Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insi...Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insights needed for this purpose.Methods:To comprehensively capture breast cancer heterogeneity,we employed integrative clustering that incorporates six molecular features from 670 breast cancer samples.Ten distinct clustering algorithms were combined to ensure robust subtype identification,and the identified subtypes were validated in four independent datasets.Subsequently,we constructed a survival support vector machine prognostic model based on key molecular features to enhance survival prediction and clinical applicability.Results:Five novel subtypes were identified:consensus subtypes 1–5(CS1–CS5).CS2 was an aggressive subtype with elevated TP53 mutation rates,high tumor mutational burden,and strong sensitivity to YM-155 and ispinesib.Conversely,CS5 exhibited stable genomics with enhanced nucleotide excision repair and favorable prognoses.CS2 and CS4 showed enriched immune checkpoint expression,indicating potential immunotherapy responsiveness,while CS1 and CS5 exhibited immune-cold profiles.The survival support vector machine model effectively predicted survival outcomes across independent datasets.Conclusions:The refined breast cancer classification framework developed in this research uncovers new insights into molecular heterogeneity,enhances risk stratification,and enables the identification of promising therapeutic targets.The potential of this framework to optimize personalized treatment strategies warrants further clinical validation.展开更多
Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through...Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.展开更多
Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi...Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi-omics strategy to characterize the changes and interactions among metabolomic(MB),transcriptomic(TX),and proteomic(PT)profiles in mechanically stressed tea leaves.Mechanical stress initially activated damage-associated molecular patterns(DAMPs),including Ca^(2+)signaling,jasmonic acid signaling,and glutathione metabolism pathways.These processes subsequently induced quality-related metabolic pathways(QRMPs),particularly α-linolenic acid and phenylalanine metabolism.Upregulated expression of LOX,ADH1,and PAR genes,together with the increased abundance of their encoded proteins,respectively promoted the accumulation of jasmine lactone,benzyl alcohol,and 2-phenylethanol.These findings indicate that mechanical stress influences the metabolite biosynthesis in tea leaves through coordinated molecular responses.This study provides new insights into the molecular mechanisms underlying tea leaf responses to mechanical stress and a foundation for future investigations into how early molecular events may contribute to post-harvest metabolic changes during oolong tea processing.展开更多
Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression;however, their contribution to the functional classification and personalized treatment of gastric cancer...Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression;however, their contribution to the functional classification and personalized treatment of gastric cancerremains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatmentstrategies for patients with gastric cancer. Methods: Single-cell and bulk transcriptomic analyses were integrated tocharacterize gastric cancer fibroblasts. “Seurat”, “Slingshot”, and “CellChat” were used for dimensionality reduction,trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast moduleswere identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct aprognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features.The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays. Results: Eight fibroblast subpopulations associated with gastric cancer metastasisexhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated thatmetastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed markedimmunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-relatedpathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressedgastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis. Conclusion: This studycharacterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis andestablished a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strongpredictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, thefindings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.展开更多
Because the pathogenesis of Alzheimer’s disease is multifactorial and complex,integrated multi-level omics analysis is essential to comprehensively elucidate its molecular alterations.We therefore utilized the well-e...Because the pathogenesis of Alzheimer’s disease is multifactorial and complex,integrated multi-level omics analysis is essential to comprehensively elucidate its molecular alterations.We therefore utilized the well-established amyloid precursor protein/presenilin 1 mouse model to carry out an integrated multi-omics study using transcriptomic,proteomic,N^(6)-methyladenosine epitranscriptomic,and phosphoproteomic analyses.The results revealed substantial molecular alterations across multiple biological dimensions and the alteration in the expression of several key genes,such as GFAP,APP,and RTN4,in a mouse model of Alzheimer’s disease.The pronounced elevation of RTN4 in reactive astrocytes is indicative of its involvement in Alzheimer’s disease pathogenesis.Furthermore,we identified dysregulation of pathways related to endocytosis,highlighting the critical role of this process in disease progression.Our findings underscore the significant impact of post-transcriptional(N^(6)-methyladenosine methylation)and post-translational(phosphorylation)protein modifications,which have been underrepresented in Alzheimer’s disease research.The significant contribution made by this study is the integrated,multi-level omics analysis that we carried out to investigate the complex biological changes that occur in Alzheimer’s disease.Our findings provide novel insights into Alzheimer’s disease pathogenesis and suggest potential therapeutic targets,such as RTN4.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid ...Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid and nutrient components between the adipose tissue of Bamei pigs and commercial pigs are still unclear.The study employed UPLC-MS/MS to quantify the composition of lipids and metabolites in the backfat of both Bamei and Large White pigs.A total of 428 lipids and 193 metabolites were significantly different between the 2 groups.Specifically,Bamei pig backfat exhibited altered levels of various lipids and metabolites that may potentially contribute to nutritional and flavor differences,including unsaturated triglycerides,free fatty acids,medium-chain triglycerides,essential amino acids,vitamins and antioxidants,while maintaining reduced cholesterol levels.Furthermore,we delved into the molecular mechanisms underlying these nutritional differences by analyzing significantly different 431 m RNAs and 865 proteins and integrating the regulatory network of protein-metabolite-lipid pathway.Importantly,in the pyruvate metabolic pathway of Bamei pigs,the bioprocess of lactate production was inhibited but the acetyl-Co A production was activated,suggesting the possibility that energy allocation favors the biogenesis of lipid precursors.These findings may contribute to guiding industrial food producers in enhancing the quality of lard at the genetic and molecular levels.展开更多
Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including no...Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including non-invasive biomarkers have been explored for the diagnosis of viral hepatitis.With the fast development of multi-omics technology,non-invasive biomarkers can be detected from blood,saliva,urine,stool,and other body fluids.The advantages of non-invasive biomarkers are:1)non-invasive;2)convenient to test and 3)repeatable.The application of non-invasive biomarkers significantly improves the diagnostic accuracy of viral hepatitis.The non-invasive biomarkers can be sugars,proteins,nucleic acids,and even microorganisms.In this review,we summarized recent advances in identifying non-invasive biomarkers using multi-omics technology and discussed their potential diagnostic values for viral hepatitis.展开更多
Lactic acid bacteria and the fermentation environment interact to form an intertwined system.Lactic acid bacteria are constantly evolving to adapt to different fermentation environments,causing changes in their physio...Lactic acid bacteria and the fermentation environment interact to form an intertwined system.Lactic acid bacteria are constantly evolving to adapt to different fermentation environments,causing changes in their physiological processes.To achieve a targeted improvement of their adaptability to various environments,a detail analysis of their evolutionary physiological processes is required.While several studies have been carried out in the past by using single-omics techniques to investigate their response to environmental stress,most researchers are now using a multi-omics approach to explore more detail in the biological regulatory networks and molecular mechanisms of lactic acid bacteria in response to environmental stress,thereby overcoming the limitations of single-omics analysis.In this review,we describe the various single-omics approaches that have been used to study environmental stress in lactic acid bacteria,present the advantages of various multi-omics combined analysis approaches,and discuss the potential and practicality of applying emerging single-cell transcriptomics and single-cell metabolomics techniques to the molecular mechanism study of microbes response to environmental stress.Multi-omics approaches enable the accurate identification of complex microbial physiological processes in different environments,allow people to comprehensively reveal the molecular mechanisms of microbes response to stress from different perspectives.Single-cell omics techniques,analyze the targeted regulation of microbial functions in a multi-dimensional space,provides a new perspective on understanding microbes responses environment stress.展开更多
Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategie...Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategies often fall short in addressing the complex,multifactorial nature of DM.This review ex-plores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches.We consolidated genomic,transcriptomic,proteomic,metabolomic,and microbiomic data from major databases and peer-reviewed publications(2015-2025),with an emphasis on clinical relevance.Multi-omics investigations have identified convergent mole-cular networks underlyingβ-cell dysfunction,insulin resistance,and diabetic complications.The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis.Novel biomarkers facilitate early detection of DM and its complications,while single-cell multi-omics and machine learning further refine risk stratification.By dissecting DM heterogeneity more precisely,multi-omics integration enables targeted in-terventions and preventive strategies.Future efforts should focus on data har-monization,ethical considerations,and real-world validation to fully leverage multi-omics in addressing the global DM burden.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering va...Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering valuable insights into tumor biology and potential treatment strategies.Methods:We conducted a comprehensive multi-omics analysis of 132 patients with American Joint Committee on Cancer(AJCC)stage III TNBC,comprising 36 long-term survivors(RFS≥8 years),62 moderate-term survivors(RFS:3-8 years),and 34 short-term survivors(RFS<3 years).Analyses investigated clinicopathological factors,whole-exome sequencing,germline mutations,copy number alterations(CNAs),RNA sequences,and metabolomic profiles.Results:Long-term survivors exhibited fewer metastatic regional lymph nodes,along with tumors showing reduced stromal fibrosis and lower Ki67 index.Molecularly,these tumors exhibited multiple alterations in genes related to homologous recombination repair,with higher frequencies of germline mutations and somatic CNAs.Additionally,tumors from long-term survivors demonstrated significant downregulation of the RTK-RAS signaling pathway.Metabolomic profiling revealed decreased levels of lipids and carbohydrate,particularly those involved in glycerophospholipid,fructose,and mannose metabolism,in long-term survival group.Multivariate Cox analysis identified fibrosis[hazard ratio(HR):12.70,95%confidence interval(95%CI):2.19-73.54,P=0.005]and RAC1copy number loss/deletion(HR:0.22,95%CI:0.06-0.83,P=0.026)as independent predictors of RFS.Higher fructose/mannose metabolism was associated with worse overall survival(HR:1.30,95%CI:1.01-1.68,P=0.045).Our findings emphasize the association between biological determinants and prolonged survival in patients with TNBC.Conclusions:Our study systematically identified the key molecular and metabolic features associated with prolonged survival in AJCC stage III TNBC,suggesting potential therapeutic targets to improve patient outcomes.展开更多
The advent of multi-omics approaches has revolutionized the field of oncology by enabling a comprehensive understanding of cancer biology through the integration of diverse biological data.This review aims to explore ...The advent of multi-omics approaches has revolutionized the field of oncology by enabling a comprehensive understanding of cancer biology through the integration of diverse biological data.This review aims to explore the synergy between three key omics domains:radiomics,genoproteomics,and pathomics.Radiomics involves extracting high-dimensional data from medical images,providing valuable insights into tumor heterogeneity and treatment response.Genoproteomics,encompassing both genomic and proteomic analyses,delves into the molecular mechanisms driving cancer progression and therapeutic resistance.Pathomics leverages advanced digital pathology techniques to quantitatively analyze tissue architecture and cellular morphology.We provide an in-depth overview of the methodologies and tools employed in each omics field,highlighting their specific applications in oncology,including cancer diagnosis,biomarker discovery,and prediction of treatment outcomes.Furthermore,we discuss the integration of multi-omics data,addressing the challenges and innovative solutions for harmonizing these complex datasets.Through an examination of recent advancements and case studies,we underscore the critical role of multi-omics in advancing our understanding of cancer and paving the way for more effective and personalized therapeutic strategies.展开更多
