Editorial: Targeting redox regulation and autophagy systems for cancer therapy
Bibliographic record
Abstract
Targeting redox regulation and autophagy systems for cancer therapyAutophagy and increased ROS production are important contributors to cancer metabolic remodeling (1,2), and an interplay between both processes has been described with important consequences on cancer cell survival and death (3).Both ROS production and autophagy have a context and stage-dependent role on tumorigenesis and cancer progression.For instance, autophagy is a tumor suppressor pathway but can promote cancer cell survival under diverse stress conditions (4).Similarly, increased ROS have been implicated in tumorigenesis caused by diverse infectious or environmental agents as well as in the maintenance of cancer cell signaling pathways (5, 6), but have been suggested to limit metastasis formation (7, 8).Thus, a detailed study of the role of ROS and autophagy is needed, to understand their role in cancer initiation and progression and to design effective therapies targeting these mechanisms.This issue includes manuscripts covering diverse aspects of ROS and autophagy in cancer progression and therapy.The review by Hasan et al., describes the close interplay between ROS and autophagy in cancer cells and the complexity involved in designing anticancer therapies targeting these processes.In a different review, Roy et al. describe the diverse roles of autophagy in response to cancer radiotherapy (RT), outlining the role of autophagy in deciding the cellular fate upon exposure to radiation, in cancer stem cell (CSC) maintenance and the radiation-induced bystander effect as well as its contribution to cell survival or death and its interplay with radiation-induced ROS.The authors emphasize the role of autophagy inhibitors and inducers in radiosensitization and, they refer to studies where autophagy markers were related to a poor therapeutic outcome in patients treated with RT or chemoradiotherapy (CRT).The authors also mention clinical trials using autophagy inhibitors as adjuvant to CRT in glioblastoma with promising results as well as another one in pancreatic cancer patients where no significant improvement in survival was observed.They conclude that an encouraging clinical response using autophagy regulators is needed to incorporate them as adjuvant to current RT or CRT regimens.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.027 | 0.020 |
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".