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. 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".