2025
TNFRSF1A and NCF1 May Act as Hub Genes in Mastitis
Ekhtiyari M, Yousefi M, Samadian F, Ghaderi‐Zefrehei M, Neysi S, Shamsabadi J, Javanmard A, Shahriarpour H, Lesch B. TNFRSF1A and NCF1 May Act as Hub Genes in Mastitis. Veterinary Medicine And Science 2025, 11: e70278. PMID: 40028770, PMCID: PMC11875065, DOI: 10.1002/vms3.70278.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCattleFemaleGene Regulatory NetworksMastitis, BovineMicroRNAsReceptors, Tumor Necrosis Factor, Type IConceptsBovine mastitisHub genesMastitis susceptibilityExpression datasetsMolecular mechanismsMultiple expression datasetsProtein-protein interactionsCo-expression network analysisSusceptibility to bovine mastitisRegulatory networksTarget key genesKey genesDairy industryMolecular playersNetwork analysisMRNA-miRNACo-expressionGenesNCF1MastitisBacterial infectionsTherapeutic targetTNFRSF1AImmune responseSusceptibilityA Selective Review of Network Analysis Methods for Gene Expression Data
Li R, Yi H, Ma S. A Selective Review of Network Analysis Methods for Gene Expression Data. Methods In Molecular Biology 2025, 2880: 293-307. PMID: 39900765, DOI: 10.1007/978-1-0716-4276-4_14.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansSoftwareConceptsGene Expression DataGene expression networksExpression DataDownstream analysisExpression networksGene expressionBiological processesGenesMolecular mechanismsBiological implicationsHigh-throughput profiling techniquesBiological findingsGlobal viewComplex interactionsProfiling techniquesRegulation
2024
Mapping the gene space at single-cell resolution with gene signal pattern analysis
Venkat A, Leone S, Youlten S, Fagerberg E, Attanasio J, Joshi N, Perlmutter M, Krishnaswamy S. Mapping the gene space at single-cell resolution with gene signal pattern analysis. Nature Computational Science 2024, 4: 955-977. PMID: 39706866, DOI: 10.1038/s43588-024-00734-0.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsCell CommunicationComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansSingle-Cell AnalysisTranscriptomeConceptsSingle-cell dataGene spaceGene representationSimulated single-cell dataGene co-expression modulesCell-cell graphCharacterization of genesGene-gene interactionsCo-expression modulesCell-cell communicationCellular state spaceSingle-cell resolutionSingle-cell sequencing analysisSequence analysisGenesBiological tasksSpatial transcriptomicsGraph signal processing approachSignal pattern analysisPattern analysisSignal processing approachComputational methodsTranscriptomeCGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection
Li H, Han Z, Sun Y, Wang F, Hu P, Gao Y, Bai X, Peng S, Ren C, Xu X, Liu Z, Chen H, Yang Y, Bo X. CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection. Nature Communications 2024, 15: 5997. PMID: 39013885, PMCID: PMC11252405, DOI: 10.1038/s41467-024-50426-6.Peer-Reviewed Original ResearchComputational reassessment of RNA-seq data reveals key genes in active tuberculosis
Arya R, Shakya H, Chaurasia R, Kumar S, Vinetz J, Kim J. Computational reassessment of RNA-seq data reveals key genes in active tuberculosis. PLOS ONE 2024, 19: e0305582. PMID: 38935691, PMCID: PMC11210783, DOI: 10.1371/journal.pone.0305582.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkersComputational BiologyDatabases, GeneticGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansProtein Interaction MapsRNA-SeqROC CurveTuberculosisConceptsMolecular Complex DetectionProtein-protein interactionsDeregulated genesGene OntologyRNA-seq dataGene Expression Omnibus (GEO) databaseIncreasing prevalence of multidrug-resistantGEO2R online toolPrevalence of multidrug resistancePathway enrichment analysisExpression levelsPatterns of variationGene expression levelsArea under curveInnate immune responseGene networksCore genesMicroarray datasetsSTRING databaseTranscript levelsEnrichment analysisGenesInterferon signalingInterferon-gamma signalingResponse to Mtb infectionAccelerated evolution in the human lineage led to gain and loss of transcriptional enhancers in the RBFOX1 locus
Berasain L, Beati P, Trigila A, Rubinstein M, Franchini L. Accelerated evolution in the human lineage led to gain and loss of transcriptional enhancers in the RBFOX1 locus. Science Advances 2024, 10: eadl1049. PMID: 38924416, PMCID: PMC11204294, DOI: 10.1126/sciadv.adl1049.Peer-Reviewed Original ResearchConceptsTranscriptional enhancersGoal of evolutionary biologyHuman lineageZebrafish reporter assayGene regulatory networksPotential regulatory elementsHuman-specific traitsChimpanzee sequencesRBFOX1 locusRegulatory elementsRegulatory networksRegulatory modificationsEvolutionary biologyAccelerated evolutionTarget genesGene expressionGenesDevelopmental stagesLociLineagesReporter assayExpressionRBFOX1SplicingTemporal changesCross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain
Wen C, Margolis M, Dai R, Zhang P, Przytycki P, Vo D, Bhattacharya A, Matoba N, Tang M, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker R, Chatzinakos C, Clarke D, Pratt H, Peters M, Gerstein M, Daskalakis N, Weng Z, Jaffe A, Kleinman J, Hyde T, Weinberger D, Bray N, Sestan N, Geschwind D, Roeder K, Gusev A, Pasaniuc B, Stein J, Love M, Pollard K, Liu C, Gandal M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clement A, Collado-Torres L, Cooper G, Crawford G, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Fullard J, Galani K, Galeev T, Gaynor S, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Haroutunian V, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Hoffman G, Huang Y, Huuki-Myers L, Hwang A, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollen A, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Roussos P, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Voloudakis G, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain. Science 2024, 384: eadh0829. PMID: 38781368, DOI: 10.1126/science.adh0829.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide association study lociSplicing quantitative trait lociQuantitative trait lociSplicing regulationCross-ancestryTrait lociAssociation studiesRegulatory elementsCellular contextHuman brainTranscriptome regulationCoexpression networkRisk genesAutism spectrum disorderGenesCellular heterogeneityComprehensive landscapeSpectrum disorderIsoformsSplicingIncreased cellular heterogeneityLociNeuronal maturationRegulationSingle-cell genomics and regulatory networks for 388 human brains
Emani P, Liu J, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee C, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken T, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard J, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman G, Huang A, Jiang Y, Jin T, Jorstad N, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran J, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan A, Riesenmy T, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini K, Wamsley B, Wang G, Xia Y, Xiao S, Yang A, Zheng S, Gandal M, Lee D, Lein E, Roussos P, Sestan N, Weng Z, White K, Won H, Girgenti M, Zhang J, Wang D, Geschwind D, Gerstein M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, Brennand K, Capauto D, Champagne F, Chatzinakos C, Chen H, Cheng L, Chess A, Chien J, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duong D, Eagles N, Edelstein J, Galani K, Girdhar K, Goes F, Greenleaf W, Guo H, Guo Q, Hadas Y, Hallmayer J, Han X, Haroutunian V, He C, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hyde T, Iatrou A, Inoue F, Jajoo A, Jiang L, Jin P, Jops C, Jourdon A, Kellis M, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Li J, Li M, Lin X, Liu S, Liu C, Loupe J, Lu D, Ma L, Mariani J, Martinowich K, Maynard K, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Mukamel E, Nairn A, Nemeroff C, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Pinto D, Pochareddy S, Pollard K, Pollen A, Przytycki P, Purmann C, Qin Z, Qu P, Raj T, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Voloudakis G, Wang T, Wang S, Wang Y, Wei Y, Weimer A, Weinberger D, Wen C, Whalen S, Willsey A, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang Y, Ziffra R, Zeier Z, Zintel T. Single-cell genomics and regulatory networks for 388 human brains. Science 2024, 384: eadi5199. PMID: 38781369, PMCID: PMC11365579, DOI: 10.1126/science.adi5199.Peer-Reviewed Original ResearchMeSH KeywordsAgingBrainCell CommunicationChromatinGene Regulatory NetworksGenomicsHumansMental DisordersPrefrontal CortexQuantitative Trait LociSingle-Cell AnalysisConceptsSingle-cell genomicsSingle-cell expression quantitative trait locusExpression quantitative trait lociDrug targetsQuantitative trait lociPopulation-level variationSingle-cell expressionCell typesDisease-risk genesTrait lociGene familyRegulatory networksGene expressionCell-typeMultiomics datasetsSingle-nucleiGenomeGenesCellular changesHeterogeneous tissuesExpressionCellsChromatinLociMultiomicsCritical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex
Cote A, Young H, Huckins L. Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex. Human Genetics And Genomics Advances 2024, 5: 100311. PMID: 38773772, PMCID: PMC11214266, DOI: 10.1016/j.xhgg.2024.100311.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociGene co-expressionCo-expressionExpression quantitative trait locus methodGenetic variantsComplex trait heritabilityMultiple testing burdenGene-based testsQuantitative trait lociTrans-eQTLsCis-eQTLsRegulatory variationSequencing datasetsTrait lociGene regulationTrait heritabilityGene functionGene modulesReal-data applicationModule genesGenesTesting burdenDorsolateral prefrontal cortexVariantsComparison to prior studiesSupervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Hodgson L, Li Y, Iturria-Medina Y, Stratton J, Wolf G, Krishnaswamy S, Bennett D, Bzdok D. Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression. Communications Biology 2024, 7: 591. PMID: 38760483, PMCID: PMC11101463, DOI: 10.1038/s42003-024-06273-8.Peer-Reviewed Original ResearchMeSH KeywordsAlzheimer DiseaseBrainDisease ProgressionGene Expression ProfilingGene Regulatory NetworksHumansMicrogliaTranscriptomeConceptsGene programAlzheimer's diseaseLate-onset Alzheimer's diseaseAD risk lociCell type-specificSingle-nucleus RNA sequencingRisk lociAD brainAlzheimer's disease progressionSnRNA-seqRNA sequencingAD pathophysiologySignaling cascadesTranscriptome modulationProgressive neurodegenerative diseaseCell-typeGWASNeurodegenerative diseasesNeuronal lossGlial cellsTranscriptomeLociGenesPseudo-trajectoriesDisease progressionSingle-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression
Gong Y, Xu J, Wu M, Gao R, Sun J, Yu Z, Zhang Y. Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression. Cell Reports Methods 2024, 4: 100742. PMID: 38554701, PMCID: PMC11045878, DOI: 10.1016/j.crmeth.2024.100742.Peer-Reviewed Original ResearchMeSH KeywordsAlzheimer DiseaseBayes TheoremCluster AnalysisDisease ProgressionGene Expression ProfilingGene Regulatory NetworksHumansSingle-Cell AnalysisTranscriptomeConceptsSnRNA-seq dataGene modulesAD progressionPathogenesis of Alzheimer's diseaseBiologically interpretable resultsSingle-cell data analysisGene regulatory changesFunctional gene modulesGene coexpression patternsAlzheimer's diseaseSingle-cell levelSnRNA-seqBiclustering methodsPolygenic diseaseBatch effectsDropout eventsCoexpression patternsNetwork biomarkersCell typesBiclusteringCellsGenesScRNABiologyComparative analysisStochastic modeling of a gene regulatory network driving B cell development in germinal centers
Koshkin A, Herbach U, Martínez M, Gandrillon O, Crauste F. Stochastic modeling of a gene regulatory network driving B cell development in germinal centers. PLOS ONE 2024, 19: e0301022. PMID: 38547073, PMCID: PMC10977792, DOI: 10.1371/journal.pone.0301022.Peer-Reviewed Original ResearchMeSH KeywordsB-LymphocytesGene Expression ProfilingGene Regulatory NetworksGerminal CenterHumansSystems BiologyConceptsGene regulatory network structureGene regulatory networksGene expression dataExpression dataB cell differentiationSingle-cellAssociated with cell developmentGC B cell differentiationStages of B-cell differentiationB cell developmentSelection of B cellsGene regulationRegulatory networksTranscriptome dataSystems biologyHigh-affinity antibodiesRegulatory mechanismsCell developmentGenesAdaptive immune systemMRNA distributionPlasmablast stageGerminal centersDifferentiationImmune systemUnveiling biomarkers and therapeutic targets in IgA nephropathy through large-scale blood transcriptome analysis
Gan T, Qu L, Qu S, Qi Y, Zhang Y, Wang Y, Li Y, Liu L, Shi S, Lv J, Zhang H, Peng Y, Zhou X. Unveiling biomarkers and therapeutic targets in IgA nephropathy through large-scale blood transcriptome analysis. International Immunopharmacology 2024, 132: 111905. PMID: 38552291, DOI: 10.1016/j.intimp.2024.111905.Peer-Reviewed Original ResearchConceptsPeripheral blood mononuclear cellsIgAN patientsIgA nephropathyBlood transcriptome analysisNK cell mediated cytotoxicityChinese IgAN patientsImmune cell profilesB-cell receptor signalingBlood mononuclear cellsModel of IgANEvaluate clinical significancePotential specific markerPersonalized treatment optionsCell mediated cytotoxicityBlood transcriptome profilesClinically significant genesTreatment optionsMononuclear cellsClinical dataClinical significanceMediated cytotoxicityHealthy controlsIgANImmune responsePotential therapeutic agents
2023
From mouse to human
Mani A. From mouse to human. ELife 2023, 12: e94382. PMID: 38060304, PMCID: PMC10703438, DOI: 10.7554/elife.94382.Peer-Reviewed Original ResearchAnimalsAtherosclerosisCoronary Artery DiseaseGene Regulatory NetworksGenome-Wide Association StudyGenomicsHumansMiceWhen Development of the Alveolar Gas Exchange Unit Fails: Universal Single-Cell Lessons from Rare Monogenic Disorders
