NSUN2-mediated cytosine-5 methylation of FSP1 protects acute myeloid leukemia cells from ferroptosis
Bibliographic record
Abstract
RNA 5-methylcytosine (m 5 C), a prevalent epitranscriptomic modification that critically regulates gene expression and cellular homeostasis. While its roles in solid tumors have been increasingly recognized, the functional landscape of m 5 C in acute myeloid leukemia (AML) remains unexplored. Here, we identified NSUN2, the principal RNA m 5 C methyltransferase, as a key regulator of AML progression. NSUN2 was aberrantly upregulated in AML patient samples and correlated with poor prognosis. Functional studies demonstrated that NSUN2 promoted leukemic cell proliferation, enhanced tumor growth in xenograft models, and conferred resistance to ferroptosis—a regulated cell death process driven by lipid peroxidation. Mechanistically, NSUN2 catalyzed m⁵C deposition on the 3’UTR of FSP1 (ferroptosis suppressor protein 1) mRNA, facilitating its recognition and stabilization by the m 5 C reader protein YBX1. This NSUN2-YBX1-FSP1 axis protected AML cells from ferroptotic stress by suppressing lipid peroxidation and oxidative damage. Depletion of NSUN2 or FSP1 induced mitochondrial remodeling, which primed cells for ferroptosis. Reconstitution of wild-type NSUN2 or FSP1 rescued ferroptosis resistance, whereas catalytically inactive NSUN2 (C271A/C321A) or non-functional FSP1 mutants (G2A/E156A) failed to reverse this phenotype. Pharmacological inhibition of NSUN2 with MY-1B or targeting FSP1 with iFSP1 exhibited potent anti-leukemic effects, synergizing robustly with ferroptosis inducers, standard chemotherapy, and the BCL-2 inhibitor venetoclax. Our study unveils NSUN2 and FSP1 as prognostic biomarkers and therapeutic targets in AML. We highlight a novel epitranscriptomic mechanism linking RNA methylation to ferroptosis evasion, providing a dual-strategy approach to overcome AML treatment 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) | 0.001 | 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".