Reduced expression of the small leucine-rich proteoglycans, lumican, and decorin is associated with poor outcome in node-negative invasive breast cancer.
Bibliographic record
Abstract
PURPOSE: To examine the prognostic significance of lumican and decorin, two abundant small leucine-rich proteoglycans in breast tissue stroma. EXPERIMENTAL DESIGN: Lumican and decorin expression was examined in a cohort of 140 invasive breast carcinomas by Western blot analysis. All cases were axillary lymph node-negative and treated by adjuvant endocrine therapy. RESULTS: Lumican and decorin expression was highly correlated (r = 0.45, P < 0.0001), but although low levels of lumican were associated with large tumor size (P = 0.0496), negative estrogen receptor (P = 0.0024) and progesterone receptor status (P = 0.0116), and increased host inflammatory response (P = 0.0077), low decorin levels were associated only with large tumor size (P = 0.0496). However, using univariate analysis, low levels of lumican and decorin were both associated with a shorter time to progression (P = 0.0013 and 0.0262) and poorer survival (P = 0.001 and 0.0076). In multivariate analysis using the Cox regression model, low decorin was also shown to be an independent predictive factor for recurrence (hazard ratio 2.25: 95% confidence interval 1-5, P = 0.047) and survival (hazard ratio 3.39: 95% confidence interval 1.2-9.6, P = 0.021). CONCLUSIONS: These results suggest that low levels of small leucine-rich proteoglycans in breast tumors may be associated with a worse prognosis in lymph node-negative invasive breast carcinomas and warrant further study with larger patient cohorts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".