Establishment and bioinformatics analysis of an early prediction model for parkinson's disease based on gut microbiota
Objective To explore the construction and evaluation of an early prediction model for parkinson's disease(PD)in the population based on gut microbiota to conduct functional analysis of gut microbiota macro-genus KO groups to explore potential therapeutic targets for PD.Methods Gut microbiota relative abundance data from the Zenodo database were standardized using Z-Score and dimensionality reduction was performed using ZicoSeq.An adaptive least absolute shrinkage and selection operator(LASSO)binary logistic regression algorithm was employed to establish the prediction model.The performance of the model was evaluated using the area under the receiver operating characteristic(ROC)curve and calibration curve,and clinical utility was assessed using decision curve analysis(DCA).Differential expression genes(DEGs)in gut microbiota macro-genus KO groups were identified using the limma package.Gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)analyses were performed on DEGs.DEGs were further screened using protein-protein interaction networks(PPI),support vector machine recursive feature elimination(SVM-RFE),and random forest(RF).Results The ROC curve and calibration curve of the adaptive LASSO binary logistic regression model showed good predictive performance.The DCA curve showed a significant net benefit of the model.PPI network analysis and machine learning methods identified 6 core DEGs,namely L-arabinose operon Q(araQ),mitochondrial FAD-dependent glyceraldehyde-3-phosphate dehydrogenase,dcd,SRP19,POP5,and ISYNA1.Conclusions The adaptive LASSO binary logistic regression algorithm model has significant advantages in predicting PD,enabling early detection,intervention,and treatment of PD patients.The discovery of relevant core genes provides scientific guidance and assistance for the development of PD treatments.
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