BACKGROUND Gastrointestinal(GI)malignancies,including gastric and colorectal cancers,remain one of the primary contributors to cancer-related illness and death globally.Despite the availability of conventional diagnos...BACKGROUND Gastrointestinal(GI)malignancies,including gastric and colorectal cancers,remain one of the primary contributors to cancer-related illness and death globally.Despite the availability of conventional diagnostic tools,early detection and personalized treatment remain significant clinical challenges.Integrated multi-omics methods encompassing genomic,transcriptomic,proteomic,metabolomic,and microbiome profiles have emerged as powerful tools for advancing precision oncology,improving diagnostic accuracy,and informing therapeutic strategies.AIM To investigate the application of multi-omics approaches in the early detection,risk stratification,treatment optimization,and biomarker discovery of GI malignancies.METHODS The systematic review process was conducted in accordance with the PRISMA 2020 guidelines.Five databases,PubMed,ScienceDirect,Scopus,ProQuest,and Web of Science,were searched for studies published in English from 2015 onwards.Eligible studies involved human subjects and focused on multi-omics integration in GI cancers,including biomarker identification,tumor microenvironment analysis,tumor heterogeneity,organoid modeling,and artificial intelligence(AI)-driven analytics.Data extraction included study characteristics,omics modalities,clinical applications,and evaluation of study quality conducted with the Cochrane risk of bias 2.0 instrument.RESULTS A total of 17196 initially identified articles,20 met the inclusion criteria.The findings highlight the superiority of multi-omics platforms over traditional biomarkers(e.g.,carcinoembryonic antigen and carbohydrate antigen 19-9 in detecting early stage GI cancers.Key applications include the identification of circulating tumor DNA,extracellular vesicles,lipidomic and proteomic signatures,and the adoption of AI algorithms to enhance diagnostic precision.Multi-omics analysis has also revealed the mechanisms of immune modulation,tumor microenvironment regulation,metastatic behavior,and drug resistance.Organoid models and microbiota profiling have contributed to personalized therapeutic strategies and immunotherapy optimization.CONCLUSION Multi-omics approaches offer significant advancements in the early diagnosis,prognostic evaluation,and personalized treatment of GI malignancies.Their integration with AI analytics,organoid biobanking,and microbiota modulation provides a pathway for precision oncology research.展开更多
Lysosomal diseases(LDs)are a group of rare inherited disorders belonging to inborn metabolism errors.LDs are characterized by the excessive storage of undegraded substrates,most often due to the enzymatic deficiency r...Lysosomal diseases(LDs)are a group of rare inherited disorders belonging to inborn metabolism errors.LDs are characterized by the excessive storage of undegraded substrates,most often due to the enzymatic deficiency resulting from disease-causing gene variants.LDs lead to dysregulated cellular pathways and imbalanced molecular homeostasis and can affect multiple organs and tissues.Despite being rare,LDs account for a significant incidence when considered collectively.Due to complex molecular and genetic fingerprints,considerable challenges in LD management must be overcome.Diagnosis can be signifi-cantly delayed due to the broad and nonspecific clinical manifestations and the lack of specific bio-markers.Available treatments fail to fully stop the disease progression and can alter the disease's typical phenotypes with novel manifestations.Therefore,a paradigm shift is crucial to better understand LDs and provide actionable insights.Herein,we comprehensively review the literature to demonstrate that multi-omics approaches are promising for pathophysiology elucidation,biomarker discovery,and pre-cision therapy in LDs.We recommend adopting longitudinal study designs integrated with a multi-omics-empowered framework to facilitate mechanistic delineation,biomarker discovery,and treat-ment development.Relevant approaches exploring the association between LDs and common neuro-degenerative disorders are also discussed,paving a potential path for improved therapeutic development and ultimately improving the patient's quality of life.展开更多
Cancer rates are increasing globally,making it more urgent than ever to enhance research and treatment strategies.This study aims to investigate how innovative technology and integrated multi-omics techniques could he...Cancer rates are increasing globally,making it more urgent than ever to enhance research and treatment strategies.This study aims to investigate how innovative technology and integrated multi-omics techniques could help improve cancer diagnosis,knowledge,and therapy.A complete literature search was undertaken using PubMed,Elsevier,Google Scholar,ScienceDirect,Embase,and NCBI.This review examined the articles published from 2010 to 2025.Relevant articles were found using keywords and selected using inclusion criteria New sequencing methods,like next-generation sequencing and single-cell analysis,have transformed our ability to study tumor complexity and genetic mutations,paving the way for more precise,personalized treatments.At the same time,imaging technologies such as Positron Emission Tomography(PET)and Magnetic Resonance Imaging(MRI)have made detecting tumors early and tracking treatment progress easier,all while improving patient comfort.Artificial intelligence(AI)and machine learning(ML)are having a significant impact by helping to analyze large volumes of data more efficiently and enhancing diagnostic accuracy.Meanwhile,Clustered Regulatory Interspaced Short Palindromic Repeats(CRISPR/Cas9)gene editing is emerging as a promising tool for directly targeting genes related to cancer,providing new possibilities for treatment.By integrating genomic,transcriptomic,proteomic,and metabolomic data,multi-omics approaches provide researchers with a more comprehensive understanding of the molecular mechanisms driving cancer,thereby facilitating the discovery of novel biomarkers and therapeutic targets.Despite these advancements,additional challenges persist,such as data integration,elevated costs,standardisation concerns,and the intricacies of translating findings into clinical practice,which might prevent wider implementation.Research needs to concentrate on improving these developments and encouraging multidisciplinary cooperation going forward to maximize their possibilities.Personalized cancer therapies will become more successful with ongoing developments,therefore enhancing patient outcomes and quality of life.展开更多
