2025
High MGMT expression identifies aggressive colorectal cancer with distinct genomic features and immune evasion properties
Zhang J, Rajendran B, Desai S, Gibson J, DiPalermo J, LoRusso P, Kong Y, Zhao H, Cecchini M, Schalper K. High MGMT expression identifies aggressive colorectal cancer with distinct genomic features and immune evasion properties. Journal For ImmunoTherapy Of Cancer 2025, 13: e011653. PMID: 40935566, DOI: 10.1136/jitc-2025-011653.Peer-Reviewed Original ResearchConceptsTumor-infiltrating lymphocytesCD8+ tumor-infiltrating lymphocytesPromoter methylation statusMGMT expressionMGMT overexpressionClinical significanceAllogeneic peripheral blood mononuclear cellsCD8+ T cellsMGMT promoter methylation statusKilling of malignant cellsMGMT-expressing cellsMultiplexed quantitative immunofluorescencePeripheral blood mononuclear cellsAggressive clinical courseMGMT protein expressionAggressive colorectal cancerMethylation statusExpression of MGMTLevels of MGMT proteinMGMT protein levelsImmune evasion propertiesBlood mononuclear cellsMutational featuresAdaptive immune evasionSomatic mutation burdenSuperGLUE facilitates an explainable training framework for multi-modal data analysis
Liu T, Zhao J, Zhao H. SuperGLUE facilitates an explainable training framework for multi-modal data analysis. Cell Reports Methods 2025, 5: 101167. PMID: 40914154, DOI: 10.1016/j.crmeth.2025.101167.Peer-Reviewed Original ResearchConceptsData integrationProbabilistic deep learningMulti-modal data analysisInference of gene regulatory networksMulti-modal data integrationDeep learningGene regulatory networksTraining frameworkBaseline modelRegulatory networksComplex biological systemsRegulatory relationshipsSensing dataCell statesGlobal structureArea of active researchActive researchOmicsBiological featuresScalable methodFrameworkBiological systemsStatistical modelNetworkBiological linkagesKnockoff procedure improves susceptibility gene identifications in conditional transcriptome-wide association studies
Zhang X, Wang L, Zhao J, Zhao H. Knockoff procedure improves susceptibility gene identifications in conditional transcriptome-wide association studies. American Journal Of Human Genetics 2025 PMID: 40902598, PMCID: PMC12412983, DOI: 10.1016/j.ajhg.2025.08.007.Peer-Reviewed Original ResearchTranscriptome-wide association studyExpression quantitative trait lociGenome-wide association studiesGene-trait pairsFalse discovery rateAssociation studiesTranscriptome-wide association study approachTranscriptome-wide association study methodExpression quantitative trait loci dataGenes associated with complex traitsGenetic variantsGenome-wide association study summary statisticsSusceptibility genesGene-trait associationsSusceptibility gene identificationQuantitative trait lociParametric bootstrap samplingGene expression levelsGenomic regionsGenetic elementsComplex traitsGene identificationTrait lociFalse discovery rate levelKnockoff procedurespVelo: RNA velocity inference for multi-batch spatial transcriptomics data
Long W, Liu T, Xue L, Zhao H. spVelo: RNA velocity inference for multi-batch spatial transcriptomics data. Genome Biology 2025, 26: 239. PMID: 40790237, PMCID: PMC12337411, DOI: 10.1186/s13059-025-03701-8.Peer-Reviewed Original ResearchConceptsSpatial transcriptomics dataTranscriptome dataGene regulatory network inferenceRegulatory network inferenceVelocity inferenceComplex tissue organizationTranscriptional dynamicsRNA velocityNetwork inferenceSpatial transcriptomicsMarker identificationRNATissue organizationDownstream applicationsBiological mechanismsTranscriptomeGenesCommunication inferenceLeveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations
Zhou G, Yolou I, Xie Y, Zhao H. Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations. American Journal Of Human Genetics 2025, 112: 1923-1935. PMID: 40633541, PMCID: PMC12252582, DOI: 10.1016/j.ajhg.2025.06.010.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresAdmixed individualsNon-European populationsLocal ancestryTransferability of PRSPerformance of polygenic risk scoresAdmixed populationsCross-ancestryPolygenic risk score calculatorGenetic prediction of complex traitsGenetic predictionEffect sizePrediction of complex traitsPopulation ArchitectureUK BiobankPolygenic predictionAdmixed AmericansAncestry clustersGenetic architectureComplex traitsPRS modelRisk scoreGenetic variantsAncestryIndividualsIncorporating local ancestry information to predict genetically associated DNA methylation in admixed populations
