Differential Expression and Clinical Significance of Transforming Growth Factor-Beta Isoforms in GBM Tumors
Bibliographic record
Abstract
Glioblastoma (GBM) represents the most common and aggressive malignant primary brain tumors in adults. Response to standard treatment is transitory and the survival of clinical trial cohorts are little more than 14 months. GBM are characterized by excessive proliferation, invasiveness, and radio-/chemoresistance features; which are strongly upregulated by transforming growth factor-beta (TGF-β). We hypothesized that TGF-β gene expression could correlate with overall survival (OS) and serve as a prognostic biomarker. TGF-β₁ and -β₂ expression were analyzed by qPCR in 159 GBM tumor specimens. Kaplan-Meier and multivariate analyses were used to correlate expression with OS and progression-free survival (PFS). In GBM, TGF-β₁ and -β₂ levels were 33- and 11-fold higher respectively than in non-tumoral samples. Kaplan-Meier and multivariate analyses revealed that high to moderate expressions of TGF-β₁ significantly conferred a strikingly poorer OS and PFS in newly diagnosed patients. Interestingly, at relapse, neither isoforms had meaningful impact on clinical evolution. We demonstrate that TGF-β₁ is the dominant isoform in newly diagnosed GBM rather than the previously acknowledged TGF-β₂. We believe our study is the first to unveil a significant relationship between TGF-β₁ expression and OS or PFS in newly diagnosed GBM. TGF-β₁ could serve as a prognostic biomarker or target affecting treatment planning and patient follow-up.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".