Prognostic Significance of Tissue Inhibitor of Metalloproteinase-1 in Breast Cancer
Bibliographic record
Abstract
Introduction. Despite advances in breast cancer systemic treatment, new prognostic and predictive factors are still needed. Tissue inhibitor of metalloproteinase-1 (TIMP-1), a physiologic inhibitor of matrix metalloproteinases (MMPs), can act in both pro- and antitumoral effects. As role of TIMP-1 in breast cancer is controversial, we aimed to determine the prognostic significance of TIMP-1 in breast cancer. Methods. A single center-based case-control study was applied. Primary breast cancers from women with early stage disease treated with standard adjuvant therapy were analyzed by gene expression microarrays and immunohistochemistry for TIMP-1. Results. At the optimized cut-point, patients with high TIMP-1 RNA levels had a significantly shorter time to relapse, with a hazard ratio (HR) of 1.64 (P = 0.04), but without significant differences in overall survival (HR 1.29, P = 0.37). Although cytoplasmic overexpression of TIMP-1 protein was not correlated with early relapse (HR 1.0, P = 0.92), there was a tendency for short overall survival in patients with high expression (HR 1.41, P = 0.21). Conclusions. Our data indicate that elevated TIMP-1 RNA levels are independently prognostic for early recurrence, and there is a tendency for association of high cytoplasmic TIMP-1 protein levels with short survival in primary breast cancer.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".