Investigating autophagy and intricate cellular mechanisms in hepatocellular carcinoma: Emphasis on cell death mechanism crosstalk
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) stands as a formidable global health challenge due to its prevalence, marked by high mortality and morbidity rates. This cancer type exhibits a multifaceted etiology, prominently linked to viral infections, non-alcoholic fatty liver disease, and genomic mutations. The inherent heterogeneity of HCC, coupled with its proclivity for developing drug resistance, presents formidable obstacles to effective therapeutic interventions. Autophagy, a fundamental catabolic process, plays a pivotal role in maintaining cellular homeostasis, responding to stressors such as nutrient deprivation. In the context of HCC, tumor cells exploit autophagy, either augmenting or impeding its activity, thereby influencing tumorigenesis. This comprehensive review underscores the dualistic role of autophagy in HCC, acting as both a pro-survival and pro-death mechanism, impacting the trajectory of tumorigenesis. The anti-carcinogenic potential of autophagy is evident in its ability to enhance apoptosis and ferroptosis in HCC cells. Pertinently, dysregulated autophagy fosters drug resistance in the carcinogenic context. Both genomic and epigenetic factors can regulate autophagy in HCC progression. Recognizing the paramount importance of autophagy in HCC progression, this review introduces pharmacological compounds capable of modulating autophagy-either inducing or inhibiting it, as promising avenues in HCC therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".