The biological role of psoriasin in breast tumorigenesis
Bibliographic record
Abstract
Alteration of psoriasin (S100A7) has previously been identified in association with the transition from preinvasive to invasive breast cancer. In this thesis we examined the 'in vitro' and 'in vivo' effects of psoriasin in two cell line models: MCF10AT3B, a "normal" breast cell line derived from non-neoplastic fibrocystic breast tissue and MDA-MB-231, an invasive breast cancer cell line. We first developed stable transfectants, along with appropriate vector alone controls and studied them in ' in vitro' cell growth and invasion assays, as well assessed them ' in vivo' with respect to tumor growth, mitotic abundance and necrotic content. We found that psoriasin has no effect on in vitro cell growth and invasive behavior in "normal" MCF10AT3B and invasive MDA-MB-231 breast cancer cells. Although we could not establish the tumorigenic "normal" breast cell line MCF10AT3B into a Balb C nu/nu mouse model, we were successful in our attempts using the invasive breast cancer cell line MDA-MB-231. Data from these experiments confirmed that psoriasin does not alter tumor cell growth 'in vivo'. As well, we observed that mitotic abundance and the amount of necrosis is independent of the presence of psoriasin in invasive MDA-MB-231 breast cancer cells. These results propose that psoriasin behaves similarly in "normal" and breast cancer models, nevertheless, our previous data suggests a role in the events that govern breast cancer. Specifically, although there is no evidence suggesting that psoriasin alters cell growth and invasion directly, psoriasin may govern the progression of early breast cancer due to changes in adhesion or angiogenesis.
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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 | 0.000 |
| 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".