Past, present, and future of chemically induced hepatocarcinogenesis rodent models: Perspectives concerning classic and new cancer hallmarks

癌症 表观遗传学 肝细胞癌 生物 肝癌 重编程 非酒精性脂肪肝 生物信息学 疾病 癌症研究 转化研究 计算生物学 微生物群 癌变 表型 脂肪肝 医学 遗传学 病理 基因 生物技术
作者
Guilherme Ribeiro Romualdo,Renato Heidor,Gabriel Prata Bacil,Fernando Salvador Moreno,Luı́s Fernando Barbisan
出处
期刊:Life Sciences [Elsevier]
卷期号:330: 121994-121994 被引量:2
标识
DOI:10.1016/j.lfs.2023.121994
摘要

Hepatocellular carcinoma (HCC), the main primary liver cancer, accounts for 5 % of all incident cases and 8.4 % of all cancer-related deaths worldwide. HCC displays a spectrum of environmental risk factors (viral chronic infections, aflatoxin exposure, alcoholic- and nonalcoholic fatty liver diseases) that result in molecular complexity and heterogeneity, contributing to a rising epidemiological burden, poor prognosis, and non-satisfactory treatment options. The emergence of HCC (i.e., hepatocarcinogenesis) is a multistep and complex process that addresses many (epi)genetic alterations and phenotypic traits, the so-called cancer hallmarks. “Polymorphic microbiomes”, “epigenetic reprogramming”, “senescent cells” and “unlocking phenotypic plasticity” are trending hallmarks/enabling features in cancer biology. As the main molecular drivers of HCC are still undruggable, chemically induced in vivo models of hepatocarcinogenesis are useful tools in preclinical research. Thus, this narrative review aimed at recapitulating the basic features of chemically induced rodent models of hepatocarcinogenesis, eliciting their permanent translational value regarding the “classic” and the “new” cancer hallmarks/enabling features. We gathered state-of-art preclinical evidence on non-cirrhotic, inflammation-, alcoholic liver disease- and nonalcoholic fatty liver-associated HCC models, demonstrating that these bioassays indeed express the recently added hallmarks, as well as reflect the interplay between classical and new cancer traits. Our review demonstrated that these protocols remain valuable for translational preclinical application, as they recapitulate trending features of cancer science. Further “omics-based” approaches are warranted while multimodel investigations are encouraged in order to avoid “model-biased” responses.
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