Diffuse gastric cancer (DGC) is the most common manifestation in germline CTNNA1 variant carriers, with one study estimating a 49-57% lifetime risk by age 80. Knowledge on CTNNA1-associated hereditary diffuse gastric cancer (HDGC), loss-of-function mechanisms, variant-type causality, disease spectrum and cancer risks remains scarce.

Although surgical gastrojejunostomy (SGJ) is the standard method for palliation of gastric outlet obstruction (GOO), an endoscopic method-endoscopic ultrasound-guided gastroenterostomy (EUS-GE)-has been proposed as a novel, less invasive approach.

Recent advances in high-throughput microbiome profiling have generated expansive data sets that offer unprecedented opportunities to investigate the role of microbes in human health. However, the complexity and high dimensionality of these data present significant analytical challenges that often exceed the capabilities of traditional computational methods. Artificial intelligence (AI), encompassing both classical machine learning and modern deep learning approaches, has emerged as a powerful solution to these challenges. In this review, we systematically explore AI-driven methodologies in microbiome research, including clustering algorithms, dimensionality reduction techniques, convolutional and recurrent neural networks, and emerging large language models. We assess how these approaches enable the extraction of meaningful biological patterns from complex microbial data from a multiscale perspective, facilitating insights into community dynamics, host-microbe interactions and functional genomics. Additionally, we explore the transformative impact of AI on translational applications across both academic research and real-world clinical settings, including disease diagnostics, therapeutic development and precision microbiome engineering. By critically evaluating the current capabilities and limitations of AI in this context, this review aims to chart a path forward for the integration of AI into microbiome research, ultimately accelerating innovations in personalised medicine and deepening our understanding of host-microbiome relationships.

Current microbiome-based therapeutics face two prominent issues: the limited clinical efficacy of probiotics and the significant variability in the efficacy of microbiota transplantation across different diseases. Although washed microbiota transplantation (WMT) is a new faecal microbiota transplantation, a single therapeutic agent cannot be universally effective for multiple dysbiosis-related diseases.

Cancers of the oesophagus and stomach are a major cause of morbidity and mortality. Research is crucial to improving outcomes. However, to maximise value and impact, areas of focus should be prioritised in partnership with patients.

Intrahepatic cholangiocarcinoma (iCCA) is a highly aggressive biliary tract cancer with a poor prognosis and a complex tumour microenvironment (TME) that remains poorly understood.

Acute-on-chronic liver failure (ACLF) is a life-threatening syndrome involving dysfunction of multiple immune cell types.

Interferon (IFN)-stimulated gene 15 (ISG15) is a downstream molecule of the IFN pathways central to many cellular processes. ISG15 mainly exerts its function through a post-translational modification process known as ISGylation.

Liver transplantation (LT) remains hampered by post-transplant complications. While gut microbiota dysbiosis has been linked to transplant outcomes, the role of the intrahepatic graft's native microbiota remains unexplored.

SCG101 is an autologous T-cell therapy specifically targeting hepatitis B virus (HBV) using a natural, high-affinity T-cell receptor that is stably expressed.

A detailed mapping of functional differences among intestinal regions in healthy individuals remains incomplete. Identifying regional alterations in individuals with type 2 diabetes (T2D) could enhance our understanding of disease-related intestinal changes.

Cholangiocarcinoma (CCA) is a heterogeneous neoplasm of the biliary epithelium that easily infiltrates, metastasises and recurs. Magnesium disbalance is a hallmark of CCA, with the magnesium transporter cyclin M4 (CNNM4) being a key driver of various hepatic diseases. This study aims to unravel the role of CNNM4 in the initiation and progression of CCA.

The liver is a highly multifunctional organ that can perform many metabolic and immunological functions due to a highly complex spatially organised microarchitecture that is disturbed in many liver diseases. Recent methodological advances in spatial omics technologies enable the comprehensive study of the intrahepatic proteome, transcriptome and metabolome at near single-cell and subcellular resolution. The spatial resolution adds an additional dimension to our understanding of the liver, with the potential to revolutionise our insights into the cellular and molecular mechanisms underlying liver physiology and their dysregulation in liver disease. The identification of spatial niches and interactions is empowered by advanced bioinformatics approaches facilitating spatial cellular and network-level analysis and providing novel opportunities for clinical translation. A recent example in immuno-oncology is the use of spatial architecture-based immune classifications, which can improve patient stratification. In this review, we provide an overview of the current methodology and novel spatial insights into metabolic, infectious, immune-mediated, toxic and malign liver diseases and discuss perspectives for clinical translation.