BACKGROUND Autoimmune liver diseases,including primary biliary cholangitis(PBC),autoi-mmune hepatitis(AIH),and their overlap syndrome(OS),involve immune-mediated liver injury,with OS occurring in 1.2%-25%of PBC patien...BACKGROUND Autoimmune liver diseases,including primary biliary cholangitis(PBC),autoi-mmune hepatitis(AIH),and their overlap syndrome(OS),involve immune-mediated liver injury,with OS occurring in 1.2%-25%of PBC patients.OS carries a higher risk of cirrhosis,hepatocellular carcinoma,and reduced survival.While its pathogenesis remains unclear,gut microbiota dysbiosis and serum metabolite alterations may play key roles.This study uses 16S rRNA sequencing and liquid chromatography-mass spec-trometry(LC-MS)metabolomics to compare gut microbiota and serum metabolites among PBC,AIH,and OS patients,and explores their associations with liver function.AIM To differentiate OS from PBC and AIH based on gut microbiota,serum metabolites,and liver function.METHODS Gut microbiota profiles were analyzed using 16S rRNA sequencing,while untargeted serum metabolomics was conducted via LC-MS.Comparative analyses were performed to identify differences in microbial composition and serum metabolite levels among PBC,AIH,and OS groups.Correlation analyses and network visualization tech-niques were applied to elucidate the interactions among liver function parameters,gut microbiota,and serum metabolites in OS patients.RESULTS Compared to patients with PBC or AIH,OS patients demonstrated significantly reduced microbial diversity and richness.Notable taxonomic shifts included decreased abundances of Firmicutes,Bacteroidetes,and Actinobacteria,alongside increased levels of Proteobacteria and Verrucomicrobia.Distinct serum metabolites,such as pentadecanoic acid and aminoimidazole carboxamide ribonucleotide,were identified in OS patients.Correlation analysis revealed that aspartate aminotransferase(AST)levels were negatively associated with the bacterial genus Fusicatenibacter and the metabolite L-Tyrosine.A microbial-metabolite network diagram further confirmed a strong association between Fusicatenibacter and L-Tyrosine in OS patients.CONCLUSION OS patients show decreased gut microbiota diversity and unique serum metabolites.Multi-omics linked AST,Fusicatenibacter,and L-Tyrosine,revealing OS mechanisms and diagnostic potential.展开更多
Objective To map the research hotspots,developmental trends,and existing challenges in the integration of artificial intelligence(AI)with multi-omics in traditional Chinese medicine(TCM)through comprehensive bibliomet...Objective To map the research hotspots,developmental trends,and existing challenges in the integration of artificial intelligence(AI)with multi-omics in traditional Chinese medicine(TCM)through comprehensive bibliometric analysis.Methods China National Knowledge Infrastructure(CNKI),Wanfang Data,China Science and Technology Journal Database(VIP),Chaoxing Journal Database,PubMed,and Web of Science were searched to collect literature on the theme of AI in TCM multi-omics research from the inception of each database to December 31,2024.Eligible records were required to simultaneously address AI,TCM,and multi-omics.Quantitative and visual analyses of publication growth,core authorship networks,institutional collaboration patterns,and keyword co-occurrence were performed using Microsoft Excel 2021,NoteExpress v4.0.0,and Cite-Space 6.3.R1.AI application modes in TCM multi-omics research were also categorized and summarized.Results A total of 1106 articles were enrolled(932 Chinese and 174 English).Publication output has increased continuously since 2010 and accelerated after 2016.Region-specific collaboration clusters were identified,dominated by Beijing University of Chinese Medicine,China Academy of Chinese Medical Sciences,Shanghai University of Traditional Chinese Medicine,and Nanjing University of Chinese Medicine.Keyword co-occurrence analysis revealed that current AI applications predominantly centered on metabolomics and algorithms such as cluster analysis and data mining.Research foci mainly ranked as follows:single herbs,herbal formulae,and disease-syndrome differentiation.Conclusion Machine learning methods are the predominant integrative modality of AI in the realm of TCM multi-omics research at present,utilized for processing omics data and uncovering latent patterns therein.The domain of TCM,in addition to investigating omics information procured through high-throughput technologies,also integrates data on traditional Chinese medicinal substances and clinical phenotypes,progressing towards joint analysis of multi-omics,high-dimensionality of data,and multi-modality of information.Deep learning approaches represent an emerging trend in the field.展开更多
Background Backfat thickness(BFT)is a vital economic trait in pigs,reflecting subcutaneous fat levels that affect meat quality and production efficiency.As a complex trait shaped by multiple genetic factors,BFT has be...Background Backfat thickness(BFT)is a vital economic trait in pigs,reflecting subcutaneous fat levels that affect meat quality and production efficiency.As a complex trait shaped by multiple genetic factors,BFT has been studied using genome-wide association studies(GWAS)and linkage analyses to locate fat-related quantitative trait loci(QTLs),but pinpointing causal variants and genes is hindered by linkage disequilibrium and limited regulatory data.This study aimed to dissect the QTLs affecting BFT on Sus scrofa chromosome 1(SSC1),elucidating regulatory variants,effector genes,and the cell types involved.Results Using whole-genome genotyping data from 3,578 pigs and phenotypic data for five BFT traits,we identified a 630.6 kb QTL on SSC1 significantly associated with these traits via GWAS and fine-mapping,pinpointing 34 candidate causal variants.Using deep convolutional neural networks to predict regulatory activity from sequence data integrated with detailed pig epigenetic profiles,we identified five SNPs potentially affecting enhancer activity in specific tissues.Notably,rs342950505(SSC1:161,123,588)influences weak enhancer activity across multiple tissues,including the brain.High-throughput chromosome conformation capture(Hi-C)analysis identified that rs342950505 interacts with eight genes.Chromatin state annotations confirmed enhancer activity at this QTL in the cerebellum.Leveraging these insights,single-cell ATAC-seq revealed a chromatin accessibility peak encompassing rs342950505 that regulates PMAIP1 expression in inhibitory neurons via enhancer-mediated mechanisms,with an adjacent peak modulating CCBE1 expression in neuroblasts and granule cells.Transcriptome-wide association studies(TWAS)confirmed PMAIP1's role in the hypothalamus,and Mendelian randomization(MR)validated PMAIP1 and CCBE1 as key brain expression quantitative trait locus(eQTL)effectors.We propose that the variant rs342950505,located within a regulatory peak,modulates PMAIP1 expression in inhibitory neurons,potentially influencing energy homeostasis via hypothalamic regulation.Similarly,CCBE1 may contribute to this process.Conclusions Our results,through systematic dissection of pleiotropic BFT-associated loci,provide a framework to elucidate regulatory mechanisms of complex traits,offering insights into polygenic control through lipid metabolism and neural signaling pathways.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.:82560497,82260502,82272656)Guizhou Provincial Basic Research Program(Grant No.:Natural Science,MS[2025]-495)Talent Fund of Guizhou Provincial People’s Hospital(Grant No.:2022-33).
