Single-cell and bulk transcriptomics with machine learning decode B cell hub genes and diagnostic biomarkers in anal fistula

Figure 7 Diagnostic performance and predictive model construction based on LINC-PINT, LRBA, and SYK. A: Box plot showing the expression levels of LINC-PINT, LRBA, and SYK in anal fistula (AF) vs control group. LINC-PINT showed significantly higher expression in the AF group; B-D: Receiver operating characteristic (ROC) curves evaluating the diagnostic performance of individual genes: LINC-PINT [area under the ROC curves (AUC)= 0.814], SYK (AUC = 0.844), and LRBA (AUC = 0.778); E: Nomogram constructed using LINC-PINT, LRBA, and SYK to predict AF risk; F: ROC curve evaluating the combined predictive model, showing strong discrimination ability; G: Decision curve analysis indicating favorable clinical utility of the nomogram-based prediction model. ^aP < 0.01; ^bP < 0.001. AF: Anal fistula; ROC: Receiver operating characteristic; AUC: Area under the receiver operating characteristic curve; DCA: Decision curve analysis.