Hepatocellular carcinoma (HCC) remains one of the most fatal cancers, primarily due to late diagnosis and the lack of effective early biomarkers. Recent advances in multi-omics and liquid biopsy technologies hold promise for improving early detection, prognostication, and monitoring of HCC. Understanding the immune landscape of HCC through genetic and epigenetic signatures is essential for identifying therapeutic targets and improving immunotherapy outcomes. This review aims to present current findings on immune-related biomarkers, multi-omics strategies, and biomarker validation in HCC. It also aims to evaluate the role of liquid biopsy and gene signatures in predicting treatment responses, with a specific focus on their applications in immunotherapy. The goal is to provide a comprehensive framework for integrating these emerging tools into clinical practice. The integration of multi-omics approaches has led to the identification of robust gene signatures that predict HCC prognosis and response to immune checkpoint inhibitors. Liquid biopsy technologies, including circulating tumor DNA, provide non-invasive alternatives for monitoring tumor evolution and therapeutic responses. Despite promising results, challenges remain in clinical validation, particularly in cross-platform reproducibility and the interpretation of complex multi-omics data. While genetic biomarkers are rapidly advancing, their clinical application in personalized medicine remains hindered by technical and ethical challenges, such as data privacy, informed consent, and method standardization. The integration of multi-omics data and liquid biopsies offers a promising path toward real-time, personalized treatment and the development of universal prognostic signatures for HCC. However, successful clinical adoption depends on cross-disciplinary collaboration to standardize data protocols and overcome challenges regarding accuracy, reproducibility, and patient privacy.

Cannabinoid hyperemesis syndrome is an underrecognized cause of recurrent vomiting, weight loss, and abdominal pain in adolescents, often overlooked due to its nonspecific presentation and overlap with other gastrointestinal conditions. This case report highlights a 13-year-old female who presented with significant weight loss and postprandial bilious vomiting initially attributed to superior mesenteric artery syndrome. Persistent symptoms, despite surgical removal of an incidental ovarian dermoid cyst, prompted reevaluation after nondiagnostic imaging and lack of improvement. Further history obtained on hospital day 12 revealed daily cannabis use since age 10, with reported cessation approximately six weeks prior to admission and probable resumed use approximately two weeks prior to presentation, confirmed by a positive urine toxicology screen for tetrahydrocannabinol consistent with chronic heavy use. Following supportive care and counseling on cannabis cessation, her acute symptoms resolved and she was discharged. She subsequently experienced a symptomatic relapse with resumed cannabis use requiring readmission two months later, but achieved sustained clinical remission at approximately six months following definitive cessation. This case illustrates how incomplete social histories and incidental findings can delay the identification of cannabinoid hyperemesis syndrome and lead to unnecessary procedures, and emphasizes that long-term symptom resolution requires ongoing cannabis abstinence. Early use of validated structured substance use screening tools (CRAFFT, BSTAD, S2BI), with urine toxicology applied as a confirmatory adjunct when the history is unreliable or symptoms remain unexplained, can facilitate timely recognition of cannabinoid hyperemesis syndrome in adolescents with persistent vomiting and abdominal pain, reduce hospital length of stay, and improve outcomes.

Unlike the traditional staging treatment of tumors, the core of “Green Tumor Treatment” is to divide the treatment of tumors into three stages: Hegemony (directly targeting the cancer focus), Kingship (supporting the body’s vital energy and eliminating pathogenic factors), and Imperialism (improving the internal environment), based on the urgency of the tumor and the patient’s physical condition. This approach guides the clinical treatment of tumors. Its treatment system incorporates all minimally invasive and low-damage treatment methods, combining internal and external treatments, traditional Chinese medicine and Western medicine, as well as local and systemic treatments. It aims to maximize treatment outcomes while ensuring the patient’s quality of life, which is highly consistent with the treatment goals for primary liver cancer. This review aims to explore the integrated Traditional Chinese and Western medicine treatment model for primary liver cancer under the guidance of the Green Tumor Treatment concept.

Early risk stratification of severe acute liver injury (SLI) that may progress to acute liver failure (ALF), is vital for timely intervention, but no universal prognostic assessment tool covers both conditions. This study aimed to develop a simplified prognostic model for early risk assessment in SLI/ALF patients.

A retrospective cohort study consecutively enrolled SLI patients (including those progressing to ALF) from July 1, 2020 to May 31, 2025. Baseline clinical and laboratory data on admission were collected, with 90-day transplant-free survival as the primary outcome. Independent prognostic factors were screened via Cox regression to build a simplified scoring model, whose performance was compared with the Model for End-Stage Liver Disease (MELD), King’s College Criteria (KCC), and the Acute Liver Failure Study Group Prognostic Index (ALFSG-PI).

