基于免疫球蛋白G N-糖链特征的结直肠腺瘤诊断模型构建与验证OA

Objective:To explore the potential application value of immunoglobulin G(IgG)N-glycan features as biomarkers for the diagnosis of colorectal adenoma(CRA).Methods:A case-control study was conducted,enrolling 90 CRA patients confirmed by colonoscopy and 90 healthy controls from the Second Affiliated Hospital of Shantou University Medical College between March and September 2021.The relative abundance of plasma IgG N-glycans was detected using hydrophilic interaction ultra-performance liquid chromatography(HILIC-UPLC).Characteristic glycans were screened by LASSO regression,and independent associated factors were identified by multivariate logistic regression to construct a nomogram diagnostic model.The discriminative ability and calibration of the model were evaluated using the area under the receiver operating characteristic(ROC)curve(AUC)and calibration curves,respectively.Model stability was validated by the Bootstrap method(1 000 resamples).Results:Among the 22 directly detected IgG N-glycans,12 showed differential abundance between the two groups(adjusted P<0.05 by the Benjamini-Hochberg method).LASSO regression screened six glycans,and multivariate analysis confirmed that GP2(OR=19.00,95%CI:3.32-162.74),GP10(OR=2.24,95%CI:1.18-4.59),GP11(OR=2.33×10-3,95%CI:1.10×10-4-2.37×10-2),and GP19(OR=3.79,95%CI:1.75-9.08)were independently associated with CRA(all P<0.05).The diagnostic model constructed based on these four factors demonstrated good discriminative performance(AUC=0.952,95%CI:0.923-0.980).The average AUC from Bootstrap validation was 0.954(95%CI:0.919-0.981).The calibration curve indicated good consistency between predicted probabilities and actual observations(Brier score=0.088).Conclusion:This study preliminarily confirms that IgG N-glycan features have potential for discriminating CRA.However,due to limitations such as the retrospective design and single-center small sample size,the generalizability and clinical applicability of this diagnostic model require further validation through multicenter prospective studies.