Zebrafish and human genomes are highly homologous;however,despite this genomic similarity,adult zebrafish can achieve neuronal proliferation,regeneration and functional restoration within 6–8 weeks after spinal cord ...Zebrafish and human genomes are highly homologous;however,despite this genomic similarity,adult zebrafish can achieve neuronal proliferation,regeneration and functional restoration within 6–8 weeks after spinal cord injury,whereas humans cannot.To analyze differentially expressed zebrafish genes between axon-regenerated neurons and axon-non-regenerated neurons after spinal cord injury,and to explore the key genes and pathways of axonal regeneration after spinal cord injury,microarray GSE56842 was analyzed using the online tool,GEO2R,in the Gene Expression Omnibus database.Gene ontology and protein-protein interaction networks were used to analyze the identified differentially expressed genes.Finally,we screened for genes and pathways that may play a role in spinal cord injury repair in zebrafish and mammals.A total of 636 differentially expressed genes were obtained,including 255 up-regulated and 381 down-regulated differentially expressed genes in axon-regenerated neurons.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment results were also obtained.A protein-protein interaction network contained 480 node genes and 1976 node connections.We also obtained the 10 hub genes with the highest correlation and the two modules with the highest score.The results showed that spectrin may promote axonal regeneration after spinal cord injury in zebrafish.Transforming growth factor beta signaling may inhibit repair after spinal cord injury in zebrafish.Focal adhesion or tight junctions may play an important role in the migration and proliferation of some cells,such as Schwann cells or neural progenitor cells,after spinal cord injury in zebrafish.Bioinformatic analysis identified key candidate genes and pathways in axonal regeneration after spinal cord injury in zebrafish,providing targets for treatment of spinal cord injury in mammals.展开更多
BACKGROUND Macrotrabecular-massive hepatocellular carcinoma(MTM-HCC)is closely related to aggressive phenotype,gene mutation,carcinogenic pathway,and immunohistochemical markers and is a strong independent predictor o...BACKGROUND Macrotrabecular-massive hepatocellular carcinoma(MTM-HCC)is closely related to aggressive phenotype,gene mutation,carcinogenic pathway,and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis.With the development of imaging technology,successful applications of contrast-enhanced magnetic resonance imaging(MRI)have been reported in identifying the MTM-HCC subtype.Radiomics,as an objective and beneficial method for tumour evaluation,is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine.AIM To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms.METHODS This retrospective study enrolled 232(training set,162;test set,70)hepatocellular carcinoma patients from April 2018 to September 2021.A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI,followed by dimension reduction of these features.Logistic regression(LR),K-nearest neighbour(KNN),Bayes,Tree,and support vector machine(SVM)algorithms were used to select the best radiomics signature.We used the relative standard deviation(RSD)and bootstrap methods to quantify the stability of these five algorithms.The algorithm with the lowest RSD represented the best stability,and it was used to construct the best radiomics model.Multivariable logistic analysis was used to select the useful clinical and radiological features,and different predictive models were established.Finally,the predictive performances of the different models were assessed by evaluating the area under the curve(AUC).RESULTS The RSD values based on LR,KNN,Bayes,Tree,and SVM were 3.8%,8.6%,4.3%,17.7%,and 17.4%,respectively.Therefore,the LR machine learning algorithm was selected to construct the best radiomics signature,which performed well with AUCs of 0.766 and 0.739 in the training and test sets,respectively.In the multivariable analysis,age[odds ratio(OR)=0.956,P=0.034],alphafetoprotein(OR=10.066,P<0.001),tumour size(OR=3.316,P=0.002),tumour-to-liver apparent diffusion coefficient(ADC)ratio(OR=0.156,P=0.037),and radiomics score(OR=2.923,P<0.001)were independent predictors of MTM-HCC.Among the different models,the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model(AUCs:0.888 vs 0.836,P=0.046)and radiological model(AUCs:0.796 vs 0.688,P=0.012),respectively,in the training set,highlighting the improved predictive performance of radiomics.The nomogram performed best,with AUCs of 0.896 and 0.805 in the training and test sets,respectively.CONCLUSION The nomogram containing radiomics,age,alpha-fetoprotein,tumour size,and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype.展开更多