文摘Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insights needed for this purpose.Methods:To comprehensively capture breast cancer heterogeneity,we employed integrative clustering that incorporates six molecular features from 670 breast cancer samples.Ten distinct clustering algorithms were combined to ensure robust subtype identification,and the identified subtypes were validated in four independent datasets.Subsequently,we constructed a survival support vector machine prognostic model based on key molecular features to enhance survival prediction and clinical applicability.Results:Five novel subtypes were identified:consensus subtypes 1–5(CS1–CS5).CS2 was an aggressive subtype with elevated TP53 mutation rates,high tumor mutational burden,and strong sensitivity to YM-155 and ispinesib.Conversely,CS5 exhibited stable genomics with enhanced nucleotide excision repair and favorable prognoses.CS2 and CS4 showed enriched immune checkpoint expression,indicating potential immunotherapy responsiveness,while CS1 and CS5 exhibited immune-cold profiles.The survival support vector machine model effectively predicted survival outcomes across independent datasets.Conclusions:The refined breast cancer classification framework developed in this research uncovers new insights into molecular heterogeneity,enhances risk stratification,and enables the identification of promising therapeutic targets.The potential of this framework to optimize personalized treatment strategies warrants further clinical validation.
基金supported by grants from the Fundamental Research Funds for the Central Universities(No.2025ZFJH03)the Central Guidance Fund for Local Science and Technology Development(No.2024ZY01054)the CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-045).
文摘Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.
基金supported by the National Key Research and Development Program of China(2022YFD2101101)the Earmarked Fund for CARS-19+2 种基金the National Natural Science Foundation of China(32402634)the Modern Agricultural(Tea)Industry Technology System of Fujian Province,China(2025 No.593)the Special Fund for Science and Technology Innovation of Fujian Zhang Tianfu Tea Development Foundation,China(FJZTF01)。
文摘Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi-omics strategy to characterize the changes and interactions among metabolomic(MB),transcriptomic(TX),and proteomic(PT)profiles in mechanically stressed tea leaves.Mechanical stress initially activated damage-associated molecular patterns(DAMPs),including Ca^(2+)signaling,jasmonic acid signaling,and glutathione metabolism pathways.These processes subsequently induced quality-related metabolic pathways(QRMPs),particularly α-linolenic acid and phenylalanine metabolism.Upregulated expression of LOX,ADH1,and PAR genes,together with the increased abundance of their encoded proteins,respectively promoted the accumulation of jasmine lactone,benzyl alcohol,and 2-phenylethanol.These findings indicate that mechanical stress influences the metabolite biosynthesis in tea leaves through coordinated molecular responses.This study provides new insights into the molecular mechanisms underlying tea leaf responses to mechanical stress and a foundation for future investigations into how early molecular events may contribute to post-harvest metabolic changes during oolong tea processing.
基金funded by the Key Research and Development and Promotion Project of Henan Province(Grant No.232102310130)。
文摘Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression;however, their contribution to the functional classification and personalized treatment of gastric cancerremains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatmentstrategies for patients with gastric cancer. Methods: Single-cell and bulk transcriptomic analyses were integrated tocharacterize gastric cancer fibroblasts. “Seurat”, “Slingshot”, and “CellChat” were used for dimensionality reduction,trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast moduleswere identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct aprognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features.The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays. Results: Eight fibroblast subpopulations associated with gastric cancer metastasisexhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated thatmetastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed markedimmunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-relatedpathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressedgastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis. Conclusion: This studycharacterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis andestablished a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strongpredictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, thefindings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.
基金supported by the National Natural Science Foundation of China,No.82374552(to WP)the Natural Science Foundation of Hunan Province,Nos.2024JJ2086,2024JJ6597(to JK)+1 种基金the Science and Technology Innovation Program of Hunan Province,No.2022RC1220(to WP)Support Plan for High-Level Health and Medical Talents in Hunan Province,No.20240304076(to WP).
文摘Because the pathogenesis of Alzheimer’s disease is multifactorial and complex,integrated multi-level omics analysis is essential to comprehensively elucidate its molecular alterations.We therefore utilized the well-established amyloid precursor protein/presenilin 1 mouse model to carry out an integrated multi-omics study using transcriptomic,proteomic,N^(6)-methyladenosine epitranscriptomic,and phosphoproteomic analyses.The results revealed substantial molecular alterations across multiple biological dimensions and the alteration in the expression of several key genes,such as GFAP,APP,and RTN4,in a mouse model of Alzheimer’s disease.The pronounced elevation of RTN4 in reactive astrocytes is indicative of its involvement in Alzheimer’s disease pathogenesis.Furthermore,we identified dysregulation of pathways related to endocytosis,highlighting the critical role of this process in disease progression.Our findings underscore the significant impact of post-transcriptional(N^(6)-methyladenosine methylation)and post-translational(phosphorylation)protein modifications,which have been underrepresented in Alzheimer’s disease research.The significant contribution made by this study is the integrated,multi-level omics analysis that we carried out to investigate the complex biological changes that occur in Alzheimer’s disease.Our findings provide novel insights into Alzheimer’s disease pathogenesis and suggest potential therapeutic targets,such as RTN4.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
基金supported by the National Key Research and Development Program of China(2021YFF1000602)the National Natural Science Foundations(32202642)the earmarked fund for CARS-35-PIG.
文摘Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid and nutrient components between the adipose tissue of Bamei pigs and commercial pigs are still unclear.The study employed UPLC-MS/MS to quantify the composition of lipids and metabolites in the backfat of both Bamei and Large White pigs.A total of 428 lipids and 193 metabolites were significantly different between the 2 groups.Specifically,Bamei pig backfat exhibited altered levels of various lipids and metabolites that may potentially contribute to nutritional and flavor differences,including unsaturated triglycerides,free fatty acids,medium-chain triglycerides,essential amino acids,vitamins and antioxidants,while maintaining reduced cholesterol levels.Furthermore,we delved into the molecular mechanisms underlying these nutritional differences by analyzing significantly different 431 m RNAs and 865 proteins and integrating the regulatory network of protein-metabolite-lipid pathway.Importantly,in the pyruvate metabolic pathway of Bamei pigs,the bioprocess of lactate production was inhibited but the acetyl-Co A production was activated,suggesting the possibility that energy allocation favors the biogenesis of lipid precursors.These findings may contribute to guiding industrial food producers in enhancing the quality of lard at the genetic and molecular levels.
