| Ovarian cancer(OC)is one of the common gynecological tumors,whose mortality rate ranks first in female gynecological tumors.More than 70% of patients are in advanced stage at diagnosis.Tumor associated antigen(TAA)autoantibodies are stable in the serum of tumor patients which can be detected months or even years before the onset of clinical symptoms.Different tumors have different combinations of markers.Therefore,they can be used as a non-invasive detection method for the early diagnosis of OC.ObjectiveThe aim of this study was to select a panel of candidate TAAs based on protein-microarray,then verified by the training cohort containing little sample as well as to select the most suitable cut-off value and optimal combination of autoantibodies by validation cohort,so as to provide theoretical basis for constructing a set of non-invasive serological diagnosis methods for early screening OC.Methods1.Differential expression of autoantibodies in serum from 17 patients with OC and 27 healthy controls were screened using protein microarrays based on 138 cancer driver genes.2.Serum from 234 patients newly diagnosed with OC was collected from the hospital,matched with 234 healthy control from the department of physical examination in the hospital according to the age ranging 3 years.3.Indirect enzyme-linked immunosorbent assay(ELISA)was used to detect the content of 10 anti-TAA autoantibodies in the serum of 140 subjects in the training cohort.The rank-sum test was used to compare the content of 10 autoantibodies in the serum of OC and normal control.The diagnostic value of each indicator for OC was analyzed by ROC curve.Thus,the differentially expressed autoantibodies were screened out.4.Differential autoantibodies screened in the test cohort were verified using another set of independent populations(validation cohort,n=328).The method of Logistic regression prediction model,the 95 th percentile,average plus two standard deviation and maximum Yoden index were used to screen the most suitable cut-off values and a optimal panel of autoantibodies and evaluate the diagnostic value.5.The relationship between autoantibodies and clinical features of patients with ovarian cancer was analyzed.Combined the panel of autoantibodies and traditional tumor markers to evaluate the diagnostic value of the combination for OC.Results1.According to different screening methods,10 TAAs(GNAS,NPM1,FUBP1,p53,KRAS,p62,IMP1,RalA,koc,survivin)were preliminarily screened by protein microarray technology.2.In the training cohort,the contents of six kinds of anti-TAA autoantibodies(GNAS,NPM1,p53,KRAS,p62,IMP1)in ovarian cancer patients were higher than those in healthy controls(P<0.05).In the validation cohort,the contents of the six anti-TAAs in patients with ovarian cancer were also significantly higher than those in the normal control group(P<0.05),and the area under the ROC in diagnosis of ovarian cancer was more than 0.6.3.In the validation cohort,the optimal set of autoantibodies based on three TAAs(GNAS,p53 and p62)was screened through the method of Logistic regression prediction model,the 95 th percentile,average plus two standard deviation and maximum Yoden index.The sensitivity,specificity and accuracy of diagnosis of OC were 53.66%,86.59%,0.4024 and 70.12%,respectively.4.The positive rate of CA125 combined with multiple TAA autoantibodies was significantly higher than that of CA125 alone or the combination of TAA autoantibodies alone(P<0.05).Conclusions1.These six anti-TAAs(GNAS,NPM1,p53,KRAS,p62,IMP1)can serve as potential diagnositic biomarkers for the diagnosis of OC.2.The value of single autoantibody in diagnosis of ovarian cancer was low.A combination based on three anti-TAA autoantibodies(GNAS,p53,and p62)can improve the diagnostic value of OC when the cut-off value was set at 95 th percentile of the normal population.3.It was better to detect OC based on combination of CA125 and a special panel of autoantibodies. |
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