PEG10 promotes the migration of human Burkitt’s lymphoma cells by up-regulating the expression of matrix metalloproteinase-2 and -9
Bibliographic record
Abstract
PURPOSE: Paternally expressed gene 10 (PEG10) is important for apoptosis resistance in cancer cells; however, the effect of PEG10 on tumor cell migration remains poorly understood. In this study, we investigated the effects of PEG10 on proliferation, apoptosis, adhesion and migration in the Burkitt's lymphoma cell line, Raji. METHODS: Apoptosis was induced by 5-fluorouracil (5-FU) in pcDNA3.0/PEG10 transiently transfected HEK293T cells and PEG10-suppressed Raji cells. siRNAPEG10 was used to inhibit PEG10 expression. Fluorescence-activated cell sorting (FACS) were performed to analyze the effect of PEG10 on apoptosis. CCK-8 were performed to detect cell proliferation and adhesion. Matrigel invasion were performed using PEG10-suppressed Raji cells to investigate cell migration. The expression levels of matrix metalloproteinases -2and -9 (MMP-2 and MMP-9) were analyzed in PEG10-suppressed Raji cells using both real-time RT-PCR and Western blot analysis. RESULTS: HEK293T cells that overexpressed PEG10 exhibited greater viability 48 h following treatment with 5-FU, relative to control cells. Specific inhibition of PEG10 expression by siRNA resulted in inhibition of growth and apoptosis in Raji cells. Adherence and invasion capabilities were downregulated and expression levels of MMP-2 and MMP-9 were reduced in PEG10-suppressed Raji cells. CONCLUSIONS: Our findings demonstrated that PEG10 enhances the apoptotic resistance and viability of Raji cells. The migration and adherence invasion capacity of Raji cells could potentially be affected by regulation of the expression of MMP-2 and MMP-9. Our research provides a promising strategy for cancer immunotherapy of lymphoma.
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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".