2020
The role of the gut microbiome in cancer-related fatigue: pilot study on epigenetic mechanisms
Xiao C, Fedirko V, Beitler J, Bai J, Peng G, Zhou C, Gu J, Zhao H, Lin IH, Chico CE, Jeon S, Knobf TM, Conneely KN, Higgins K, Shin DM, Saba N, Miller A, Bruner D. The role of the gut microbiome in cancer-related fatigue: pilot study on epigenetic mechanisms. Supportive Care In Cancer 2020, 29: 3173-3182. PMID: 33078326, PMCID: PMC8055716, DOI: 10.1007/s00520-020-05820-3.Peer-Reviewed Original ResearchConceptsCancer-related fatigueGut microbiomePilot studyHigh-fatigue groupLow-fatigue groupsGut-brain axisMultidimensional Fatigue InventoryGut microbiota patternBrain healthCancer patientsFatigue InventoryNeck cancerPurposeRecent evidenceStool samplesImmune responseMicrobiota patternsGut microbiotaInflammationOne monthBrain functionPatientsConclusionsOur resultsFatty acid synthesisEPIC BeadChipDNA methylation changesGut Microbiome Associated with the Psychoneurological Symptom Cluster in Patients with Head and Neck Cancers
Bai J, Bruner DW, Fedirko V, Beitler JJ, Zhou C, Gu J, Zhao H, Lin IH, Chico CE, Higgins KA, Shin DM, Saba NF, Miller AH, Xiao C. Gut Microbiome Associated with the Psychoneurological Symptom Cluster in Patients with Head and Neck Cancers. Cancers 2020, 12: 2531. PMID: 32899975, PMCID: PMC7563252, DOI: 10.3390/cancers12092531.Peer-Reviewed Original ResearchPsychoneurological symptomsCancer patientsGut microbiomeNeck cancerPost-radiation therapyPsychoneurological symptom clusterCommon Terminology CriteriaPatient gut microbiomeGut Microbiome AssociatedFunctional pathway analysisTerminology CriteriaAdverse eventsOutcomes versionDiscriminant analysis effect sizeLinear discriminant analysis effect sizeStool specimensPatientsSymptom clustersCancer treatmentPilot studyGreater decreaseVitamin metabolismPotential rolePreliminary dataSignificant differencesImproving the Diagnosis of Phenylketonuria by Using a Machine Learning–Based Screening Model of Neonatal MRM Data
Zhu Z, Gu J, Genchev G, Cai X, Wang Y, Guo J, Tian G, Lu H. Improving the Diagnosis of Phenylketonuria by Using a Machine Learning–Based Screening Model of Neonatal MRM Data. Frontiers In Molecular Biosciences 2020, 7: 115. PMID: 32733913, PMCID: PMC7358370, DOI: 10.3389/fmolb.2020.00115.Peer-Reviewed Original ResearchNerve developmentCommon genetic metabolic disorderShanghai Children's HospitalMetabolic patternsLogistic regression analysis modelNewborn Screening CenterPositive predictive valueGenetic metabolic disordersPediatric patientsChildren's HospitalPatient recallMetabolic disordersScreening centerHealth providersPredictive valueDiagnosis of phenylketonuriaScreening methodDevelopmental delayFalse positive rateRegression analysis modelHigh false positive ratePhenylketonuriaPatientsHospitalScreening dataElevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors
Schalper KA, Carleton M, Zhou M, Chen T, Feng Y, Huang SP, Walsh AM, Baxi V, Pandya D, Baradet T, Locke D, Wu Q, Reilly TP, Phillips P, Nagineni V, Gianino N, Gu J, Zhao H, Perez-Gracia JL, Sanmamed MF, Melero I. Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors. Nature Medicine 2020, 26: 688-692. PMID: 32405062, PMCID: PMC8127102, DOI: 10.1038/s41591-020-0856-x.Peer-Reviewed Original ResearchMeSH KeywordsAntibodies, MonoclonalAntineoplastic Agents, ImmunologicalBiomarkers, PharmacologicalBiomarkers, TumorCell Cycle CheckpointsCohort StudiesFemaleHumansInterleukin-8MaleNeoplasmsNeutrophil InfiltrationNeutrophilsPrognosisProtein Kinase InhibitorsRetrospective StudiesSurvival AnalysisTreatment FailureTumor MicroenvironmentUp-RegulationConceptsSerum IL-8 levelsImmune checkpoint inhibitorsIL-8 levelsAdvanced cancerSerum interleukin-8 levelsPhase 3 clinical trialsSerum interleukin-8Interleukin-8 levelsLarge-scale retrospective analysisNeutrophil infiltrationWorse prognosisClinical benefitPoor outcomeIndependent biomarkerTumor immunobiologyClinical trialsInterleukin-8Retrospective analysisPatientsCancerInhibitorsIpilimumabNivolumabEverolimusPrognosis
2019
Genomic analysis of a spinal muscular atrophy (SMA) discordant family identifies a novel mutation in TLL2, an activator of growth differentiation factor 8 (myostatin): a case report
Jiang J, Huang J, Gu J, Cai X, Zhao H, Lu H. Genomic analysis of a spinal muscular atrophy (SMA) discordant family identifies a novel mutation in TLL2, an activator of growth differentiation factor 8 (myostatin): a case report. BMC Medical Genomics 2019, 20: 204. PMID: 31888525, PMCID: PMC6938020, DOI: 10.1186/s12881-019-0935-3.Peer-Reviewed Original ResearchConceptsSMA casesHeterozygous mutationsInternational SMA ConsortiumBackgroundSpinal muscular atrophyDifferent clinical typesRare neuromuscular disorderCopies of SMN2Compound heterozygous mutationsWhole-exome sequencingSeverity of SMAGenomic analysisFemale patientsMale patientsClinical typesCase reportAccurate counselingRare caseMouse modelDiagnostic criteriaMuscular functionPatientsGrowth differentiation factor 8Neuromuscular disordersSMA patientsMuscular atrophy
2016
Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures
Zheng B, Liu J, Gu J, Du J, Wang L, Gu S, Cheng J, Yang J, Lu H. Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures. PLOS ONE 2016, 11: e0164570. PMID: 27776138, PMCID: PMC5077123, DOI: 10.1371/journal.pone.0164570.Peer-Reviewed Original ResearchConceptsClinical dataGene expression signaturesClinical informationMalignant thyroid nodulesThyroid nodulesExpression signaturesNovel diagnostic testsClinical characteristicsClinical featuresPreoperative diagnosisThyroid carcinomaThyroid tumorsPredictive sensitivityDifferent hospitalsDiagnostic testsThyroid samplesPatientsGut microbiota community adaption during young children fecal microbiota transplantation by 16s rDNA sequencing
Gu J, Wang Y, Liu S, Yu G, Zhang T, Lu H. Gut microbiota community adaption during young children fecal microbiota transplantation by 16s rDNA sequencing. Neurocomputing 2016, 206: 66-72. DOI: 10.1016/j.neucom.2016.01.095.Peer-Reviewed Original ResearchFecal microbiota transplantationMicrobiota transplantationGastrointestinal disordersGastrointestinal diseasesChronic gastrointestinal diseasePediatric gastrointestinal disordersFecal microbiota compositionUseful treatment methodRDNA sequencing technologyAdult patientsFMT treatmentYounger patientsFecal transplantationHealthy donorsIntestinal microbiotaMicrobiota compositionPatientsTransplantationInterindividual variabilityFecal microbiotaIntestinal environmentYoung childrenDiseased individualsDiseaseTreatment methods