The HepG2 Cell Line as a Model for Studying Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease (NAFLD), is the most common chronic liver disease in the world. The disease progresses from steatosis to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis, and hepatocellular carcinoma. The modern concept of "multiple parallel hits" interprets disease progression as the result of the synergistic action of lipotoxicity, oxidative stress, mitochondrial dysfunction, endoplasmic reticulum stress, proinflammatory signals, and gut-liver axis dysfunction. Against the background of the limited translation of preclinical data from animal models due to interspecies differences, the importance of human-oriented in vitro platforms compatible with controlled design and high-throughput screening is increasing. The current review analyzes MASLD models based on the HepG2 cell line, systematizing steatosis induction protocols, evaluating the metabolic characteristics and limitations of this cell, and comparing 2D monocultures, 3D systems, and co-cultures. HepG2 has been shown to demonstrate a predictable steatogenic response to free fatty acids (FFAs) and is convenient for reproducing early stages of pathogenesis and primary pharmacological selection of compounds. At the same time, key limitations of the model are highlighted, namely tumor origin, glycolytic shift (Warburg effect), reduced β-oxidation, impaired very-low-density lipoprotein (VLDL) assembly and secretion, and sharply reduced cytochrome P450 (CYP450) activity, as well as limited reproducibility of fructose-induced de novo lipogenesis (DNL). Comparative analysis demonstrates an increase in physiological relevance with the transition from 2D to 3D and multicomponent co-cultures, accompanied by increased complexity and cost, but allowing for the modeling of inflammation and fibrogenesis. The review justifies approaches to selecting the appropriate platform based on the specific research task.