Autoantibodies Against Multiple Tumor-associated Antigens As Diagnostic Biomarkers In The Detection Of Gastric Cancer

The morbidity and mortality of gastric cancer(GC)are ranked as the second among all malignant tumors in China,and the 5 year survival rate is less than 20%.At present,gastroscopy is a traditional technique for the diagnosis of GC,which is a uncomfortable and invasive approach,and not suitable for the screening of asymptomatic high-risk population.Tumor associated antigents(TAAs)and anti-TAAs autoantibodies are stable in the serum of cancer patients and can be detected several months or years before the onset of symptoms.Therefore,TAAs and anti-TAAs autoantibodies could serve as potential biomarkers in the early immunodiagnosis of cancers.PurposeThis study aimed to identify a panel of multiple TAAs for diagnosing GC,nine TAAs(p53,p62,c-Myc,PTEN,IMP1,p16,14-3-3ξ,MDM2 and NPM1)were selected to detect corresponding autoantibodies in the sera from GC patients and healthy controls.A logistic regression model predicting the risk of being diagnosed with GC in the training cohort(n=558)was construsted and then validated in an independent cohort(n=372).The ultimate purpose of this study was to provide theoretical basis and technical support for the establishment of a non-invasive diagnostic method for the immunodiagnosis of GC.Methods1.Expression and purification of PTEN recombinant protein:The recombinant plasmid(pET-30a(+)-PTEN)was transformed into expression host E.col BL21(DE3)competent cell to induce expression.Then the PTEN recombinant protein was puried by Ni2+ affinity chromatography.2.Diagnostic performance of single autoantibody:The anti-TAAs autoantibodies in 930 serum samples were detected by Enzyme linked immunosorbent assay(ELISA).Nonparametric Mann-Whitney U tests were used to analyze the differences of autoantibodies levels between GC patients and healthy controls.The receiver operating characteristic(ROC)analysis was used to assess the diagnostic performance of the single autoantibody,leading to estimate the area under the curve(AUC)with 95%confidence interval(CI),sensitivity and specificity.3.Establishing the predictive TAA panel:A forward(conditional)logistic regression model was established based on the autoantibodies levels in the training cohort(n=558).The predicted probability(P=0.5)was set as cutoff value to distinguish between positive and negative results in the prediction model.ROC curve analysis was used to evaluate the diagnostic performance of the predition model for GC in the training cohort.4.Validating the prediction model:The prediction model was validated by an independent cohort(n=372).The predicted probability of each subject in the validation cohort was calculated according to the prediction model.ROC curve analysis was also used to evaluate the diagnostic performance of the predition model for GC in the validation cohort.5.Prediction model for the diagnosis of GC with different TNM stage:Evaluating the diagnostic performance of prediction model for early stage GC(Ⅰ-Ⅱ)and late stage GC(Ⅲ-Ⅳ)in the training and validaton cohort.Furthermore,this study assessed the ability of prediction model to distinguish all early stage GC patients.6.The difference of anti-TAA sautoantibody levels between sera from GC patients before and after surgery were compared by Wilcoxon matched-pairs signed rank test.Results1.PTEN recombinant protein was successfully purified.The concertation was 0.65 mg/ml.2.Both in the training and validation cohort,9 TAAs showed to exhibite significantly higher autoantibody response in GC patients than that in healthy control(all P>0.05).3.In the training cohort,the diagnostic AUCs of 9 anti-TAAs autoantibodies for GC ranged from 0.570 to 0.738,sensitivity ranged from 59.50%to 91.04%and specificity ranged from 26.52%to 74.91%.Anti-c-Myc antibody showed the highest diagnostic performance for GC with AUC of 0.738(95%CI:0.700-0.774),sensitivity of 61.65%and specificity of 74.91%.4.A forward logistic regression model was established to estimate the risk of being diagnosed with GC based on six anti-TAAs autoantibodies(p62,c-Myc,NPM1,14-3-3ξ,,MDM2 and p16)in the training cohort.In the training cohort,the AUC of the prediction model with 6 TAAs was 0.841(95%CI:0.808-0.871)with sensitivity of 78.14%and specificity of 78.85%.In the validation cohort,the AUC of the prediction model with 6 TAAs was 0.856(95%CI:0.812-0.893)with sensitivity of 84.52%and specificity of 79.35%.When the two cohorts were combined,this model for all early-stage GC yielded an AUC of 0.834(95%CI:0.790-0.873,sensitivity=78.92%,specificity=74.70%and accuracy=76.90%).There was no significant difference between different TNM stages of GC patients for the diagnostic performance(P>0.05).5.Autoantibodies against p62,c-Myc,IMP 1,14-3-34 and NPM1 in serum from GC patients after surgery were significantly higher than that before surgery(all P<0.05).However,anti-p53,anti-PTEN,anti-p16,anti-MDM2 autoantibodies showed no significant difference before and after surgery(P>0.05).Conclusions1.These nine anti-TAAs autoantibodies may serve as potential diagnositic biomarkers for GC.2.Single autoantibody showed relatively low diagnostic performance for GC.Prediction model with 6 anti-TAAs autoantibodies(p62,c-Myc,NPM1,14-3-3ξ,MDM2 and p16)can be used to distinguish GC patients from healthy individuals with a high degree of diagnostic performance,especially for early-stage GC.3.This study further supports that a customized array of multiple TAAs could enhance autoantibodies detection in the immunodiagnosis of cancer.