Abstract Precision medicine has evolved through distinct phases, from the origins of the Human Genome Project to mutation‐based targeted therapies. This editorial posits that ‘stereological cell biomedicine’ could be a new approach promoting the development of the next generation of precision medicine. This emerging discipline transitions the focus from genomic data to the multi‐dimensional and spatiotemporal complexity of single cells. Driven by advances in Stereo single‐cell multi‐omics (Stereo Cell‐seq), spatial transcriptomics (Stereo‐seq) and single‐cell surfaceomics (sc‐surfaceome), this approach aims to capture the stereologically dynamic interactions between organelles within a cell and between cells in the tissue. We argue that understanding the spatiotemporal location of molecules, particularly protein interactions at organelle interfaces and on the cell surface, is as critical as their abundance for defining cellular function in health and disease. Integrating these high‐resolution measurements with artificial intelligence and computational modelling will bridge the gap between advanced omics and pathology. Initiatives such as the newly established European Stereo Cell Center signal a global shift towards this new paradigm, which promises to unlock novel diagnostic biomarkers and therapeutic targets for truly multi‐factorial and dynamic precision medicine.

Abstract Gastric cancer continues to be a major threat to global health, accounting for a significant number of cancer deaths annually, with precancerous lesions representing critical intervention windows to halt malignant progression. Current risk stratification for malignant transformation in gastric precancerous lesions relies heavily on invasive endoscopic and histopathological assessments, which lack precision in quantifying individual transformation risk. Recent research points to the significance of epigenetic dysregulation in driving the evolution of gastric precancerous lesions: DNA methylation, 5‐hydroxymethylcytosine, non‐coding RNAs, RNA editing and modifications. Integrating multi‐omics epigenetic signatures offers a transformative approach to refining risk stratification, guiding personalised surveillance intervals and therapeutic interventions. Future efforts should prioritise large‐scale clinical validation of epigenetic biomarkers, standardisation of detection technologies, and development of cost‐effective, non‐invasive platforms to bridge mechanistic insights into precision prevention strategies for gastric carcinogenesis.

Abstract Background The forkhead box family transcription factor FOXC2 has emerged as an important oncogene in cancers of epithelial origin, where it has been associated with several hallmarks of cancer progression. In this study, we provide clinical evidence of a role for FOXC2 in the progression of melanoma. Using bioinformatics‐based approaches, we aimed to gain insight into the oncogenic functions of this transcription factor in this cancer of non‐epithelial origin. Methods We investigated multiple RNA‐sequencing datasets from biopsies of independent melanoma patient cohorts to identify genes whose expression correlated with that of the FOXC2 gene. These data were analysed using topology‐based Impact Pathway and high‐specificity pruning Gene Ontology approaches. Results We identified novel biologic pathways and processes significantly impacted by FOXC2 ‐correlated genes, including those related to Rap1 signalling and collagen fibril organisation. For these novel pathways/processes, as well as others previously linked with the activity of FOXC2, our study also revealed specific FOXC2 ‐correlated genes associated with these biological phenomena. Conclusions Together, our data offer novel insights into the oncogenic functions of FOXC2 in melanoma and other cancers.

Abstract Background Ulcerative colitis (UC) is a chronic inflammatory bowel disease posing a growing global health burden. Current therapies face safety concerns and fail to induce mucosal healing. Mesenchymal stem cell (MSC) therapy has emerged as a promising alternative due to its immunomodulatory and regenerative properties. However, its integration into precision medicine paradigms remains challenging. Methods This review examines the key challenges hindering the clinical translation of MSC therapy for UC and proposes innovative strategies to optimize this cell‐based modality. The analysis is based on a systematic evaluation of the current literature, encompassing the biological properties of MSCs, preclinical studies, clinical trial data, biomanufacturing processes, and emerging technological platforms. Results Our analysis identifies key barriers to precision MSC therapy across biomanufacturing, clinical translation, and mechanistic understanding. To address these challenges, we propose a strategic framework that progresses from challenge identification to developing biological strategies for enhancing MSC potency and homing, streamlining therapeutic workflows, and integrating intelligent systems. Conclusion As a viable therapy for UC, MSC therapy faces significant challenges within the precision medicine paradigm. The convergence of novel approaches with artificial intelligence (AI) is paving the way for precision frameworks, and their rigorous validation through clinical trials will be crucial to delivering reliable, patient‐specific therapies. Highlights Overviews key challenges and innovative strategies in MSC therapy for UC within a precision medicine framework. Examines barriers in MSC biomanufacturing, clinical translation, and host microenvironment adaptation. Highlights emerging approaches, encompassing pharmacological preconditioning, 3D culture, cellular engineering, and advanced delivery platforms to enhance MSC potency and homing. Proposes an integrated roadmap combining standardized workflows, combinatorial regimens, and AI for predictive patient stratification and individualized therapy.

