The Lipid Hydrolase ABHD6 is a Therapeutic Target in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)-Related Hepatocellular Carcinoma
Bibliographic record
Abstract
Abstract Primary liver cancer accounts for approximately 700,000 deaths worldwide annually ranking third in cancer-related mortality, with hepatocellular carcinoma (HCC) comprising the majority of these tumors. Metabolic dysfunction-associated steatotic liver disease (MASLD) is currently a leading cause of HCC in the United States. We previously identified the lipid hydrolase alpha/beta hydrolase domain 6 (ABHD6) as a key mediator of the development of metabolic syndrome and intimately involved in cell signaling, making it a prime target for investigation in MASLD-related HCC. ABHD6 displays higher expression within HCC tumor cores when compared to adjacent non-tumor liver tissue in human subjects. Using an in vivo antisense oligonucleotide (ASO)-driven knockdown approach, we have shown the inhibition of ABHD6 prevents the development and progression of HCC in an obesity/MASLD-driven mouse model. Additionally, a xenograft model using the human Huh7 cell line displayed reduced tumor engraftment and growth with ABHD6 genetic deletion and small molecule inhibition. ABHD6 knockout cells demonstrated increased levels of bis(monoacylglycerol)phosphates (BMPs), lipids relevant to high fat diet-induced lysosomal dysfunction, and knockout cells also demonstrated altered autophagy and lysosomal activity using in vitro model of saturated fatty acid-induced lipotoxicity. These studies reveal novel lipid signaling mechanisms by which MASLD progresses towards HCC and provide support for ABHD6 as a therapeutic target in HCC. Significance We have identified that alpha/beta hydrolase domain 6 (ABHD6) plays a role in lysosomal membrane lipid remodeling pathways that are relevant in obesity/MASLD-driven HCC. Inhibitors targeting ABHD6 reorganize lysosomal lipid homeostasis to improve outcomes in HCC.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".