The role of autophagy in cancer: from molecular mechanism to therapeutic window
Bibliographic record
Abstract
Autophagy is a cellular degradation process that plays a crucial role in maintaining metabolic homeostasis under conditions of stress or nutrient deprivation. This process involves sequestering, breaking down, and recycling intracellular components such as proteins, organelles, and cytoplasmic materials. Autophagy also serves as a mechanism for eliminating pathogens and engulfing apoptotic cells. In the absence of stress, baseline autophagy activity is essential for degrading damaged cellular components and recycling nutrients to maintain cellular vitality. The relationship between autophagy and cancer is well-established; however, the biphasic nature of autophagy, acting as either a tumor growth inhibitor or promoter, has raised concerns regarding the regulation of tumorigenesis without inadvertently activating harmful aspects of autophagy. Consequently, elucidating the mechanisms by which autophagy contributes to cancer pathogenesis and the factors determining its pro- or anti-tumor effects is vital for devising effective therapeutic strategies. Furthermore, precision medicine approaches that tailor interventions to individual patients may enhance the efficacy of autophagy-related cancer treatments. To this end, interventions aimed at modulating the fate of tumor cells by controlling or inducing autophagy substrates necessitate meticulous monitoring of these mediators' functions within the tumor microenvironment to make informed decisions regarding their activation or inactivation. This review provides an updated perspective on the roles of autophagy in cancer, and discusses the potential challenges associated with autophagy-related cancer treatment. The article also highlights currently available strategies and identifies questions that require further investigation in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".