Evaluating usefulness of ING1 expression in stroma as a prognostic biomarker in breast cancer.
Bibliographic record
Abstract
e11543 Background: INhibitor of Growth (ING) proteins are type II tumor suppressors that are frequently down-regulated in diverse cancer types, including the neoplastic transformation of breast tissue. The ING1 gene encodes multiple isoforms that have been shown to affect apoptosis, senescence and cell cycle. Recently, we reported that low levels of ING1 protein are correlated with metastasis in breast cancer patients. In the present study, we have used immunohistochemistry and an automated quantitative analysis (AQUA) technique to quantify ING1 expression in the tumor and stromal components of breast cancer tissue. Methods: We used the Calgary Tamoxifen Cohort, which contains 816 breast cancer patients, for this study. Cytokine profiling was done using immortalized human mammary fibroblasts (HMF3s) infected with adenoviral ING1a or GFP. To determine the activity of matrix metalloproteases MMP-1 and MMP-2, gelatin and casein zymography were performed, respectively. Three-dimensional culture was performed using ING1a or GFP infected HMF3s and MCF7 cells. Results: Higher expression of ING1 in stroma was associated with worse prognosis in breast cancer cohort. The predictive value of stromal ING1 was found to be more significant than established biomarkers such as HER2 and ER. An increase in the levels of G-CSF, GM-CSF and MIP-1a was observed. However, a significant decrease was found in pro-inflammatory cytokines such as IL-6, IL-8, PDGFA, PDGFB, VEGF and GRO in HMF3s cells upon ING1a overexpression. ING1a overexpression increased the metalloproteases (MMPs) levels and decreased the levels of MMP inhibitors (TIMPs). ING1a overexpressing HMF3s cells induced disorganization of breast cancer cell derived organoids. Conclusions: Our study indicates the role of ING1a in breast cancer stroma and its role in epithelial transformation and suggests ING1 as a novel prognostic biomarker in 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 | 0.000 |
| 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".