Abstract Objective We aimed to determine if omega‐3 fatty acid (FA) supplementation significantly reduces cardiovascular (CV) events in patients with established CV disease or at high CV risk. Methods We conducted a comprehensive literature search on PubMed, Embase and Cochrane CENTRAL. The results of our analyses were presented as risk ratios (RRs) with 95% confidence intervals (CIs) and pooled using a random effects model. Meta‐regression bubble plots were generated to visualise the results of the analysis, while the detailed results were tabulated. A p ‐value less than‐.05 was considered significant in all cases. Results A total of 42 studies (176 253 participants) were included in our analysis. The pooled analysis demonstrates that omega‐3 FA are associated with a significant reduction in CV mortality ( p =‐.02), CV disease ( p =‐.03), coronary heart disease (CHD) ( p =‐.007), myocardial infarction (MI) ( p =‐.008), fatal MI ( p =‐.0004) and revascularisation ( p =‐.003), and a significant increase in atrial fibrillation ( p =‐.01), and gastrointestinal (GI) adverse events ( p =‐.02). Subgroup analysis demonstrated a significant improvement with EPA monotherapy compared to EPA+DHA combination therapy in the risk of CV mortality ( p = 0.01), CVD events ( p <‐.00001), MACE ( p < .00001), CHD ( p < .00001), MI ( p <‐.00001), fatal MI ( p =‐.004) and revascularisation ( p <‐.0001). EPA monotherapy was associated with a significant increase in the risk of atrial fibrillation ( p =‐.01). Regression analysis demonstrates a dose–response relationship between omega‐3 FA (EPA or EPA+DHA) and CVD events ( p =‐.001), CHD ( p =‐.035), revascularisation ( p =‐.035) and ischemic stroke ( p =‐.003). Conclusion Our study demonstrated a significant reduction in the risk of cardiovascular outcomes with omega‐3 FA administration.

Abstract Purpose : Cancer progression is a non‐linear, multiscale process driven by the interaction of molecular, cellular and tissue systems, which ultimately leads to tumour growth, invasion and metastasis. Understanding and predicting these dynamics is essential for improving diagnostics and personalising therapy. Mathematical and computational modelling has become central to this effort, enabling in silico simulations of progression and treatment response. Methods : This survey outlines computational frameworks for modelling cancer across biological scales, incorporating mathematical formalisms and algorithmic paradigms. These frameworks include: ordinary and partial differential equation models of growth, angiogenesis and invasion; agent‐based and hybrid multiscale approaches that capture heterogeneity and microenvironmental feedback; emerging digital twin platforms that integrate mechanistic and data‐driven modelling for patient‐specific predictions. Recent advances in artificial intelligence (AI), such as graph neural networks, transformer‐based architectures and multimodal data fusion, enhance these frameworks by combining interpretability with predictive power through mechanistic learning pipelines. Results : Based on these advances, the survey presents a conceptual roadmap for multiscale cancer modelling. This roadmap offers a structured workflow that begins with defining the biological question and modelling scale and continues through selecting appropriate paradigms, calibrating and validating models with experimental or clinical data and integrating multimodal information into individualised simulations. The long‐term goal is to develop digital twins of cancer that can personalise and optimise therapy and ultimately guide clinical decisions. These models will capture processes ranging from single‐cell dynamics and cancer progression to angiogenesis, tumour evolution and treatment response. To achieve this vision, we need advances in parameter calibration, multimodal dataset integration and cross‐scale model validation. Conclusion : Computational oncology bridges the gap between biological mechanisms and predictive modelling, offering a framework for reproducible, data‐driven precision medicine. The convergence of mechanistic modelling, AI‐assisted inference and digital twin technologies establishes a continuum for translating research into real‐time, patient‐specific decision support in oncology. Highlights Provides an integrative overview of computational frameworks for modelling cancer across biological scales. Illustrates the characteristics, advantages, and limitations of ODE/PDE, agent‐based, hybrid multiscale, and integrative translational models. Highlights how these paradigms collectively bridge mechanistic understanding with clinical translation. Presents a conceptual roadmap linking modelling methodologies to precision oncology applications.