Targeting Wnt-β-Catenin Signaling Pathway for Hepatocellular Carcinoma Nanomedicine
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) represents a high-fatality cancer with a 5-year survival of 22%. The Wnt/β-catenin signaling pathway presents as one of the most upregulated pathways in HCC. However, it has so far not been targetable in the clinical setting. Therefore, studying new targets of this signaling cascade from a therapeutic aspect could enable reversal, delay, or prevention of hepatocarcinogenesis. Although enormous advancement has been achieved in HCC research and its therapeutic management, since HCC often occurs in the context of other liver diseases such as cirrhosis leading to liver dysfunction and/or impaired drug metabolism, the current therapies face the challenge of safely and effectively delivering drugs to the HCC tumor site. In this review, we discuss how a targeted nano drug delivery system could help minimize the off-target toxicities of conventional HCC therapies as well as enhance treatment efficacy. We also put forward the current challenges in HCC nanomedicine along with some potential therapeutic targets from the Wnt/β-catenin signaling pathway that could be used for HCC therapy. Overall, this review will provide an insight to the current advances, limitations and how HCC nanomedicine could change the landscape of some of the undruggable targets in the Wnt/β-catenin pathway.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".