Using Protein Micro Array And GEO Database To Screen Esophageal Squamous Cell Carcinoma Associated Antigens And Evaluation Of Diagnostic Values Of Their Corresponding Autoantibodies

Esophageal cancer(EC)is one of the most common malignant tumors of the digestive tract.In China,patients with esophageal squamous cell carcinoma(ESCC)account for more than 90%ofpatients with EC.Many patients are in later stage at the initial diagnosis and have poor prognosis because of the occult onset and no obvious early symptoms.Autoantibodies against tumor associated antigens(TAAs)can exist stably in the serum of cancer patients,and can be detected months or even years before the onset of clinical symptoms.Therefore,they have the potential to be biomarkers for early immunodiagnosis of cancers.ObjectiveIn this study,we customized the protein microarray encoded by cancer driving genes,and analyzed the data sets related to ESCC in GEO database,and combined with enzyme-linked immunosorbent assay(ELISA)technology to screen and verify esophageal squamous cell carcinoma associated autoantibodies.The purpose of this study was to explore the optimal diagnosis model of ESCC,and to provide scientific basis for the establishment of a non-invasive technology for early diagnosis of ESCC.Methods1.Using protein microarray to screen esophageal squamous cell carcinoma associated antigensThe human protein microarray based on cancer driving genes were customized to detect the presence levels of anti-TAA autoantibodies in sera from 90 ESCC patients and 50 normal individuals.Receiver operating characteristic(ROC)curve(AUC>0.5,P<0.05)and nonparametric test(the average rank of cases>that of controls,P<0.05)were used to screen out TAAs with high expression in ESCC patients.2.Using GEO database to screen esophageal squamous cell carcinoma associated antigensUsing the classical Bayes algorithm of limma package in R language to perform difference analysis of the relevant data sets in GEO database to screen out the different genes with high expression in cancer tissues compared with the normal esophageal tissues(fold change>2,P<0.05).Further,the enrichment analysis,GEPIA database and relevant studies were used to screen out the TAAs related to development and progression of cancer.3.Using ELISA to confirm the diagnostic value of anti-TAA autoantibodies for ESCCPASS software was used to calculate the sample size,and finally 486 subjects were included.The case group and control group were matched according to gender and age(±3 years).243 sera from patients with ESCC and 243 sera from normal controls were randomly divided into the train set and validation set according to the ratio of 2:1.The presence levels of candidate anti-TAA auto antibodies in the sera of the subjects were detected by ELISA.Non parametric test was used to compare the presence levels of anti-TAA autoantibodies in ESCC group with that in normal control group.The cut-off value of the absorbance value was defined with the specificity>90%and the maximum Youden.The diagnostic values of anti-TAA autoantibodies in ESCC were evaluated with ROC curve.The evaluation indicators included sensitivity,specificity,AUC and coincidence rate.4.Construction and evaluation of diagnostic models for ESCCThe expression levels of autoantibodies with diagnostic value were used as independent variable,and whether the subject had ESCC as dependent variable.Logistic regression analysis,recursive partition analysis and support vector machine were used to build diagnostic models in the train set,and the diagnostic values of different models were evaluated in the validation set.Delong test was applied to compare whether the differences in AUCs were statistically significant The optimal diagnostic model was selected according to the diagnostic performance and stability of the models.The diagnostic value of the optimal model for ESCC subgroup was evaluated based on different degrees of differentiation,TNM stages,the presence of lymph node metastasis and the presence of distant metastasis.Results1.Five candidate TAAs were identified by protein microarray technology,including P53,PTEN,GNA11,GNAS and SRSF2(P<0.05).The AUCs of diagnosis of ESCC with their corresponding autoantibodies were 0.628(95%CI:0.535-0.721),0.659(95%CI:0.564-0.754),0.682(95%CI:0.588-0.776),0.606(95%CI:0.511-0.701)and 0.623(95%CI:0.522-0.723),respectively.2.196 highly expressed genes in ESCC were obtained based on the differential analysis of GSE20347 and GSE2340 from GEO database.Enrichment analysis showed that 15 genes were related to pathway in cancer.Finally,9 genes were identified based on the verification results of GEPIA database and relevant studies.The corresponding proteins encoded by these genes were used as candidate TAAs,including MSH6,COL4A1,MMP9,CXCL8,MMP1,CKS2,LAMC2,FN1 and SLC2A1.3.This study evaluated the diagnostic values of 14 anti-TAA autoantibodies for ESCC and demonstrated that the presence levels of 9 anti-TAA autoantibodies(P53,PTEN,GNA11,SRSF2,MSH6,CXCL8,MMP1,LAMC2 and SLC2A1)in ESCC patients were higher than that in normal controls(P<0.05).Among them,the diagnostic value of anti-LAMC2 autoantibody was highest,the sensitivity and specificity in the train set and the validation set were 22.22%,90.12%and 23.46%,90.12%,respectively.4.In the constructed logistic regression model,decision tree model and support vector machine model,the optimal diagnosis model was logistic regression model consisting of 4 anti-TAA autoantibodies,PRE(P=ESCC)=1/(1+EXP(-(-6.355-3.293 × SLC2A1 + 7.063 × MMP1+4.559 × P53+10.545 × GNA11))).The sensitivity,specificity and accuracy of the model in the train set and the validation set were 70.37%,72.84%,71.60%and 67.90%,67.90%,67.90%,respectively.5.The results of clinical characteristic analysis showed that the model had higher diagnostic value for the early ESCC compared with the later ESCC patients.The AUCs of diagnose early and later patients in the train set and validation set were 0.84,0.85 and 0.72,0.73,respectively.There was no significant difference in the ability of the model to distinguish the ESCC patients of different degrees of differentiation,the presence of lymph node metastasis and the presence of distant metastasis(P>0.05).Conclusions1.9 anti-TAA autoantibodies(P53,PTEN,GNA11,SRSF2,MSH6,CXCL8,MMP1,LAMC2 and SLC2A1)were identified as potential diagnostic biomarkers for the immunodiagnosis of ESCC patients.2.In the established diagnostic models,the logistic regression model consisting of 4 anti-TAA autoantibodies(SLC2A1,MMP1,P53 and GNA11)showed great diagnostic value for early ESCC patients.