Cheng Y, Zhou G, Li H, Zhang X, Justice A, Martinez C, Aouizerat B, Xu K, Zhao H. Incorporating local ancestry information to predict genetically associated DNA methylation in admixed populations. Briefings In Bioinformatics 2025, 26: bbaf325. PMID: 40622482, PMCID: PMC12232425, DOI: 10.1093/bib/bbaf325.Peer-Reviewed Original ResearchConceptsMethylome-wide association studiesAdmixed populationsComplex traitsLocal ancestryAssociation studiesDNA methylationAssociated with complex traitsLocal ancestry informationPopulations of European ancestryCpG methylation levelsNon-European populationsMeasurement of methylationAncestry informationCpG sitesMethylation levelsEuropean ancestryEpigenetic underpinningsCpGAncestryTraitsMethylationAmerican populationAfrican American populationDNAPopulationspEMO: Leveraging Multi-Modal Foundation Models for Analyzing Spatial Multi-Omic and Histopathology Data
Zhao H, Liu T, Huang T, Ding T, Wu H, Humphrey P, Perincheri S, Schalper K, Ying R, Xu H, Zou J, Mahmood F. spEMO: Leveraging Multi-Modal Foundation Models for Analyzing Spatial Multi-Omic and Histopathology Data. 2025 DOI: 10.21203/rs.3.rs-6941589/v1.Peer-Reviewed Original ResearchInformation retrieval capabilitiesMedical report generationMulti-modal alignmentSingle-modal dataDownstream tasksLanguage modelModel architectureComputer systemsAI systemsSpatial domain identificationData modalitiesHistopathological imagesReport generationEvaluation taskMultimodal representationsTaskMulti-omics dataFoundation modelMulti-omics technologiesDataSpatial biologyMulti-OmicsTissue contextDomain identificationInformationCorrection: Single-nucleus RNA sequencing reveals distinct pathophysiological trophoblast signatures in spontaneous preterm birth subtypes
Uhm C, Gu J, Ju W, Pizzella S, Oktay H, Peng J, Guariglia S, Liu Y, Zhao H, Wang Y, Menon R, Zhong N. Correction: Single-nucleus RNA sequencing reveals distinct pathophysiological trophoblast signatures in spontaneous preterm birth subtypes. Cell & Bioscience 2025, 15: 90. PMID: 40571915, PMCID: PMC12203711, DOI: 10.1186/s13578-025-01421-x.Peer-Reviewed Original ResearchRobust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk
Hu J, Ye Y, Zhang C, Ruan Y, Natarajan P, Zhao H. Robust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk. PLOS Computational Biology 2025, 21: e1013191. PMID: 40570042, PMCID: PMC12212871, DOI: 10.1371/journal.pcbi.1013191.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresCAD-PRSUK BiobankCoronary artery disease polygenic risk scoreSummary-level dataCAD-related traitsSamples of European ancestryCoronary artery diseaseHigh-risk individualsPotential genetic heterogeneityCurrent smokingPolygenic scoresPolygenic riskTargeted interventionsEuropean ancestryRisk scorePleiotropic regionsRisk predictionGenetic heterogeneityBiological functionsPleiotropySignificant interactionPhenotypic heterogeneityBlood pressureDisease interpretationSingle-cell transcriptomic and chromatin dynamics of the human brain in PTSD
Hwang A, Skarica M, Xu S, Coudriet J, Lee C, Lin L, Terwilliger R, Sliby A, Wang J, Nguyen T, Li H, Wu M, Dai Y, Duan Z, Srinivasan S, Zhang X, Lin Y, Cruz D, Deans P, Huber B, Levey D, Glausier J, Lewis D, Gelernter J, Holtzheimer P, Friedman M, Gerstein M, Sestan N, Brennand K, Xu K, Zhao H, Krystal J, Young K, Williamson D, Che A, Zhang J, Girgenti M. Single-cell transcriptomic and chromatin dynamics of the human brain in PTSD. Nature 2025, 643: 744-754. PMID: 40533550, PMCID: PMC12267058, DOI: 10.1038/s41586-025-09083-y.Peer-Reviewed Original ResearchPost-traumatic stress disorderPrefrontal cortexDorsolateral prefrontal cortexHuman prefrontal cortexTraumatic stress responseHuman brainDepressive disorderStress disorderTrauma exposureCell type-specific contextRegulatory mechanismsGABAergic transmissionCell-type clusteringCell type-specific mannerMolecular regulatory mechanismsGlucocorticoid signalingGene expression changesChromatin dynamicsCredible variantsDisordersTranscriptional regulationEpigenetic dataCortexPersistent effectsPolygenic disorderA perspective on developing foundation models for analyzing spatial transcriptomic data
