Ferroptosis in Cancer and Inflammatory Diseases: Mechanisms and Therapeutic Implications
Bibliographic record
Abstract
Ferroptosis, an iron-dependent cell death pathway driven by lipid peroxidation, has emerged as a critical pathophysiological mechanism linking cancer and inflammatory diseases. The seemingly distinct pathologies exhibit shared microenvironmental hallmarks-oxidative stress, immune dysregulation, and metabolic reprogramming-that converge on ferroptosis regulation. This review synthesizes how ferroptosis operates at the intersection of these diseases, acting as both a tumor-suppressive mechanism and a driver of inflammatory tissue damage. In cancer, ferroptosis eliminates therapy-resistant cells but paradoxically facilitates metastasis through lipid peroxidation byproducts that remodel the tumor microenvironment and suppress antitumor immunity. In chronic inflammatory diseases-from atherosclerosis to rheumatoid arthritis-ferroptosis amplifies neuroinflammatory cascades while simultaneously exposing vulnerabilities for therapeutic targeting. Central to this duality are shared regulatory nodes, including nuclear factor kappa B-driven inflammation, NOD-like receptor family pyrin domain-containing 3 inflammasome activation, and GPX4 dysfunction. Therapeutically, ferroptosis induction shows promise against therapy-resistant cancers but risks exacerbating inflammatory damage, underscoring the need for precision modulation. Emerging strategies-nanoparticle-based inducers, immunotherapy combinations, and biomarker-guided patient stratification-aim to balance prodeath efficacy against off-target toxicity. By dissecting the ferroptosis-inflammation-cancer axis, this review provides a unified framework for understanding disease pathogenesis and advancing therapies for conditions resistant to conventional treatments. Future research must prioritize spatial mapping of ferroptosis dynamics, mechanistic crosstalk with immune checkpoints, and combinatorial regimens that exploit ferroptosis vulnerabilities while mitigating its inflammatory consequences.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".