Pathogenesis and management of metabolic dysfunction-associated steatohepatitis-related hepatocellular carcinoma: a narrative review

Metabolic dysfunction-associated steatohepatitis (MASH) is increasingly recognized as a leading cause of hepatocellular carcinoma (HCC), the third-leading cause of cancer mortality worldwide, driven by the global obesity epidemic. Projected to become the primary cause of HCC by 2030, MASH-HCC presents unique clinical challenges. This review examines its clinical management, including surveillance strategies and treatment advances, and discusses prospects to overcome existing challenges. MASH-HCC accounts for 10–20% of HCC cases, particularly in Western countries, with a rising incidence due to obesity. Risk factors include cirrhosis, diabetes, obesity, alcohol, smoking, genetic polymorphisms (e.g., PNPLA3), and microbiome alterations. The pathogenesis involves fibrosis, immune dysfunction (e.g., T-cell impairment), and molecular changes. Prevention focuses on lifestyle modifications. Surveillance in patients with MASH cirrhosis is crucial but is hindered by poor ultrasound sensitivity in obese patients, necessitating alternative methods. Treatment mirrors that of other HCC types, but comorbidities and potentially reduced efficacy of immunotherapy necessitate tailored approaches. MASH is becoming the leading cause of HCC, necessitating lifestyle interventions for prevention. Improved surveillance and early detection are critical but challenging due to obesity-related factors. Treatments align with those for other HCC types, but comorbidities and potential differences in immunotherapy efficacy due to T-cell dysfunction require careful consideration. Key needs include identifying molecular drivers in non-cirrhotic MASLD, developing preventive therapies, refining surveillance methods, and tailoring treatments. Trials should specifically report MASH-HCC outcomes to enable personalized therapies. Further research is needed to understand T-cell dysfunction, optimize immunotherapies, and identify predictive biomarkers.