Cathepsin B: a potential prognostic marker for inflammatory breast cancer
Bibliographic record
Abstract
BACKGROUND: Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer. In non-IBC, the cysteine protease cathepsin B (CTSB) is known to be involved in cancer progression and invasion; however, very little is known about its role in IBC. METHODS: In this study, we enrolled 23 IBC and 27 non-IBC patients. All patient tissues used for analysis were from untreated patients. Using immunohistochemistry and immunoblotting, we assessed the levels of expression of CTSB in IBC versus non-IBC patient tissues. Previously, we found that CTSB is localized to caveolar membrane microdomains in cancer cell lines including IBC, and therefore, we also examined the expression of caveolin-1 (cav-1), a structural protein of caveolae in IBC versus non-IBC tissues. In addition, we tested the correlation between the expression of CTSB and cav-1 and the number of positive metastatic lymph nodes in both patient groups. RESULTS: Our results revealed that CTSB and cav-1 were overexpressed in IBC as compared to non-IBC tissues. Moreover, there was a significant positive correlation between the expression of CTSB and the number of positive metastatic lymph nodes in IBC. CONCLUSIONS: CTSB may initiate proteolytic pathways crucial for IBC invasion. Thus, our data demonstrate that CTSB may be a potential prognostic marker for lymph node metastasis in IBC.
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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 | 0.001 |
| 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 | 0.001 | 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".