Differential expression of TGFβ isoforms in breast cancer highlights different roles during breast cancer progression
Bibliographic record
Abstract
While TGFβ plays a critical role in tumor formation and progression, the role and contribution of its three different isoforms remain unclear. In this study, we aimed at elucidating the prognostic value of the TGFβ isoforms and assessed their expression levels in breast cancer patients at different stages of the disease. We found higher levels of TGFβ1 and TGFβ3 in cancer patients compared to normal tissues, with no significant changes in TGFβ2 expression. Similarly, TGFβ1 and TGFβ3, but not TGFβ2, showed higher expression levels in advanced lymph node-positive and metastatic tumors, suggesting different roles for the different isoforms in tumor progression and the metastatic process, while in the least aggressive molecular subtype (luminal A), expression of the three TGFβ isoforms significantly correlated with expression of both TGFβ receptors, such correlation only occurred between TGFβ1 and TGFβ3 and the TGFβ type II receptor (TβRII) in the highly aggressive basal-like subtype. Interestingly, a distinct and somehow opposite pattern was observed in HER-2 tumors, only showing significant association pattern between TGFβ2 and the TGFβ type I receptor (TβRI). Finally, the three TGFβ isoforms showed distinct association patterns with patient outcome depending on the different molecular subtype, highlighting context-dependent, differential prognostic values.
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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 | 0.001 |
| 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) | 0.001 | 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".