Bioinformatics analysis and qRT-PCR validation of iron metabolism-related genes in pediatric asthma
Fig 7
(A). The diagnostic value of the 15 IMRDEGs included in the logistic regression model for PA is visualized through a forest plot. The horizontal axis for each gene represents the odds ratio and its 95% confidence interval; the red dots indicate the p-value of the gene, reflecting its statistical significance in the model. (B). The trend of accuracy changes in the SVM model. As the number of genes increases, the accuracy gradually rises, with the best accuracy being observed for four genes (0.6835). (C). Changes in the trend of error rates. As the number of features (genes) increases, the error rate gradually decreases, indicating that the model performs optimally when four genes are selected, with the error rate reaching its lowest point (0.365). (D). Visualization of the LASSO regression model. The error changes of the model under different regularization parameters (λ) are depicted, with the error decreasing as λ increases; the optimal λ value is marked as the one that minimizes the cross-validation error. €. The coefficient changes of each gene in the LASSO regression model. Different colors represent different genes; as the λ increases, the coefficients of each gene gradually decrease, illustrating the feature selection process. This figure demonstrates the importance of the 15 IMRDEGs in the diagnosis of PA and indicates the diagnostic potential of four key genes (C19orf12, IREB2, XK, and GDF15). PA: Pediatric asthma, LASSO: Least Absolute Shrinkage and Selection Operator, IMRDEGs: Iron Metabolism-Related Differentially Expressed Genes, SVM: Support vector machine.