Advancements in Utilizing Natural Compounds for Modulating Autophagy in Liver Cancer: Molecular Mechanisms and Therapeutic Targets
Bibliographic record
Abstract
Autophagy, an intrinsic catabolic mechanism that eliminates misfolded proteins, dysfunctional organelles, and lipid droplets, plays a vital function in energy balance and cytoplasmic quality control, in addition to maintaining cellular homeostasis. Liver cancer such as hepatocellular carcinoma (HCC) is one of the most common causes of cancer deaths globally and shows resistance to several anticancer drugs. Despite the rising incidence and poor prognosis of malignant HCC, the underlying molecular mechanisms driving this aggressive cancer remain unclear. Several natural compounds, such as phytochemicals of dietary and non-dietary origin, affect hepatocarcinogenesis signaling pathways in vitro and in vivo, which may help prevent and treat HCC cells. Current HCC cells treatments include chemotherapy, radiation, and surgery. However, these standard therapies have substantial side effects, and combination therapy enhances side effects for an acceptable therapeutic benefit. Therefore, there is a need to develop treatment strategies for HCC cells that are more efficacious and have fewer adverse effects. Multiple genetic and epigenetic factors are responsible for the HCC cells to become resistant to standard treatment. Autophagy contributes to maintain cellular homeostasis, which activates autophagy for biosynthesis and mitochondrial regulation and recycling. Therefore, modifying autophagic signaling would present a promising opportunity to identify novel therapies to treat HCC cells resistant to current standard treatments. This comprehensive review illustrates how natural compounds demonstrate their anti-hepatocellular carcinoma function through autophagy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".