Liu T, Hao M, Liu X, Zhao H. A perspective on developing foundation models for analyzing spatial transcriptomic data. Quantitative Biology 2025, 13 DOI: 10.1002/qub2.70010.Peer-Reviewed Original ResearchTumor microenvironment of non-small cell lung cancer impairs immune cell function among people with HIV
Desai S, Salahuddin S, Yusuf R, Ranjan K, Gu J, Osmani L, Lin Y, Mehta S, Talmon R, Kang I, Kluger Y, Zhao H, Schalper K, Emu B. Tumor microenvironment of non-small cell lung cancer impairs immune cell function among people with HIV. Journal Of Clinical Investigation 2025, 135: e177310. PMID: 40459946, PMCID: PMC12259253, DOI: 10.1172/jci177310.Peer-Reviewed Original ResearchNon-small cell lung cancerTumor microenvironmentImmune cellsLung cancerCohort of non-small cell lung cancerExpression of PD-1Impairs anti-tumor responsesTumor-infiltrating CD8+Tumor-specific immune responsesCD4+ T cellsHIV-associated tumorsInfiltrating CD8+Expression of immunoregulatory moleculesAnti-tumor responsesTumor-associated macrophagesCell lung cancerImmune cell functionNSCLC cell linesPD-1Tumor killingBlocker therapyCD8+LAG-3Immune landscapeImmunoregulatory phenotypescMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
Wang G, Zhao J, Lin Y, Liu T, Zhao Y, Zhao H. scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links. Nature Communications 2025, 16: 4994. PMID: 40442129, PMCID: PMC12122792, DOI: 10.1038/s41467-025-60333-z.Peer-Reviewed Original ResearchConceptsDeep learning frameworkSingle-cell multi-omics researchSingle-cell multi-omics dataLearning frameworkMulti-omics dataGenerative adversarial networkSingle-cell technologiesData alignmentSingle-cell resolutionMulti-omics researchDownstream analysisCellular statesOmics datasetsAdversarial networkNeural networkProteomic profilingCorrelated featuresBiological informationOmics perspectiveDiverse datasetsFeature topologyDisease mechanismsCell embeddingData resourcesRelationship inferenceThe subfornical organ is a nucleus for gut-derived T cells that regulate behaviour
Yoshida T, Nguyen M, Zhang L, Lu B, Zhu B, Murray K, Mineur Y, Zhang C, Xu D, Lin E, Luchsinger J, Bhatta S, Waizman D, Coden M, Ma Y, Israni-Winger K, Russo A, Wang H, Song W, Al Souz J, Zhao H, Craft J, Picciotto M, Grutzendler J, Distasio M, Palm N, Hafler D, Wang A. The subfornical organ is a nucleus for gut-derived T cells that regulate behaviour. Nature 2025, 643: 499-508. PMID: 40437096, DOI: 10.1038/s41586-025-09050-7.Peer-Reviewed Original ResearchMeningeal T cellsCentral nervous systemT cellsSubfornical organCD4 T cellsInnate immune compartmentGut-brain axisSteady-state brainGut microbiotaSpecialized immune cellsCentral nervous system homeostasisAdaptive immune systemBiological functionsImmune compartmentGut-derived T cellsImmune cellsWhite adiposeImmune systemNervous systemAdipose tissueComposition of adipose tissueGastrointestinal tissuesWell-characterizedHomeostasisBrainBuilding a unified model for drug synergy analysis powered by large language models
Liu T, Chu T, Luo X, Zhao H. Building a unified model for drug synergy analysis powered by large language models. Nature Communications 2025, 16: 4537. PMID: 40374634, PMCID: PMC12081637, DOI: 10.1038/s41467-025-59822-y.Peer-Reviewed Original ResearchUnraveling the genetics of gulf war illness in diverse participants enrolled in the million veteran program
