Decreasing New York esophageal squamous cell carcinoma 1 expression inhibits multiple myeloma growth and osteolytic lesions
Bibliographic record
Abstract
New York esophageal squamous cell carcinoma 1 (NY-ESO-1) is aberrantly expressed in multiple myeloma (MM) patients, however, its role remains largely unknown. The present study aimed to investigate the effect of NY-ESO-1 knockdown on MM impact and provide evidence for targeting treatment of MM. Human MM U266 cells were infected with lentivirus-based small hairpin RNA-targeting NY-ESO-1 (LV-shNY-ESO-1). Cellular proliferation, colony-forming, migration, and invasion assays were employed. The expressions of cell cycle and epithelial-mesenchymal transition (EMT)-related molecules, MM growth, and mouse osteolytic lesions were evaluated. The results showed that the LV-shNY-ESO-1-U266 cells had a lower expression of NY-ESO-1 and a higher expressions of p21 and E-cadherin, and a weaker abilities of colony formation, drug-resistant to adriamycin, migration, and invasion than those of the control cells. Importantly, the knockdown of NY-ESO-1 inhibited significantly the U266 cell-induced MM growth and osteolytic lesions along with increasing the expressions of E-cadherin, p21, and p53 in mice challenged with LV-shNY-ESO-1-U266 cells. Collectively, our findings demonstrate that knockdown of NY-ESO-1 suppressed the U266 cell-induced MM growth and osteolytic lesions by inhibition of the MMs cell cycle and EMT. The NY-ESO-1 knockdown may be considered for future clinical trials in MM.
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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 | 0.001 |
| 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".