Of 302 patients, 190 (62.9%) achieved 90-day transplant-free survival. Multivariate Cox regression identified international normalized ratio (hazard ratio [HR]: 1.118, 95% confidence interval [CI]: 1.050–1.191), platelet count (HR: 0.995, 95% CI: 0.993–0.998), and hepatic encephalopathy grade ≥ 2 (HR: 5.187, 95% CI: 3.403–7.907) as independent predictors, forming the HIP (derived from the above-mentioned three predictors) model. It showed good discrimination (area under the receiver operating characteristic curve [AUC]: 0.82), outperforming MELD (AUC: 0.76, P = 0.019) and KCC (AUC: 0.72, P = 0.002), and performing comparably to ALFSG-PI (AUC: 0.80, P = 0.429). The model also performed robustly in ALF subgroups defined by the American College of Gastroenterology and the 2024 Chinese Medical Association guidelines (AUCs: 0.80 and 0.76, respectively) and achieved an AUC of 0.85 in the validation set.

The HIP model is a simple and effective tool for prognostic risk stratification in SLI/ALF patients, suitable for emergency and primary care to facilitate timely intervention.

Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease. Its molecular etiology remains poorly defined, hindering the development of mechanism-based diagnostics and therapies. Therefore, this study aimed to identify key molecular drivers and causal biomarkers of PSC by integrating transcriptomics, machine learning, and genetic causal inference.

We deployed an integrated computational framework combining transcriptomics, network biology, machine learning, and genetic causal inference. Peripheral blood transcriptomes from PSC patients and controls were analyzed to identify disease-associated modules. Candidate genes were refined via protein-protein interaction networks and a multi-algorithm machine learning screen. Causal inference was performed using two-sample Mendelian randomization, integrating plasma protein quantitative trait loci with PSC genome-wide association study summary statistics.

Transcriptomic analysis revealed a PSC-associated module enriched in ribosome biogenesis and protein homeostasis pathways. A machine learning-optimized nine-gene signature (including PTMA, SUMO1, Shwachman-Bodian-Diamond syndrome (SBDS), RPL7, EIF1AX, ANP32A, PCNA, FAM98A, and MPHOSPH6) achieved high diagnostic accuracy (mean AUC = 0.908) and was consistently downregulated in PSC. This signature was linked to a remodeled immune microenvironment characterized by myeloid skewing and specific transcriptional-immune covariation patterns. Mendelian randomization identified SBDS as a putatively causal protective factor, where genetically instrumented higher plasma SBDS protein levels were robustly associated with a lower PSC risk (IVW OR = 0.525, 95% CI: 0.356–0.773, P = 0.001). Sensitivity analyses supported the validity of the Mendelian randomization assumptions.

Our study establishes disrupted ribosome homeostasis as a causal pathway in PSC and nominates plasma SBDS as a high-confidence diagnostic biomarker and therapeutic target. The integrative framework provides a generalizable strategy for discovering causal biomarkers in complex diseases.

Artificial intelligence (AI) is increasingly reshaping diagnostic pathology, with breast pathology representing one of the most advanced and clinically impactful areas of adoption. Despite rapid progress, many practicing pathologists remain unfamiliar with core AI concepts and their practical implications. This review provides a concise and accessible overview of AI in breast pathology, focusing on foundational principles, current clinical applications, and future directions.

Pertinent literature was reviewed. Personal experiences were also summarized and incorporated.

Key AI concepts, including algorithms, models, architectures, machine learning, deep learning, neural networks, and multimodal and foundational models, are introduced to establish a common framework. Important distinctions among generative, black-box, and explainable AI are highlighted, emphasizing the need for transparency and interpretability in clinical settings. The evolution of AI in breast pathology is reviewed, from early rule-based computer-assisted diagnostic systems to modern deep learning approaches leveraging large-scale whole-slide imaging datasets. Current applications span multiple domains, including detection of lymph node metastases, Nottingham grading, classification of benign and malignant lesions, and automated quantification of critical biomarkers. AI-based approaches to prognosis, risk stratification, prediction of treatment response, and analysis of the tumor microenvironment are also discussed. Finally, the review addresses challenges associated with real-world implementation, including data quality, bias, regulatory considerations, cost, infrastructure, and workflow integration.

As AI continues to evolve toward large-scale, multimodal, and explainable models, it is expected to function as an augmentative tool rather than a replacement for pathologists, supporting diagnostic accuracy, standardization, and personalized management in breast cancer care.