Gianna Maggiore, Meng-Hsiung Hsieh, Amaey Bellary, Purva Gopal, Lin Li, Jason Guo, David Hsiehchen, Tulin Dadali, Wendy Broom, Martin Maier, Hao Zhu
{"title":"Chemoprevention of hepatocellular carcinoma using N-acetylgalactosamine-conjugated siRNAs.","authors":"Gianna Maggiore, Meng-Hsiung Hsieh, Amaey Bellary, Purva Gopal, Lin Li, Jason Guo, David Hsiehchen, Tulin Dadali, Wendy Broom, Martin Maier, Hao Zhu","doi":"10.1242/dmm.052370","DOIUrl":null,"url":null,"abstract":"<p><p>The ability to prevent hepatocellular carcinoma (HCC) in patients with chronic liver disease remains an unmet clinical need. We performed a head-to-head comparison of N-acetylgalactosamine (GalNAc)-conjugated small interfering RNA (siRNA)-mediated inhibition of five genes (CDK1, PD-L1, CTNNB1, SMYD3, ANLN) to prevent cancer in four distinct autochthonous HCC mouse models. siRNA targeting Cdk1 and Anln (siCdk1 and siAnln, respectively) increased overall survival in the CTNNB1/MYC hydrodynamic transfection (HDT) model, in which HCC formation is driven by oncogenes. Both long-term and transient dosing of siCtnnb1 or siAnln prevented cancer development in the NRASG12V/shp53-driven HDT model. siCdk1 and siAnln prevented cancer in a diethylnitrosamine/phenobarbital model, in which tumor formation is driven by mutagenesis and chemical injury. Moreover, siCtnnb1 and siAnln decreased cancer development in a metabolic dysfunction-associated steatohepatitis (MASH) model driven by a Western diet and carbon tetrachloride (CCl4). Given that the use of siAnln was effective in several models, we validated Anln effects using Cre-lox and found that histologic features of MASH and HCC development were independently reduced. This demonstrates that siRNAs are safe and effective in preventing HCC in a large panel of preclinical cancer models, and identifies ANLN as an effective chemoprevention target.</p>","PeriodicalId":11144,"journal":{"name":"Disease Models & Mechanisms","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12452061/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Disease Models & Mechanisms","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1242/dmm.052370","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/9/1 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The ability to prevent hepatocellular carcinoma (HCC) in patients with chronic liver disease remains an unmet clinical need. We performed a head-to-head comparison of N-acetylgalactosamine (GalNAc)-conjugated small interfering RNA (siRNA)-mediated inhibition of five genes (CDK1, PD-L1, CTNNB1, SMYD3, ANLN) to prevent cancer in four distinct autochthonous HCC mouse models. siRNA targeting Cdk1 and Anln (siCdk1 and siAnln, respectively) increased overall survival in the CTNNB1/MYC hydrodynamic transfection (HDT) model, in which HCC formation is driven by oncogenes. Both long-term and transient dosing of siCtnnb1 or siAnln prevented cancer development in the NRASG12V/shp53-driven HDT model. siCdk1 and siAnln prevented cancer in a diethylnitrosamine/phenobarbital model, in which tumor formation is driven by mutagenesis and chemical injury. Moreover, siCtnnb1 and siAnln decreased cancer development in a metabolic dysfunction-associated steatohepatitis (MASH) model driven by a Western diet and carbon tetrachloride (CCl4). Given that the use of siAnln was effective in several models, we validated Anln effects using Cre-lox and found that histologic features of MASH and HCC development were independently reduced. This demonstrates that siRNAs are safe and effective in preventing HCC in a large panel of preclinical cancer models, and identifies ANLN as an effective chemoprevention target.
期刊介绍:
Disease Models & Mechanisms (DMM) is an online Open Access journal focusing on the use of model systems to better understand, diagnose and treat human disease.