Transglutaminase II expression in invasive ductal carcinomas.
Bibliographic record
Abstract
34 Background: Molecular markers for predicting breast cancer patients at high risk of recurrence are urgently needed for more effective disease management. The impact of alterations in extracellular matrix components on tumor aggressiveness is under intense investigation. Overexpression of Transglutaminase 2 (TG2), a multifunctional enzyme, in cancer cells impacts epithelial mesenchymal transition and cancer stem cell phenotype, growth, invasion and interactions with tumor microenvironment. The objective of our study is to determine the clinical relevance of stromal TG2 overexpression and explore its potential to identify which IDCs are at high risk of recurrence. Methods: This retrospective study is based on immunohistochemical analysis of TG2 expression in 168 invasive ductal carcinomas with clinical, pathological and follow-up data available for up to 12 years. TG2 expression was correlated with clinical and pathological parameters as well as disease free survival (DFS) of breast cancer patients. Results: Stromal TG2 overexpression was observed in 97/168 (57.7%) invasive ductal carcinomas (IDC). Notably, IDC patients showing stromal TG2 accumulation had significantly reduced disease free survival (mean DFS = 110 months) in comparison with patients showing low expression (mean DFS = 130 months), as revealed by Kaplan-Meier survival analysis (p <0.001). In Cox multivariate regression analysis, TG2 accumulation in tumor stroma emerged as an independent risk factor for recurrence in IDC (p = 0.006, Hazard’s ratio, H.R. = 3.79). Conclusions: Accumulation of TG2 in tumor stroma is an independent risk factor for identifying breast cancer patients at high risk of recurrence. TG2 overexpression in tumor stroma may serve as a predictor of poor prognosis for IDC of the breast.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".