Hepatocellular carcinoma (HCC) is a highly lethal cancer in which epigenetic dysregulation plays a key role in tumor development and progression. Among epigenetic mechanisms, histone methylation regulates chromatin structure and gene transcription and influences multiple aspects of HCC biology, including tumor proliferation, metastasis, immune modulation, cancer stemness, and therapeutic resistance. In this review, we summarize recent advances in understanding the roles of histone methyltransferases in HCC, focusing on their involvement in tumor proliferation, metastasis, immune regulation, drug resistance, and cancer stem cell maintenance. We also discuss their clinical relevance as potential diagnostic and prognostic biomarkers and highlight emerging therapeutic strategies targeting histone methylation pathways. Collectively, these findings suggest that histone methyltransferases represent promising targets for developing novel epigenetic-based diagnostic and therapeutic approaches for HCC.

Liver cancer is among the most prevalent and life-threatening cancers worldwide, with hepatocellular carcinoma and liver metastases contributing significantly to cancer related mortality. Accurate segmentation of liver tumors in abdominal computed tomography and magnetic resonance imaging is crucial for tumor prediction, radiation therapy, and treatment monitoring. In recent years, medical image segmentation has advanced rapidly due to deep learning (DL), particularly convolutional neural networks (CNN) and transformer-based architectures. This paper provides a comprehensive review of DL based liver tumor segmentation methods in abdominal imaging. We examine the evolution from traditional encoder decoder frameworks such as U-Net and V-Net to residual, attention based, and hybrid CNN transformer models, including vision transformers. The review also analyzes performance across publicly available benchmarks, including the Medical Segmentation Decathlon, 3D-IRCADb, CHAOS, and the Liver Tumor Segmentation Challenge, using evaluation metrics such as Dice Similarity Coefficient, Hausdorff Distance, and Intersection over Union. Furthermore, we discuss key challenges including tumor heterogeneity, class imbalance, limited annotated data, and domain adaptation across imaging protocols. Emerging directions such as foundation models, self-supervised learning, federated learning, and clinically deployable AI segmentation systems are also highlighted.

Immune checkpoint inhibitor (ICI)-based combination therapy has transformed the treatment of advanced hepatocellular carcinoma (HCC), yet immune-related adverse events (irAEs) remain a serious and potentially fatal complication. We report two cases of severe irAEs following ICIbased therapy for HCC. In Case 1, a man with multimetastatic HCC developed immune-mediated colitis with hemodynamic collapse and multi-organ failure after the second cycle of the STRIDE regimen yet achieved a near-complete oncologic response. In Case 2, a patient who had tolerated 17 cycles of adjuvant atezolizumab (ate) plus bevacizumab (bev) without irAE developed fatal immune-mediated fulminant hepatitis after a single dose of ate+bev reinitiated as first-line therapy for unresectable HCC following recurrence and TACE failure. These cases highlight the unpredictability of irAEs—even in previously tolerant patients—and underscore the importance of early recognition and prompt management.

Hepatocellular carcinoma (HCC) remains the leading cause of cancer-related mortality worldwide, largely because of its late detection and high recurrence rates. Conventional biomarkers such as alpha-fetoprotein (AFP) are unable to detect early-stage diseases with sufficient accuracy. Exosomal microRNAs (miRNAs) are small non-coding RNAs, encapsulated within extracellular vesicles, that have emerged as highly sensitive and specific non-invasive biomarkers with revolutionary potentials for improving HCC diagnosis and prognosis prediction. Several studies have demonstrated that circulating exosomal miRNAs outperform AFP detection in differentiating early-stage HCC from chronic liver disease and in predicting metastasis, recurrence, and patient survival. Furthermore, multi-miRNA panels and AI-driven predictive models integrating exosomal miRNA signatures with clinical parameters enhance the diagnostic accuracy and enable personalized risk stratification. Despite promising results, clinical implementation has been challenged by assay standardization, interpatient variability, and the need for large-scale prospective validation. Future research should include developing robust, high-throughput exosomal miRNA detection platforms, incorporating machine learning for optimized biomarker selection, and integrating exosomal miRNAs with other liquid biopsy approaches for comprehensive disease monitoring. In summary, exosomal miRNAs represent a powerful tool for revolutionizing the early detection and tailored management of HCC, ultimately improving patient outcomes through timely and precise interventions.

Body composition analysis provides essential evidence for evaluating patient health, predicting disease risk, and guiding personalized treatment. Abdominal computed tomography (CT) enables high-resolution visualization of skeletal muscle, adipose tissue, and major organs, making it well-suited for constructing 3D anatomical distribution maps. However, current clinical practice largely depends on manual analysis, which is limited in scope, efficiency, and standardization. This review presents recent deep learning techniques, focusing on methods developed by our group, that aim to automate the generation of anatomical distribution maps from abdominal CT. The key topics include: (1) automated localization and identification of the entire spine beyond single-level detection at the third lumbar vertebra (L3); (2) cross-domain consistency learning to overcome limited annotations in medical imaging; (3) multi-organ segmentation that incorporates inter-slice structural continuity and anatomical relationships; and (4) a comparative study of continual learning and multi-dataset learning for building generalizable models. These approaches enable rapid, standardized generation of anatomical distribution maps across large-scale datasets and diverse patient cohorts, providing a robust technical foundation for future precision medicine applications. In particular, these methods are expected to provide for early cancer detection, pediatric CT analysis, and clinical correlation studies linking anatomical distribution maps with outcomes such as cancer prognosis.

Acute hepatitis A is an inflammation of the liver caused by the hepatitis A virus, typically resulting in mild to severe illness from which most individuals recover fully without complications. However, in rare cases, it may lead to long-term sequelae. Secondary hemochromatosis refers to iron overload resulting from various causes, including chronic liver disease, repeated blood transfusions, and systemic inflammation. Here, we report a rare case of secondary hemochromatosis that developed following acute hepatitis A.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and therapeutic cancer vaccines have emerged as a promising immunotherapeutic strategy. These vaccines target tumor-associated antigens such as glypican-3, alpha-fetoprotein, melanoma-associate antigen-1, heat shock protein 70, glutamine synthetase, and TMEM176A/B, which are abnormally expressed in HCC cells and serve as both diagnostic markers and therapeutic targets. Various vaccine platforms—including peptide-based, dendritic cells-based, viral vector-based, and genetic vaccines (DNA/mRNA)—are under investigation for their ability to elicit antigen-specific cytotoxic T cell responses and establish long-term immune memory. Despite promising preclinical and early clinical results, challenges such as the immunosuppressive tumor microenvironment, antigen heterogeneity, and immune evasion mechanisms limit their efficacy. Future strategies focus on combination therapies with immune checkpoint inhibitors, personalized neoantigen vaccines, and advanced delivery technologies. These approaches aim to enhance immunogenicity and clinical outcomes, positioning therapeutic cancer vaccines as a key component of precision oncology in HCC.