Background: Gastrointestinal (GI) cancers are characterized by high malignancy and poor prognosis. Tumors in different locations exhibit both commonalities and differences. Although immunotherapy has made progress in some GI cancers, the specific immune-related patterns in hepatobiliary tumors have not yet been fully elucidated.
Methods: Using our developed explainable gene ontology fingerprint (XGOF) method, a GI cancer GOF was established. By integrating omics data from 20 hepatocellular carcinoma (HCC) and 15 intrahepatic cholangiocarcinoma (ICC) tissues in our clinic with public databases, immune-related patterns specifically expressed in hepatobiliary tumors were identified via RNA, protein, methylation, tumor microenvironment (TME) analysis, and experimental verification.
Results: XGOF showed that GI cancers are related to diverse immune functions, especially macrophage migration. Compared to others, hepatobiliary tumors exhibit distinct patterns of gene expression, mutation, and methylation. Seven genes (APOA1, LBP, FGA, C9, APCS, ARG1, and MBL2) were identified as immune-related genes specifically decreased in hepatobiliary cancer. The impact of APOA1 on TME, prognosis, and genomic landscape in HCC was explored in prior research. In this work, the experiment confirmed the down-regulation of six genes in cancerous tissues. Moreover, LBP promoter methylation was elevated in cholangiocarcinoma. Single-cell analysis revealed downregulated immune genes in hepatocytes of HCC and cholangiocytes of ICC, enriched in humoral immunity and complement pathways. Additionally, the macrophage migration inhibitory factor (MIF) pathway was identified as a key signal in interactions between ICC tumor cells and microenvironmental cells.
Conclusion: This study identified immune-related gene patterns in hepatobiliary cancer, contributing to the discovery of novel immunotherapy targets and tumor biomarkers for future research.
AlphaFold3 (AF3), as the latest generation of artificial intelligence model jointly developed by Google DeepMind and Isomorphic Labs, has been widely heralded in the scientific research community since its launch. With unprecedented accuracy, the AF3 model may successfully predict the structure and interactions of virtually all biomolecules, including proteins, ligands, nucleic acids, ions, etc. By accurately simulating the structural information and interactions of biomacromolecules, it has shown great potential in many aspects of structural prediction, mechanism research, drug design, protein engineering, vaccine development, and precision therapy. In order to further understand the characteristics of AF3 and accelerate its promotion, this article sets out to address the development process, working principle, and application in drugs and biomedicine, especially focusing on the intricate differences and some potential pitfalls compared to other deep learning models. We explain how a structure-prediction tool can impact many research fields, and in particular revolutionize the strategies for designing of effective next generation vaccines and chemical and biological drugs.
The emergence of artificial intelligence (AI) is transforming cardiovascular medicine. Initially, AI applications concentrated on analyz-ing single data types, such as electrocardiograms and imaging studies. However, advancements in multimodal AI have now enabled the integration of diverse data sources, facilitating a comprehensive understanding of patient health and predictive accuracy of dis-ease outcomes. In this review, we discuss current achievements in multimodal AI within cardiovascular medicine, including various combinations of different modalities, computer algorithms of data integration and fusion, and their integration into clinical workflow. As the field continues to evolve, we further propose current challenges and prospects for their future implementation.
Protein glycosylation is a critical post-translational modification that influences protein folding, localization, stability, and functional interactions by attaching glycans to specific sites. This process is crucial for biological functions of glycoproteins, and aberrant glyco-sylation can lead to genetic disorders, immune system issues, and multi-organ pathologies. Recent advancements in glycoproteomic technologies have made the study of protein glycosylation a key focus for understanding the pathogenesis of kidney diseases. This review provides a comprehensive overview of protein glycosylation mechanisms, its biological roles, molecular pathways, and signif-icant functions in renal physiology and pathology. It specifically highlights the dynamic changes and regulatory networks associated with aberrant glycosylation in kidney diseases such as immunoglobulin A nephropathy, diabetic kidney disease, autosomal domi-nant polycystic kidney disease, renal cell carcinoma, and acute kidney injury. It also evaluates the clinical applications of related technologies and biomarkers. Additionally, it discusses the challenges in developing glycosylation-targeted therapeutic strategies. Future research should focus on clarifying cell-specific glycosylation regulatory networks in the kidney, integrating glycobiology with multi-omics approaches, and improving precision diagnostics and treatment for kidney diseases.
Background: No studies have explored the genetic differences between the Chinese and other ethnic hypertrophic cardiomyopathy (HCM) populations.
Methods: This cross-sectional study included Chinese patients ( n = 593) with HCM and controls ( n= 491) who underwent whole-exome sequencing. Rare variants in 16 validated HCM genes were assessed and compared with a United Kingdom HCM cohort ( n = 1 232) and controls ( n= 344 745).
Results: Chinese HCM patients have a higher proportion of rare variants (52.8% vs 13.6%, P< 0.001) but have a similar proportion of pathogenic (P) or likely pathogenic (LP) variants compared to the UK cohort. In addition, the Chinese cohort had additional associations with the combined thin filament genes ( P= 1.29E −9) and myosin light chain genes ( P= 4.43E −3). The United Kingdom cohort was significantly associated with MYBPC3non-truncating variants ( P= 2.99E −7). By classifying variants using the tool genebe, the variants of uncertain significance were minimized to 46.8% compared to other tools (63.3% by Intervar; 91.3% by CardioClassifier). Furthermore, we report that c.3624del in MYBPC3and c.300C > G in TNNT2account for 2.9% and 1.5% of all Chinese HCM cases, respectively.
Conclusion: Our findings suggested that patients of Chinese ancestry with HCM have a higher proportion of rare variants but are less likely to be classified as P/LP variants in HCM genes than those of European origin. The variants of c.3624del in MYBPC3and c.300C > G in TNNT2were specific to Chinese individuals and provide important insights into the ethnic differences of HCM genetic architecture.