基金supported by the State Key Program of National Natural Science Foundation of China,No.81330042(to SQF)the International Cooperation Program of the National Natural Science Foundation of China,No.81620108018(to SQF)
文摘Zebrafish and human genomes are highly homologous;however,despite this genomic similarity,adult zebrafish can achieve neuronal proliferation,regeneration and functional restoration within 6–8 weeks after spinal cord injury,whereas humans cannot.To analyze differentially expressed zebrafish genes between axon-regenerated neurons and axon-non-regenerated neurons after spinal cord injury,and to explore the key genes and pathways of axonal regeneration after spinal cord injury,microarray GSE56842 was analyzed using the online tool,GEO2R,in the Gene Expression Omnibus database.Gene ontology and protein-protein interaction networks were used to analyze the identified differentially expressed genes.Finally,we screened for genes and pathways that may play a role in spinal cord injury repair in zebrafish and mammals.A total of 636 differentially expressed genes were obtained,including 255 up-regulated and 381 down-regulated differentially expressed genes in axon-regenerated neurons.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment results were also obtained.A protein-protein interaction network contained 480 node genes and 1976 node connections.We also obtained the 10 hub genes with the highest correlation and the two modules with the highest score.The results showed that spectrin may promote axonal regeneration after spinal cord injury in zebrafish.Transforming growth factor beta signaling may inhibit repair after spinal cord injury in zebrafish.Focal adhesion or tight junctions may play an important role in the migration and proliferation of some cells,such as Schwann cells or neural progenitor cells,after spinal cord injury in zebrafish.Bioinformatic analysis identified key candidate genes and pathways in axonal regeneration after spinal cord injury in zebrafish,providing targets for treatment of spinal cord injury in mammals.
基金Supported by Zhejiang Provincial Natural Science Foundation of China,No.LTGY23H180017Medical Science and Technology Project of Zhejiang Province,No.2023KY503.
文摘BACKGROUND Macrotrabecular-massive hepatocellular carcinoma(MTM-HCC)is closely related to aggressive phenotype,gene mutation,carcinogenic pathway,and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis.With the development of imaging technology,successful applications of contrast-enhanced magnetic resonance imaging(MRI)have been reported in identifying the MTM-HCC subtype.Radiomics,as an objective and beneficial method for tumour evaluation,is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine.AIM To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms.METHODS This retrospective study enrolled 232(training set,162;test set,70)hepatocellular carcinoma patients from April 2018 to September 2021.A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI,followed by dimension reduction of these features.Logistic regression(LR),K-nearest neighbour(KNN),Bayes,Tree,and support vector machine(SVM)algorithms were used to select the best radiomics signature.We used the relative standard deviation(RSD)and bootstrap methods to quantify the stability of these five algorithms.The algorithm with the lowest RSD represented the best stability,and it was used to construct the best radiomics model.Multivariable logistic analysis was used to select the useful clinical and radiological features,and different predictive models were established.Finally,the predictive performances of the different models were assessed by evaluating the area under the curve(AUC).RESULTS The RSD values based on LR,KNN,Bayes,Tree,and SVM were 3.8%,8.6%,4.3%,17.7%,and 17.4%,respectively.Therefore,the LR machine learning algorithm was selected to construct the best radiomics signature,which performed well with AUCs of 0.766 and 0.739 in the training and test sets,respectively.In the multivariable analysis,age[odds ratio(OR)=0.956,P=0.034],alphafetoprotein(OR=10.066,P<0.001),tumour size(OR=3.316,P=0.002),tumour-to-liver apparent diffusion coefficient(ADC)ratio(OR=0.156,P=0.037),and radiomics score(OR=2.923,P<0.001)were independent predictors of MTM-HCC.Among the different models,the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model(AUCs:0.888 vs 0.836,P=0.046)and radiological model(AUCs:0.796 vs 0.688,P=0.012),respectively,in the training set,highlighting the improved predictive performance of radiomics.The nomogram performed best,with AUCs of 0.896 and 0.805 in the training and test sets,respectively.CONCLUSION The nomogram containing radiomics,age,alpha-fetoprotein,tumour size,and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype.
