutation, non-mutated BRAF and EGFR, and non-methylated MGMT. During recurrence and metastasis, significant differences in amino acid profiles were observed between blood and cerebrospinal fluid (CSF) samples. Additionally, protein-protein interaction (PPI) analysis identified key genomic drivers potentially responsible for the transition from recurrent to metastatic GB. CONCLUSIONS: Beyond established risk factors such as craniotomy, biopsies, ventricular shunting, and radiation therapy, our findings suggest that metabolic reprogramming plays a crucial role in the transition from recurrent to metastatic GB. Targeting these metabolic shifts could provide new avenues for managing and preventing extracranial metastasis in GB, making this an important focus for future research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".