Knockdown of E-cadherin induces cancer stem-cell-like phenotype and drug resistance in cervical cancer cells
Bibliographic record
Abstract
Cervical cancer is one of the leading causes of mortality amongst women in developing countries, and resistance to therapy is the main reason for treatment failure. Recent advances suggest that cancer stem cells (CSCs) are critically involved in regulating the chemo-resistant behavior of cervical cancer cells. In our study, cells with the CSC phenotype were isolated, and we examined the expression levels of stem cell markers and genes associated with epithelial-mesenchymal transition (EMT) using different assays. However, the cells with the CSC phenotype could not be cultured for further cytotoxicity studies, so we established a model of CSC in cervical cancer cells. We performed siRNA-mediated knockdown of E-cadherin in these cells, and studied them for EMT-associated stem-cell-like properties. We also performed dose-dependent cell viability assays using clinically relevant drugs such as cisplatin, cyclopamine, and GANT58 to analyze the drug resistant behavior of these cancer cells. We found that knockdown of E-cadherin induces EMT in cervical cancer cells, imparting stem-cell like characteristics along with enhanced tumorsphere formation, cell migration, invasiveness, and drug resistance. This is the first study to establish a CSC model in cervical cancer cells by knockdown of E-cadherin, which can be used to develop anti-cancer therapies.
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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".