The Role of Versican in Modulating Breast Cancer Cell Self-renewal
Bibliographic record
Abstract
Versican is highly expressed during the early stages of tissue development and its expression is elevated during wound repair and tumor growth. There is little literature on the potential role of breast cancer stem cells on the cellular-extracellular matrix interactions involving versican. An anti-versican short hairpin RNA (shRNA) was used to observe the effect of reduction of versican on breast cancer self-renewal. A versican G3 construct was exogenously expressed in breast cancer cell lines. Colony formation and mammosphere formation assays were conducted; flow cytometry was applied to analyze the prevalence of side population cells. The versican G3- and vector-transfected 66c14 cells were injected transdermally into BALB/c mice as a 10-fold dilution series from 1 × 10(5) to 1 × 10(2) cells per mouse. Versican G3 domain enhanced breast cancer self-renewal in both experimental in vitro and in vivo models. Versican G3-transfected cells contained high levels of side population cells, formed more mammospheres when cultured in the serum-free medium, and formed a greater number and larger colonies. Reduction of versican's functionality through anti-versican shRNA or knocking out the EGF-like motifs reduced the effect of versican on enhancing mammosphere and colony formation. Versican-enhanced self-renewal played a role in enhanced chemotherapeutic drug resistance, relating partly to the upregulated expression of EGF receptor (EGFR) signaling. Versican is highly expressed in breast cancer progenitor cells and was maintained at high levels before cell differentiation. Overexpression of versican enhanced breast cancer self-renewal through EGFR/AKT/GSK-3β (S9P) signaling and conferred resistant to chemotherapeutic drugs tested.
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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".