Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as the most prevalent chronic hepatopathy and a leading precursor of hepatocellular carcinoma (HCC) worldwide. Initially attributed to insulin resistance (IR)-driven metabolic imbalance, recent insights highlight a multifactorial pathogenesis involving oxidative stress (OS), chronic inflammation, and immune dysregulation. The hepatic accumulation of free fatty acids (FFAs) initiates mitochondrial dysfunction and excessive reactive oxygen species (ROS) production, culminating in lipotoxic intermediates and mitochondrial DNA damage. These damage-associated molecular patterns (DAMPs), together with gut-derived pathogen-associated molecular patterns (PAMPs), activate innate immune cells and amplify cytokine-mediated inflammation. Kupffer cell activation further exacerbates OS, while ROS-induced transcriptional pathways perpetuate inflammatory gene expression. Traditional immunity refers to the well-established dichotomy of innate and adaptive immune responses, where innate immunity provides immediate but non-specific defense, and adaptive immunity offers long-lasting, antigen-specific protection. However, a paradigm shift has occurred with the recognition of trained immunity (TI)—an adaptive-like memory response within innate immune cells that enables enhanced responses upon re-exposure to stimuli. Following non-specific antigenic stimulation, TI induces durable epigenetic and metabolic reprogramming, leading to heightened inflammatory responses and altered functional phenotypes. These rewired cells acquire the capacity to produce lipid mediators, cytokines, and matrix-modifying enzymes, reinforcing hepatic inflammation and fibrogenesis. In this context, the concept of immunometabolism has gained prominence, linking metabolic rewiring with immune dysfunction. This literature review provides an up-to-date synthesis of emerging evidence on immunometabolism and trained immunity as pathogenic drivers in MASLD. We discuss their roles in the transition from hepatic steatosis to steatohepatitis, fibrosis, and cirrhosis, and explore their contribution to the initiation and progression of MASLD-related HCC. Understanding these processes may reveal novel immunometabolic targets for therapeutic intervention.

In recent years, novel findings have progressively and promisingly supported the potential role of Artificial intelligence (AI) in transforming the management of various neoplasms, including hepatocellular carcinoma (HCC). HCC represents the most common primary liver cancer. Alarmingly, the HCC incidence is dramatically increasing worldwide due to the simultaneous “pandemic” spreading of metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD currently constitutes the leading cause of chronic hepatic damage (steatosis and steatohepatitis), fibrosis, and liver cirrhosis, configuring a scenario where an HCC onset has been reported even in the early disease stage. On the other hand, HCC represents a serious plague, significantly burdening the outcomes of chronic hepatitis B (HBV) and hepatitis C (HCV) virus-infected patients. Despite the recent progress in the management of this cancer, the overall prognosis for advanced-stage HCC patients continues to be poor, suggesting the absolute need to develop personalized healthcare strategies further. In this “cold war”, machine learning techniques and neural networks are emerging as weapons, able to identify the patterns and biomarkers that would have normally escaped human observation. Using advanced algorithms, AI can analyze large volumes of clinical data and medical images (including routinely obtained ultrasound data) with an elevated accuracy, facilitating early diagnosis, improving the performance of predictive models, and supporting the multidisciplinary (oncologist, gastroenterologist, surgeon, radiologist) team in opting for the best “tailored” individual treatment. Additionally, AI can significantly contribute to enhancing the effectiveness of metabolomics–radiomics-based models, promoting the identification of specific HCC-pathogenetic molecules as new targets for realizing novel therapeutic regimens. In the era of precision medicine, integrating AI into routine clinical practice appears as a promising frontier, opening new avenues for liver cancer research and treatment.

Background: Hepatobiliary liver cancers (HBLCs) represent the sixth most common neoplasm in the world. Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) constitute the main HBLC types, with alarming epidemiological projections. Methods: In recent decades, alterations in gut microbiota, with mutual implications on the gut–liver axis and gut–biliary axis permeability status, have been massively investigated and proposed as HBLC pathogenetic deus ex machina. Results: In the HCC setting, elevated intestinal levels of Escherichia coli and other Gram-negative bacteria have been demonstrated, resulting in a close association with increased lipopolysaccharide (LPS) serum levels and, consequently, chronic systemic inflammation. In contrast, the intestinal microbiota of HCC individuals feature reduced levels of Lactobacillus spp., Bifidobacterium spp., and Enterococcus spp. In the CC setting, evidence has revealed an increased expression of Lactobacillus spp., with enhanced levels of Actynomices spp. and Alloscardovia spp. Besides impaired strains/species representation, gut-derived metabolites, including bile acids (BAs), short-chain fatty acids (SCFAs), and oxidative-stress-derived products, configure a network severely impacting the progression of HBLC. Conclusions: In the era of Precision Medicine, the clarification of microbiota composition and functioning in HCC and CC settings can contribute to the identification of individual signatures, potentially providing novel diagnostic markers, therapeutic approaches, and prognostic/predictive tools.

