The obesity-autophagy-cancer axis: Mechanistic insights and therapeutic perspectives
Bibliographic record
Abstract
Autophagy, a self-degradative process vital for cellular homeostasis, plays a significant role in adipose tissue metabolism and tumorigenesis. This review aims to elucidate the complex interplay between autophagy, obesity, and cancer development, with a specific emphasis on how obesity-driven changes affect the regulation of autophagy and subsequent implications for cancer risk. The burgeoning epidemic of obesity underscores the relevance of this research, particularly given the established links between obesity, autophagy, and various cancers. Our exploration delves into hormonal influence, notably INS (insulin) and LEP (leptin), on obesity and autophagy interactions. Further, we draw attention to the latest findings on molecular factors linking obesity to cancer, including hormonal changes, altered metabolism, and secretory autophagy. We posit that targeting autophagy modulation may offer a potent therapeutic approach for obesity-associated cancer, pointing to promising advancements in nanocarrier-based targeted therapies for autophagy modulation. However, we also recognize the challenges inherent to these approaches, particularly concerning their precision, control, and the dual roles autophagy can play in cancer. Future research directions include identifying novel biomarkers, refining targeted therapies, and harmonizing these approaches with precision medicine principles, thereby contributing to a more personalized, effective treatment paradigm for obesity-mediated cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".