文摘Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including non-invasive biomarkers have been explored for the diagnosis of viral hepatitis.With the fast development of multi-omics technology,non-invasive biomarkers can be detected from blood,saliva,urine,stool,and other body fluids.The advantages of non-invasive biomarkers are:1)non-invasive;2)convenient to test and 3)repeatable.The application of non-invasive biomarkers significantly improves the diagnostic accuracy of viral hepatitis.The non-invasive biomarkers can be sugars,proteins,nucleic acids,and even microorganisms.In this review,we summarized recent advances in identifying non-invasive biomarkers using multi-omics technology and discussed their potential diagnostic values for viral hepatitis.
基金supported by the National Natural Science Foundation of China(32160578)the Ningxia Hui Autonomous Region Key Research and Develoment Program(2023BCF01027).
文摘Lactic acid bacteria and the fermentation environment interact to form an intertwined system.Lactic acid bacteria are constantly evolving to adapt to different fermentation environments,causing changes in their physiological processes.To achieve a targeted improvement of their adaptability to various environments,a detail analysis of their evolutionary physiological processes is required.While several studies have been carried out in the past by using single-omics techniques to investigate their response to environmental stress,most researchers are now using a multi-omics approach to explore more detail in the biological regulatory networks and molecular mechanisms of lactic acid bacteria in response to environmental stress,thereby overcoming the limitations of single-omics analysis.In this review,we describe the various single-omics approaches that have been used to study environmental stress in lactic acid bacteria,present the advantages of various multi-omics combined analysis approaches,and discuss the potential and practicality of applying emerging single-cell transcriptomics and single-cell metabolomics techniques to the molecular mechanism study of microbes response to environmental stress.Multi-omics approaches enable the accurate identification of complex microbial physiological processes in different environments,allow people to comprehensively reveal the molecular mechanisms of microbes response to stress from different perspectives.Single-cell omics techniques,analyze the targeted regulation of microbial functions in a multi-dimensional space,provides a new perspective on understanding microbes responses environment stress.
文摘Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategies often fall short in addressing the complex,multifactorial nature of DM.This review ex-plores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches.We consolidated genomic,transcriptomic,proteomic,metabolomic,and microbiomic data from major databases and peer-reviewed publications(2015-2025),with an emphasis on clinical relevance.Multi-omics investigations have identified convergent mole-cular networks underlyingβ-cell dysfunction,insulin resistance,and diabetic complications.The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis.Novel biomarkers facilitate early detection of DM and its complications,while single-cell multi-omics and machine learning further refine risk stratification.By dissecting DM heterogeneity more precisely,multi-omics integration enables targeted in-terventions and preventive strategies.Future efforts should focus on data har-monization,ethical considerations,and real-world validation to fully leverage multi-omics in addressing the global DM burden.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
基金supported by grants from the Medical Engineering Jiont Fund of the Fudan University(No.IDH2310117)。
文摘Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering valuable insights into tumor biology and potential treatment strategies.Methods:We conducted a comprehensive multi-omics analysis of 132 patients with American Joint Committee on Cancer(AJCC)stage III TNBC,comprising 36 long-term survivors(RFS≥8 years),62 moderate-term survivors(RFS:3-8 years),and 34 short-term survivors(RFS<3 years).Analyses investigated clinicopathological factors,whole-exome sequencing,germline mutations,copy number alterations(CNAs),RNA sequences,and metabolomic profiles.Results:Long-term survivors exhibited fewer metastatic regional lymph nodes,along with tumors showing reduced stromal fibrosis and lower Ki67 index.Molecularly,these tumors exhibited multiple alterations in genes related to homologous recombination repair,with higher frequencies of germline mutations and somatic CNAs.Additionally,tumors from long-term survivors demonstrated significant downregulation of the RTK-RAS signaling pathway.Metabolomic profiling revealed decreased levels of lipids and carbohydrate,particularly those involved in glycerophospholipid,fructose,and mannose metabolism,in long-term survival group.Multivariate Cox analysis identified fibrosis[hazard ratio(HR):12.70,95%confidence interval(95%CI):2.19-73.54,P=0.005]and RAC1copy number loss/deletion(HR:0.22,95%CI:0.06-0.83,P=0.026)as independent predictors of RFS.Higher fructose/mannose metabolism was associated with worse overall survival(HR:1.30,95%CI:1.01-1.68,P=0.045).Our findings emphasize the association between biological determinants and prolonged survival in patients with TNBC.Conclusions:Our study systematically identified the key molecular and metabolic features associated with prolonged survival in AJCC stage III TNBC,suggesting potential therapeutic targets to improve patient outcomes.
文摘The advent of multi-omics approaches has revolutionized the field of oncology by enabling a comprehensive understanding of cancer biology through the integration of diverse biological data.This review aims to explore the synergy between three key omics domains:radiomics,genoproteomics,and pathomics.Radiomics involves extracting high-dimensional data from medical images,providing valuable insights into tumor heterogeneity and treatment response.Genoproteomics,encompassing both genomic and proteomic analyses,delves into the molecular mechanisms driving cancer progression and therapeutic resistance.Pathomics leverages advanced digital pathology techniques to quantitatively analyze tissue architecture and cellular morphology.We provide an in-depth overview of the methodologies and tools employed in each omics field,highlighting their specific applications in oncology,including cancer diagnosis,biomarker discovery,and prediction of treatment outcomes.Furthermore,we discuss the integration of multi-omics data,addressing the challenges and innovative solutions for harmonizing these complex datasets.Through an examination of recent advancements and case studies,we underscore the critical role of multi-omics in advancing our understanding of cancer and paving the way for more effective and personalized therapeutic strategies.
