Predictive model for vestibular schwannoma risk based on LASSO regression-screened plasma immune markers

KUAI Guohu; LIAN Minghao; LI Yandong; ZHU Guohua; MEMETIL Mijit; GENG Dangmurengjiafu

doi:10.3969/j.issn.1671-2897.2026.02.004

Abstract：Objective To construct a model for predicting the risk of vestibular schwannoma(VS)using plasma immune markers based on the Dryad database.Methods A secondary analysis was performed on patient data from the Dryad database.Participants were divided into a VS group(n=213)and a non-VS group(n=113).Univariate analysis,multivariate Logistic regression,and LASSO regression were used to screen risk factors and construct a nomogram prediction model.Results Univariate analysis revealed statistically significant differences(P<0.05)in plasma markers including TNF-R2,MIF,SDF-1α,IL-2R,MCP-2,CD30,IL-16,TWEAK,BLC,and MCP-3 between the two groups.Multivariate Logistic regression combined with LASSO regression identified SDF-1α[OR(95%CI)=1.001(1.000～1.001)],IL-2R[OR(95%CI)=1.002(1.001～1.003)],BLC[OR(95%CI)=1.015(1.006-1.023)],and MCP-3[OR(95%CI)=1.294(1.159-1.444)]as independent risk factors for VS.A nomogram model was constructed using these four variables.The analysis of the receiver operating characteristic(ROC)curve revealed an area under the curve(AUC)value of 0.856(95%CI:0.815-0.897),which suggested a high level of predictive accuracy.The model exhibited strong predictive accuracy,as evidenced by a well-calibrated curve that closely aligned with the ideal line,achieved through bootstrap resampling with 1 000 iterations.Furthermore,decision curve analysis(DCA)indicated robust clinical utility,with the decision curve surpassing both the"None"and"All"reference lines across a threshold probability range of 0-60%.Conclusions The nomogram prediction model for VS risk,constructed on plasma immune markers screened by LASSO regression,can provide a reference for the early clinical diagnosis.