Schupp J, Kaminski N. When Development of the Alveolar Gas Exchange Unit Fails: Universal Single-Cell Lessons from Rare Monogenic Disorders. American Journal Of Respiratory And Critical Care Medicine 2023, 208: 652-654. PMID: 37555730, PMCID: PMC10515565, DOI: 10.1164/rccm.202307-1271ed.Commentaries, Editorials and LettersA Glucocorticoid-Sensitive Hippocampal Gene Network Moderates the Impact of Early-Life Adversity on Mental Health Outcomes
Arcego D, Buschdorf J, O'Toole N, Wang Z, Barth B, Pokhvisneva I, Rayan N, Patel S, de Mendonça Filho E, Lee P, Tan J, Koh M, Sim C, Parent C, de Lima R, Clappison A, O'Donnell K, Dalmaz C, Arloth J, Provençal N, Binder E, Diorio J, Silveira P, Meaney M. A Glucocorticoid-Sensitive Hippocampal Gene Network Moderates the Impact of Early-Life Adversity on Mental Health Outcomes. Biological Psychiatry 2023, 95: 48-61. PMID: 37406925, DOI: 10.1016/j.biopsych.2023.06.028.Peer-Reviewed Original ResearchConceptsHippocampal dentate gyrusDentate gyrusPsychiatric disordersHuman hippocampal cellsBrain gray matter densityEarly adversitySingle nucleotide polymorphismsMultiple psychiatric conditionsEarly life adversityGray matter densityAdult female macaquesMental health outcomesTranscriptional activityGlucocorticoid exposureNucleotide polymorphismsLater psychosisStress mediatorsHippocampal cellsPsychotic disordersAnimal modelsHealth outcomesPsychiatric conditionsSignificant associationFemale macaquesMental healthReversing pathological cell states: the road less travelled can extend the therapeutic horizon
Kholodenko B, Kolch W, Rukhlenko O. Reversing pathological cell states: the road less travelled can extend the therapeutic horizon. Trends In Cell Biology 2023, 33: 913-923. PMID: 37263821, PMCID: PMC10593090, DOI: 10.1016/j.tcb.2023.04.004.Peer-Reviewed Original ResearchIntegrated analysis of competitive endogenous RNA networks in elder patients with non-small cell lung cancer
Chen Z, Yu F, Zhu B, Li Q, Yu Y, Zong F, Liu W, Zhang M, Wu S. Integrated analysis of competitive endogenous RNA networks in elder patients with non-small cell lung cancer. Medicine 2023, 102: e33192. PMID: 36897674, PMCID: PMC9997791, DOI: 10.1097/md.0000000000033192.Peer-Reviewed Original ResearchConceptsMRNA ceRNA networkEndogenous RNA (ceRNA) networkCeRNA networkMessenger RNAsRNA networkLncRNA-miRNACompetitive endogenous RNA (ceRNA) networkLong non-coding RNAsPotential ceRNA networksNon-coding RNAsFunctions of DEmRNAsCancer-related processesCancer Genome AtlasGenome analysisGene OntologyKyoto EncyclopediaDevelopment of NSCLCGene Expression Omnibus (GEO) cohortNovel insightsGenome AtlasSurvival packageExpression levelsIntegrated analysisDEmRNAsMiRNAsThe Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks
Ben Guebila M, Wang T, Lopes-Ramos C, Fanfani V, Weighill D, Burkholz R, Schlauch D, Paulson J, Altenbuchinger M, Shutta K, Sonawane A, Lim J, Calderer G, van IJzendoorn D, Morgan D, Marin A, Chen C, Song Q, Saha E, DeMeo D, Padi M, Platig J, Kuijjer M, Glass K, Quackenbush J. The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks. Genome Biology 2023, 24: 45. PMID: 36894939, PMCID: PMC9999668, DOI: 10.1186/s13059-023-02877-1.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsComputational BiologyGene Regulatory NetworksHumansMultiomicsNeoplasmsSoftware
2022
The microRNA-3622 family at the 8p21 locus exerts oncogenic effects by regulating the p53-downstream gene network in prostate cancer progression
Zhang Y, Xu Z, Wen W, Liu Z, Zhang C, Li M, Hu F, Wei S, Bae S, Zhou J, Liu R, Wang L. The microRNA-3622 family at the 8p21 locus exerts oncogenic effects by regulating the p53-downstream gene network in prostate cancer progression. Oncogene 2022, 41: 3186-3196. PMID: 35501464, PMCID: PMC9177620, DOI: 10.1038/s41388-022-02289-8.Peer-Reviewed Original ResearchConceptsGene networksHuman prostate cancerDual-luciferase assayRepression of p53 signalingInvasion of human prostate cancer cellsOncogenic functionHuman prostate cancer cellsOncogenic effectsCell proliferationHuman prostate cancer cell linesProstate cancer cell linesCRISPR interferenceControl apoptosisCancer cell linesProstate cancer cellsTumor progressionInvasion in vitroP53 signalingUpregulation of vimentinMetastasis in vivoHuman prostate cancer tissuesCell cycleImmunoprecipitation assaysC-mycCell migration
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