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Establishment and validation of an immunodiagnostic model for prediction of breast cancer.
OncoImmunology ( IF 6.5 ) Pub Date : 2019-10-28 , DOI: 10.1080/2162402x.2019.1682382
Cuipeng Qiu 1, 2 , Peng Wang 1, 2 , Bofei Wang 2 , Jianxiang Shi 2, 3 , Xiao Wang 2, 3 , Tiandong Li 1, 2 , Jiejie Qin 1, 2 , Liping Dai 2, 3 , Hua Ye 1, 2 , Jianying Zhang 1, 2, 3
Affiliation  

Serum autoantibodies that react with tumor-associated antigens (TAAs) can be used as potential biomarkers for diagnosis of cancer. This study aims to evaluate the immunodiagnostic value of 11 anti-TAAs autoantibodies for detection of breast cancer (BC) and establish a diagnostic model for distinguishing BC from normal human controls (NHC) and benign breast diseases (BBD). Sera from 10 BC patients and 10 NHC were used to detect 11 anti-TAAs autoantibodies by western blotting. The 11 anti-TAAs autoantibodies were further assessed in 983 sera by relative quantitative enzyme-linked immunosorbent assay (ELISA). Binary logistic regression and Fisher linear discriminant analysis were conducted to establish a prediction model by using 184 BC and 184 NHC (training cohort, n = 568) and validated by leave-one-out cross-validation. Logistic regression model was selected to establish the prediction model. Results were validated using an independent validation cohort (n = 415). The five anti-TAAs (p53, cyclinB1, p16, p62, 14-3-3ξ) autoantibodies were selected to construct the model with the area under the curve (AUC) of 0.943 (95% CI, 0.919-0.967) in training cohort and 0.916 (95% CI, 0.886-0.947) in the validation cohort. In the identification of BC and BBD, AUCs were 0.881 (95% CI, 0.848-0.914) and 0.849 (95% CI, 0.803-0.894) in training and validation cohort, respectively. In summary, our study indicates that the immunodiagnostic model can distinguish BC from NHC and BC from BBD and this model may have a potential application in immunodiagnosis of breast cancer.

中文翻译:

建立和验证预测乳腺癌的免疫诊断模型。

与肿瘤相关抗原(TAA)反应的血清自身抗体可用作诊断癌症的潜在生物标记。这项研究旨在评估11种抗TAA自身抗体对乳腺癌(BC)的免疫诊断价值,并建立一种区分BC与正常人对照(NHC)和良性乳腺疾病(BBD)的诊断模型。通过蛋白质印迹,使用来自10例BC患者和10例NHC的血清来检测11种抗TAA自身抗体。通过相对定量酶联免疫吸附测定(ELISA)在983血清中进一步评估了11种抗TAA自身抗体。通过使用184 BC和184 NHC(训练队列,n = 568)进行了二元logistic回归和Fisher线性判别分析,以建立预测模型,并通过留一法交叉验证进行了验证。选择Logistic回归模型建立预测模型。使用独立的验证队列(n = 415)验证结果。选择了五个抗TAA(p53,cyclinB1,p16,p62、14-3-3ξ)自身抗体来构建训练队列中曲线下面积(AUC)为0.943(95%CI,0.919-0.967)的模型在验证队列中为0.916(95%CI,0.886-0.947)。在鉴定BC和BBD时,在训练和验证队列中,AUC分别为0.881(95%CI,0.848-0.914)和0.849(95%CI,0.803-0.894)。总而言之,我们的研究表明,该免疫诊断模型可以区分BC与NHC和BC与BBD,并且该模型在乳腺癌的免疫诊断中可能具有潜在的应用。使用独立的验证队列(n = 415)验证结果。选择了五个抗TAA(p53,cyclinB1,p16,p62、14-3-3ξ)自身抗体来构建训练队列中曲线下面积(AUC)为0.943(95%CI,0.919-0.967)的模型在验证队列中为0.916(95%CI,0.886-0.947)。在鉴定BC和BBD时,在训练和验证队列中,AUC分别为0.881(95%CI,0.848-0.914)和0.849(95%CI,0.803-0.894)。总而言之,我们的研究表明,该免疫诊断模型可以区分BC与NHC和BC与BBD,并且该模型在乳腺癌的免疫诊断中可能具有潜在的应用。使用独立的验证队列(n = 415)验证结果。选择了五个抗TAA(p53,cyclinB1,p16,p62、14-3-3ξ)自身抗体来构建训练队列中曲线下面积(AUC)为0.943(95%CI,0.919-0.967)的模型在验证队列中为0.916(95%CI,0.886-0.947)。在鉴定BC和BBD时,在训练和验证队列中,AUC分别为0.881(95%CI,0.848-0.914)和0.849(95%CI,0.803-0.894)。总而言之,我们的研究表明,该免疫诊断模型可以区分BC与NHC和BC与BBD,并且该模型在乳腺癌的免疫诊断中可能具有潜在的应用。培训队列中为967),验证队列中为0.916(95%CI,0.886-0.947)。在鉴定BC和BBD时,在训练和验证队列中,AUC分别为0.881(95%CI,0.848-0.914)和0.849(95%CI,0.803-0.894)。总而言之,我们的研究表明,该免疫诊断模型可以区分BC与NHC和BC与BBD,并且该模型在乳腺癌的免疫诊断中可能具有潜在的应用。培训队列中为967),验证队列中为0.916(95%CI,0.886-0.947)。在鉴定BC和BBD时,在训练和验证队列中,AUC分别为0.881(95%CI,0.848-0.914)和0.849(95%CI,0.803-0.894)。总而言之,我们的研究表明,该免疫诊断模型可以区分BC与NHC和BC与BBD,并且该模型在乳腺癌的免疫诊断中可能具有潜在的应用。
更新日期:2019-10-28
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