Objectives: This review emphasizes a novel, multidisciplinary, coordinated approach in the management of chronic liver diseases (CLDs). Background: Chronic liver diseases represent a significant global health burden, marked by a notable shift in the prevalence patterns from virus-related to metabolic and alcohol-related entities. Malnutrition, frailty, and sarcopenia exert a substantial impact on patients with cirrhosis, affecting 75%-90% of cases and escalating as the disease progresses. The European Association for the Study of the Liver recommends a comprehensive approach to nutritional care, emphasizing the need for detailed assessments in patients with cirrhosis, using diverse tools such as computed tomography scans, bioelectrical impedance analysis, and evaluations of muscle function. Considering the prevalence of nutritional and psychological disorders in the CLD population, the treatment of these patients should be founded indispensably on a multidisciplinary approach. Methods: A systematic search was conducted of the PubMed, MEDLINE, and SCOPUS databases to identify trials investigating the health effects of nutritional and psychological assessments in patients with CLD. Results: In dealing with the treatment of patients with CLD, an exploration of the psychological domain emerges as crucial, because psychological distress, especially depression, exerts a tangible influence on patient outcomes. Thus, the engagement of psychologists and/or psychotherapists, who might use techniques such as cognitive behavioral therapy, could enhance patients' comprehension of nutritional implications in their treatment and make them more aware of their illness. Conclusion: The review emphasizes the relevance of both nutritional and psychological assessments in patients with CLD that could improve patient education on the pivotal role of nutrition in disease management. Randomized controlled trials evaluating the combined impact of nutritional and psychological support are recommended to further investigate this complex clinical landscape.

Alpha-Glutathione-S-transferase (alphaGST) is a liver enzyme whose serum levels increase with the worsening of fibrosis in alcoholic and viral chronic hepatitis. Its usefulness in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) remains unexplored. From January 2016 to December 2017, 200 patients with MASLD and 30 controls were enrolled. AlphaGST serum levels were measured. Variables related to advanced fibrosis (AF) were selected via Principal Component Analysis (PCA), and the best cut-off (BCO) was estimated using ROC analysis. Liver stiffness measurement (LSM), NAFLD fibrosis (NFS), Fibrosis-4 (FIB-4), and BMI-AST/ALT Ratio-Diabetes (BARD) scores were determined. The first acute cardiovascular events (ACE) in ACE-naïve subjects were recorded over five years. A validation cohort of 60 MASLD patients was enrolled from January 2018 to May 2019 and followed for five years. AlphaGST levels increased with fibrosis stage (p < 0.0001) in both cohorts, showing high accuracy in predicting AF (TrC: AUC 0.89, VlC: AUC 0.89). PCA-selected variables were HbA1c, HDL, and alphaGST, forming the “FLAME” model. FLAME showed superior predictive performance for AF and ACEs compared to other models and scores. FLAME represents a novel tool that accurately predicts AF and ACEs in MASLD.

Various non-invasive tools (NITs) predicting first hepatic decompensation (HD) in advanced chronic liver disease (ACLD) enclose platelet (PLT) count. A relevant proportion of metabolic dysfunction-associated steatotic liver disease (MASLD)-ACLD patients do not show splenomegaly- and hypersplenism-related thrombocytopenia. We aimed to evaluate the performance of NITs in predicting HD according to ultrasound-assessed spleen size. In this observational study, 148 splenic and 27 asplenic (ASP) MASLD-compensated advanced chronic liver disease (cACLD) patients were enrolled. Ultrasound artificial intelligence-based tools distinguished splenomegaly-affected patients (SAPs) and normal-spleen patients (NSPs). Albumin-Bilirubin score (ALBI) and PLT count-based NITs (PLNs) (Fbrosis-4 [FIB-4], ALBI-FIB-4, red cell distribution width-to-PLT ratio [RPR], liver stiffness measurement [LSM]-to-platelet ratio [LSM/PLTr], and ANTICIPATE ± non-alcoholic steatohepatitis [NASH]) were determined. Over 3 years, the first HD was recorded. Limitedly to SAP, spleen area inversely correlated with PLT (relationship [R]: -0.981; P < 0.0001), confirming the role of splenomegaly-related hypersplenism in conditioning thrombocytopenia. HD occurred similarly in SAPs (20.48%), NSPs (21.15%), and ASP patients (25%) (P: 0.198). In NSP, PLNs showed a reduced influence on HD (FIB-4 [P: 0.03], ALBI-FIB-4 [P: 0.001], RPR [P: 0.002], LSM/PLTr [P: 0.01], and ANTICIPATE ± NASH [P: 0.001]) compared to SAP. In NSP, the spleen area was inversely associated (adjusted sub-distribution hazard ratio: 0.870) and more significantly (P < 0.0001) impacted HD. Consistently, unlike SAPs, in NSPs and ASP patients, PLNs showed poor performance, and exclusively ALBI maintained a good accuracy (NSP: area under the curve [AUC]: 0.651, P: 0.04; ASP patients: AUC: 0.625, P: 0.03) in predicting 3-year HD. Ultrasound-assessed spleen size affects the predictive performance of the PLNs in MASLD-cACLD patients.