Exploring How Reactive Oxygen Species Contribute to Cancer via Oxidative Stress
Bibliographic record
Abstract
Cancer remains a major global health burden driven by genetic, metabolic, and microenvironmental alterations. Although reactive oxygen species (ROS) and oxidative stress have long been implicated in cancer biology, current understanding remains fragmented and, in several areas, conceptually disputed considering how ROS and oxidative stress thresholds determine the switch between tumor-promoting signaling and cytotoxic outcomes, and whether redox-based therapies can be safely and selectively applied across different cancer types. Moreover, existing studies often examine isolated pathways or single ROS, leaving unanswered the question of how spatial and temporal ROS dynamics and oxidative stress responses shape carcinogenesis, metastasis, and therapeutic resistance. This review moves beyond descriptive summarization by critically examining unresolved mechanistic gaps, including (i) how ROS and oxidative stress interact with epigenetic and metabolic reprogramming, (ii) the context-dependent role of ROS-driven oxidative stress within the tumor microenvironment and immune evasion, and (iii) why ROS-targeting and oxidative stress-modulating therapies have shown inconsistent clinical translation despite promising preclinical data. We highlight areas of consensus as well as conflicting evidence, synthesizing recent advances across multiple cancer types to clarify where ROS and oxidative stress function as drivers, modulators, or vulnerabilities. Finally, we outline emerging research priorities, such as real-time redox profiling, subtype-specific targeting strategies, and combination approaches, to guide the development of more precise and effective ROS- and oxidative-stress-based interventions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".