文摘BACKGROUND Gastrointestinal(GI)malignancies,including gastric and colorectal cancers,remain one of the primary contributors to cancer-related illness and death globally.Despite the availability of conventional diagnostic tools,early detection and personalized treatment remain significant clinical challenges.Integrated multi-omics methods encompassing genomic,transcriptomic,proteomic,metabolomic,and microbiome profiles have emerged as powerful tools for advancing precision oncology,improving diagnostic accuracy,and informing therapeutic strategies.AIM To investigate the application of multi-omics approaches in the early detection,risk stratification,treatment optimization,and biomarker discovery of GI malignancies.METHODS The systematic review process was conducted in accordance with the PRISMA 2020 guidelines.Five databases,PubMed,ScienceDirect,Scopus,ProQuest,and Web of Science,were searched for studies published in English from 2015 onwards.Eligible studies involved human subjects and focused on multi-omics integration in GI cancers,including biomarker identification,tumor microenvironment analysis,tumor heterogeneity,organoid modeling,and artificial intelligence(AI)-driven analytics.Data extraction included study characteristics,omics modalities,clinical applications,and evaluation of study quality conducted with the Cochrane risk of bias 2.0 instrument.RESULTS A total of 17196 initially identified articles,20 met the inclusion criteria.The findings highlight the superiority of multi-omics platforms over traditional biomarkers(e.g.,carcinoembryonic antigen and carbohydrate antigen 19-9 in detecting early stage GI cancers.Key applications include the identification of circulating tumor DNA,extracellular vesicles,lipidomic and proteomic signatures,and the adoption of AI algorithms to enhance diagnostic precision.Multi-omics analysis has also revealed the mechanisms of immune modulation,tumor microenvironment regulation,metastatic behavior,and drug resistance.Organoid models and microbiota profiling have contributed to personalized therapeutic strategies and immunotherapy optimization.CONCLUSION Multi-omics approaches offer significant advancements in the early diagnosis,prognostic evaluation,and personalized treatment of GI malignancies.Their integration with AI analytics,organoid biobanking,and microbiota modulation provides a pathway for precision oncology research.
基金Graphical abstract and Figs.1-4 were createdwith BioRender.com.
文摘Lysosomal diseases(LDs)are a group of rare inherited disorders belonging to inborn metabolism errors.LDs are characterized by the excessive storage of undegraded substrates,most often due to the enzymatic deficiency resulting from disease-causing gene variants.LDs lead to dysregulated cellular pathways and imbalanced molecular homeostasis and can affect multiple organs and tissues.Despite being rare,LDs account for a significant incidence when considered collectively.Due to complex molecular and genetic fingerprints,considerable challenges in LD management must be overcome.Diagnosis can be signifi-cantly delayed due to the broad and nonspecific clinical manifestations and the lack of specific bio-markers.Available treatments fail to fully stop the disease progression and can alter the disease's typical phenotypes with novel manifestations.Therefore,a paradigm shift is crucial to better understand LDs and provide actionable insights.Herein,we comprehensively review the literature to demonstrate that multi-omics approaches are promising for pathophysiology elucidation,biomarker discovery,and pre-cision therapy in LDs.We recommend adopting longitudinal study designs integrated with a multi-omics-empowered framework to facilitate mechanistic delineation,biomarker discovery,and treat-ment development.Relevant approaches exploring the association between LDs and common neuro-degenerative disorders are also discussed,paving a potential path for improved therapeutic development and ultimately improving the patient's quality of life.
文摘Cancer rates are increasing globally,making it more urgent than ever to enhance research and treatment strategies.This study aims to investigate how innovative technology and integrated multi-omics techniques could help improve cancer diagnosis,knowledge,and therapy.A complete literature search was undertaken using PubMed,Elsevier,Google Scholar,ScienceDirect,Embase,and NCBI.This review examined the articles published from 2010 to 2025.Relevant articles were found using keywords and selected using inclusion criteria New sequencing methods,like next-generation sequencing and single-cell analysis,have transformed our ability to study tumor complexity and genetic mutations,paving the way for more precise,personalized treatments.At the same time,imaging technologies such as Positron Emission Tomography(PET)and Magnetic Resonance Imaging(MRI)have made detecting tumors early and tracking treatment progress easier,all while improving patient comfort.Artificial intelligence(AI)and machine learning(ML)are having a significant impact by helping to analyze large volumes of data more efficiently and enhancing diagnostic accuracy.Meanwhile,Clustered Regulatory Interspaced Short Palindromic Repeats(CRISPR/Cas9)gene editing is emerging as a promising tool for directly targeting genes related to cancer,providing new possibilities for treatment.By integrating genomic,transcriptomic,proteomic,and metabolomic data,multi-omics approaches provide researchers with a more comprehensive understanding of the molecular mechanisms driving cancer,thereby facilitating the discovery of novel biomarkers and therapeutic targets.Despite these advancements,additional challenges persist,such as data integration,elevated costs,standardisation concerns,and the intricacies of translating findings into clinical practice,which might prevent wider implementation.Research needs to concentrate on improving these developments and encouraging multidisciplinary cooperation going forward to maximize their possibilities.Personalized cancer therapies will become more successful with ongoing developments,therefore enhancing patient outcomes and quality of life.
基金Supported by WBE Liver Foundation,No.WBE20220182022 Young and Middle-aged Talents Incubation Project(Youth Innovation)of Beijing Youan Hospital,Capital Medical University,No.BJYAYY-YN-2022-092023 Young and Middle-aged Talents Incubation Project(Youth Innovation)of Beijing Youan Hospital,Capital Medical University,No.BJYAYYYN2023-14.
