Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma - Luo, Manli; Wu, Songmei; Ma, Yan; Liang, Hong; Luo, Yage; Gu, Wentao; Fan, Lijuan; Hao, Yang; Li, Haiting; Xing, Linbo (Frontiers in Genetics, 2022)
Background: The aim of this study was to identify a panel of candidate autoantibodies against tumor-associated antigens in the detection of osteosarcoma (OS), so as to provide theoretical basis for constructing a non-invasive serological diagnosis method in early immunodiagnosis of OS. Methods: Serological proteome analysis (SERPA) approach was used to select candidate anti-TAA autoantibodies. Then indirect enzyme-linked immunosorbent assay (ELISA) was used to verify the expression level of eight candidate autoantibodies in the serum of 51 OS, 28 osteochondroma (OC), and 51 normal human sera (NHS). The rank-sum test was used to compare the content of eight autoantibodies in the sera of three groups. The diagnostic value of each indicator for OS was analyzed by ROC curve. Differential autoantibodies between OS and NHS were screened. Then a binary logistic regression model was used to establish a prediction logistical regression model. Results: Through ELISA, the expression level of seven autoantibodies (ENO1, GAPDH, HSP27, HSP60, PDLIM1, STMN1, TPI1) in OS patients were identified higher than those in the healthy (P<0.05). By establishing a binary logistic regression predictive model, the optimal panel including three anti-TAAs (ENO1, GAPDH, TPI1) autoantibodies was screened out. The sensitivity, specificity, Youden’s index, accuracy and AUC of diagnosis of OS were 70.59%, 86.27%, 0.5686, 78.43%, and 0.798, respectively. Conclusions: The results proved that through establishing a predictive model, an optimal panel of autoantibodies could help to detect OS from OC or NHS at early stage, which could be used as a promising and powerful tool in clinical practice.