Fig. 1

Construction of a diagnostic model for bladder cancer based on six critical differentially expressed GTs. A, B Differentially expressed GTs in bladder cancer were screened using the R packages “wilcox” (A) and “limma” (B) with the TCGA–BLCA data set obtained from the TCGA database. C Venn analysis was performed to identify common GTs screened by both the “wilcox” and “limma” methods in bladder cancer. D Four machine learning algorithms (GBM, LASSO, RF, and XGB) was applied to screen essential differentially expressed GTs with diagnostic value for establishing the bladder cancer diagnostic model. E Venn analysis was used to visualize the common genes selected by the four machine learning algorithms (GBM, LASSO, RF, and XGB). F Diagnostic model for bladder cancer was established using six common critical GTs based on the TCGA–BLCA data set as the training set, with diagnostic accuracy evaluated through ROC curves. G Diagnostic accuracy of the constructed model was validated using the GSE188715 data set as the testing set, as assessed by ROC curves