Erythropoietin inhibits chemotherapy-induced cell death and promotes a senescence-like state in leukemia cells
Bibliographic record
Abstract
Abstract There are conflicting reports on the adverse effects of erythropoietin (EPO) for the management of cancer-associated anemia. The recognition that erythropoietin receptors (EPORs) are expressed outside the erythroid lineage and concerns that erythropoiesis-stimulating agents (ESAs) may cause tumors to grow and increase the risk of venous thromboembolism have resulted in substantially fewer cancer patients receiving ESA therapy to manage myelosuppressive chemotherapy. In this study, we found that EPO suppresses p53-dependent apoptosis induced by genotoxic (daunorubicin, doxorubicin, and γ-radiation) and non-genotoxic (nutlin-3a) agents and induces a senescence-like state in myeloid leukemia cells. EPO interferes with stress-dependent Mdm2 downregulation and leads to the destabilization of p53 protein. EPO selectively modulates the expression of p53 target genes in response to DNA damage preventing the induction of a number of noncoding RNAs (ncRNAs) previously associated with p53-dependent apoptosis. EPO also enhances the expression of the cyclin-dependent kinase inhibitor p21WAF1 and promotes recruitment of p53 to the p21 promoter. In addition, EPO antagonizes Mcl-1 protein degradation in daunorubicin-treated cells. Hence, EPO signaling targets Mcl-1 expression and the p53-Mdm2 network to promote tumor cell survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".