SENP1 knockdown potentiates the apoptosis, cell cycle arrest, and reduces cisplatin resistance of diffuse large B cell lymphoma cells via inducing ferroptosis
Bibliographic record
Abstract
Ferroptosis has been regarded as a critical event in the process of diffuse large B cell lymphoma (DLBCL). Sentrin-specific protease 1 (SENP1) has emerged as an oncogene in multiple human malignancies. The present work was to investigate the effects of SENP1 on the progression of DLBCL and the possible regulatory mechanism involving ferroptosis. SENP1 expression in DLBCL tissues, parental and cisplatin-resistant DLBCL cells were, respectively, tested by GEPIA database, RT-qPCR, and Western blot. Cell viability was estimated via CCK-8 assay. Flow cytometry analysis estimated cell apoptosis and cycle. Western blot examined the expression of apoptosis-, cell cycle-, and ferroptosis-associated proteins. TBARS assay and BODIPY 581/591 C11 probe measured lipid peroxidation. Related assay kit assessed total iron levels. CCK-8 and flow cytometry evaluated cisplatin resistance. SENP1 expression was raised in DLBCL tissues and cells. SENP1 knockdown reduced cell viability, boosted cell apoptosis, cell cycle arrest, and elevated cisplatin sensitivity in DLBCL. SENP1 depletion drove the ferroptosis of both parental and cisplatin-resistant DLBCL cells and ferroptosis inhibitor Fer-1 reversed the influences of SENP1 inhibition on cell viability, apoptosis, cell cycle, and cisplatin resistance in DLBCL. Anyway, SENP1 absence might facilitate ferroptosis to obstruct the development of DLBCL and cisplatin resistance.
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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) | 0.002 | 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".