文摘BACKGROUND Autoimmune liver diseases,including primary biliary cholangitis(PBC),autoi-mmune hepatitis(AIH),and their overlap syndrome(OS),involve immune-mediated liver injury,with OS occurring in 1.2%-25%of PBC patients.OS carries a higher risk of cirrhosis,hepatocellular carcinoma,and reduced survival.While its pathogenesis remains unclear,gut microbiota dysbiosis and serum metabolite alterations may play key roles.This study uses 16S rRNA sequencing and liquid chromatography-mass spec-trometry(LC-MS)metabolomics to compare gut microbiota and serum metabolites among PBC,AIH,and OS patients,and explores their associations with liver function.AIM To differentiate OS from PBC and AIH based on gut microbiota,serum metabolites,and liver function.METHODS Gut microbiota profiles were analyzed using 16S rRNA sequencing,while untargeted serum metabolomics was conducted via LC-MS.Comparative analyses were performed to identify differences in microbial composition and serum metabolite levels among PBC,AIH,and OS groups.Correlation analyses and network visualization tech-niques were applied to elucidate the interactions among liver function parameters,gut microbiota,and serum metabolites in OS patients.RESULTS Compared to patients with PBC or AIH,OS patients demonstrated significantly reduced microbial diversity and richness.Notable taxonomic shifts included decreased abundances of Firmicutes,Bacteroidetes,and Actinobacteria,alongside increased levels of Proteobacteria and Verrucomicrobia.Distinct serum metabolites,such as pentadecanoic acid and aminoimidazole carboxamide ribonucleotide,were identified in OS patients.Correlation analysis revealed that aspartate aminotransferase(AST)levels were negatively associated with the bacterial genus Fusicatenibacter and the metabolite L-Tyrosine.A microbial-metabolite network diagram further confirmed a strong association between Fusicatenibacter and L-Tyrosine in OS patients.CONCLUSION OS patients show decreased gut microbiota diversity and unique serum metabolites.Multi-omics linked AST,Fusicatenibacter,and L-Tyrosine,revealing OS mechanisms and diagnostic potential.
基金General Project of Scientific Research of Hunan Provincial Education Department (22C0191)General Project of University-level Scientific Research of Hunan University of Chinese Medicine (Z2023XJYB21)Hunan Provincial Degree and Graduate Education Reform Research Project(2024JGYB157)。
文摘Objective To map the research hotspots,developmental trends,and existing challenges in the integration of artificial intelligence(AI)with multi-omics in traditional Chinese medicine(TCM)through comprehensive bibliometric analysis.Methods China National Knowledge Infrastructure(CNKI),Wanfang Data,China Science and Technology Journal Database(VIP),Chaoxing Journal Database,PubMed,and Web of Science were searched to collect literature on the theme of AI in TCM multi-omics research from the inception of each database to December 31,2024.Eligible records were required to simultaneously address AI,TCM,and multi-omics.Quantitative and visual analyses of publication growth,core authorship networks,institutional collaboration patterns,and keyword co-occurrence were performed using Microsoft Excel 2021,NoteExpress v4.0.0,and Cite-Space 6.3.R1.AI application modes in TCM multi-omics research were also categorized and summarized.Results A total of 1106 articles were enrolled(932 Chinese and 174 English).Publication output has increased continuously since 2010 and accelerated after 2016.Region-specific collaboration clusters were identified,dominated by Beijing University of Chinese Medicine,China Academy of Chinese Medical Sciences,Shanghai University of Traditional Chinese Medicine,and Nanjing University of Chinese Medicine.Keyword co-occurrence analysis revealed that current AI applications predominantly centered on metabolomics and algorithms such as cluster analysis and data mining.Research foci mainly ranked as follows:single herbs,herbal formulae,and disease-syndrome differentiation.Conclusion Machine learning methods are the predominant integrative modality of AI in the realm of TCM multi-omics research at present,utilized for processing omics data and uncovering latent patterns therein.The domain of TCM,in addition to investigating omics information procured through high-throughput technologies,also integrates data on traditional Chinese medicinal substances and clinical phenotypes,progressing towards joint analysis of multi-omics,high-dimensionality of data,and multi-modality of information.Deep learning approaches represent an emerging trend in the field.
基金supported by the China Postdoctoral Science Foundation[Grant Number BX20240146 and 2024M761230]Key Project of Research and Development Plan in Jiangxi Province[Grant Number 20243BCC31001].
文摘Background Backfat thickness(BFT)is a vital economic trait in pigs,reflecting subcutaneous fat levels that affect meat quality and production efficiency.As a complex trait shaped by multiple genetic factors,BFT has been studied using genome-wide association studies(GWAS)and linkage analyses to locate fat-related quantitative trait loci(QTLs),but pinpointing causal variants and genes is hindered by linkage disequilibrium and limited regulatory data.This study aimed to dissect the QTLs affecting BFT on Sus scrofa chromosome 1(SSC1),elucidating regulatory variants,effector genes,and the cell types involved.Results Using whole-genome genotyping data from 3,578 pigs and phenotypic data for five BFT traits,we identified a 630.6 kb QTL on SSC1 significantly associated with these traits via GWAS and fine-mapping,pinpointing 34 candidate causal variants.Using deep convolutional neural networks to predict regulatory activity from sequence data integrated with detailed pig epigenetic profiles,we identified five SNPs potentially affecting enhancer activity in specific tissues.Notably,rs342950505(SSC1:161,123,588)influences weak enhancer activity across multiple tissues,including the brain.High-throughput chromosome conformation capture(Hi-C)analysis identified that rs342950505 interacts with eight genes.Chromatin state annotations confirmed enhancer activity at this QTL in the cerebellum.Leveraging these insights,single-cell ATAC-seq revealed a chromatin accessibility peak encompassing rs342950505 that regulates PMAIP1 expression in inhibitory neurons via enhancer-mediated mechanisms,with an adjacent peak modulating CCBE1 expression in neuroblasts and granule cells.Transcriptome-wide association studies(TWAS)confirmed PMAIP1's role in the hypothalamus,and Mendelian randomization(MR)validated PMAIP1 and CCBE1 as key brain expression quantitative trait locus(eQTL)effectors.We propose that the variant rs342950505,located within a regulatory peak,modulates PMAIP1 expression in inhibitory neurons,potentially influencing energy homeostasis via hypothalamic regulation.Similarly,CCBE1 may contribute to this process.Conclusions Our results,through systematic dissection of pleiotropic BFT-associated loci,provide a framework to elucidate regulatory mechanisms of complex traits,offering insights into polygenic control through lipid metabolism and neural signaling pathways.