Pathak G, Koller D, Cabrera-Mendoza B, Nono Djotsa A, Wendt F, De Lillo A, Friligkou E, He J, Kouakou M, Duong L, Vahey J, Steele L, Quaden R, Harrington K, Ahmed S, Gaziano J, Concato J, Zhao H, Radhakrishnan K, Gelernter J, Gifford E, Aslan M, Helmer D, Hauser E, Polimanti R. Unraveling the genetics of gulf war illness in diverse participants enrolled in the million veteran program. Human Molecular Genetics 2025, ddaf075. PMID: 40366759, DOI: 10.1093/hmg/ddaf075.Peer-Reviewed Original ResearchPolygenic scoresGulf War IllnessGenome-wide dataDepression polygenic scoresPhenome-wide analysisDiverse ancestral backgroundsT2D polygenic scoreAssociated with higher oddsGulf War eraPolygenic architectureAncestral backgroundVeteran ProgramChronic conditionsHigher oddsType 2 diabetesGW veteransVeteransDisease pathogenesisDiverse participantsGulf WarOddsPhysical strengthIllnessComprehensive assessmentAnxietyA novel prognostic framework for HBV-infected hepatocellular carcinoma: insights from ferroptosis and iron metabolism proteomics
Cheng Z, Ren Y, Wang X, Zhang Y, Hua Y, Zhao H, Lu H. A novel prognostic framework for HBV-infected hepatocellular carcinoma: insights from ferroptosis and iron metabolism proteomics. Briefings In Bioinformatics 2025, 26: bbaf216. PMID: 40381315, PMCID: PMC12085197, DOI: 10.1093/bib/bbaf216.Peer-Reviewed Original ResearchConceptsHepatocellular carcinoma patientsHepatocellular carcinomaHCC patientsPrognostic modelAdverse prognosisClinically relevant risk groupsRisk groupsTreatment of hepatocellular carcinomaTumor immune microenvironmentAlpha-fetoprotein levelsHigh-risk HCC patientsRelevant risk groupsPrognosis of HCC patientsHBV-infected hepatocellular carcinomaIron metabolismOverall survivalDifferential expression patternsDistant metastasisImmune microenvironmentTumor sizeLiver cancer progressionTumor differentiationMicrovascular invasionPredictive nomogramValidation cohortA multi-omic approach implicates novel protein dysregulation in post-traumatic stress disorder
Wang J, Liu Y, Li H, Nguyen T, Soto-Vargas J, Wilson R, Wang W, Lam T, Zhang C, Lin C, Lewis D, Glausier J, Holtzheimer P, Friedman M, Williams K, Picciotto M, Nairn A, Krystal J, Duman R, Young K, Zhao H, Girgenti M. A multi-omic approach implicates novel protein dysregulation in post-traumatic stress disorder. Genome Medicine 2025, 17: 43. PMID: 40301990, PMCID: PMC12042318, DOI: 10.1186/s13073-025-01473-1.Peer-Reviewed Original ResearchConceptsPost-traumatic stress disorderDorsolateral prefrontal cortexPsychiatric disordersAutism spectrum disorderPrefrontal cortexDepressive disorderStress disorderGamma-aminobutyric acidGenome-wide association studiesPTSD brainsGenome-wide measurementsStudies of postmortem brainsSubgenual prefrontal cortexDisabling psychiatric disorderMultiple psychiatric disordersPrefrontal cortical areasPTSD casesHuman brain studiesBrain regionsSpectrum disorderGABAergic processesPostmortem brainsMDDProtein co-expression modulesProteomic profilingJointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation
Xu L, Zhou G, Jiang W, Zhang H, Dong Y, Guan L, Zhao H. JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation. Nature Communications 2025, 16: 3841. PMID: 40268942, PMCID: PMC12019179, DOI: 10.1038/s41467-025-59243-x.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenetic risk predictionUK BiobankGenome-wide association study summary statisticsAdmixed American populationsRisk predictionGenetic correlationsNon-European populationsContinental populationsAssociation studiesReal-data applicationBinary traitsTrait predictionSummary statisticsMultiple populationsAmerican populationData-adaptive approachSample sizeData applicationsAOUPopulationBiobankData scenarioTraitsProbabilistic exponential family inverse regression and its applications
Pang D, Zhu R, Zhao H, Wang T. Probabilistic exponential family inverse regression and its applications. Biometrics 2025, 81: ujaf065. PMID: 40407023, DOI: 10.1093/biomtc/ujaf065.Peer-Reviewed Original ResearchConceptsExponential familyDouble exponential familyHigh-dimensional regressionLow-dimensional reductionHierarchical likelihoodData exampleInverse regressionDiscrete predictorsSimulation studyDiscrete dataHigh-dimensional dataParallelizable algorithmContinuous predictorsPresence–absence recordsDimension reductionResponse variablesAccumulation of high dimensional dataHigh-throughput sequencing technologyFactor model frameworkLatent factorsRecords of speciesSequence readsSingle-cell studiesSequencing technologiesCommunity ecology
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