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. 2022 Apr 25:13:872253.
doi: 10.3389/fgene.2022.872253. eCollection 2022.

Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma

Manli Luo  1   2   3 , Songmei Wu  1   3 , Yan Ma  1 , Hong Liang  1 , Yage Luo  1 , Wentao Gu  1 , Lijuan Fan  1   3 , Yang Hao  1   3 , Haiting Li  1   3 , Linbo Xing  1   3
Affiliations

Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma

Manli Luo et al. Front Genet. .

Abstract

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 a theoretical basis for constructing a non-invasive serological diagnosis method in early immunodiagnosis of OS. Methods: The 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 levels of eight candidate autoantibodies in the serum of 51 OS cases, 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 an 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 levels of seven autoantibodies (ENO1, GAPDH, HSP27, HSP60, PDLIM1, STMN1, and TPI1) in OS patients were identified higher than those in healthy patients (p < 0.05). By establishing a binary logistic regression predictive model, the optimal panel including three anti-TAAs (ENO1, GAPDH, and TPI1) autoantibodies was screened out. The sensitivity, specificity, Youden index, accuracy, and AUC of diagnosis of OS were 70.59%, 86.27%, 0.5686, 78.43%, and 0.798, respectively. Conclusion: The results proved that through establishing a predictive model, an optimal panel of autoantibodies could help detect OS from OC or NHS at an early stage, which could be used as a promising and powerful tool in clinical practice.

Keywords: autoantibody; detection; early diagnosis; osteosarcoma; panel; tumor-associated antigen.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Scatter plots of serum levels of autoantibodies against eight TAAs in OS, OC and NHS (median with interquartile). p < 0.001; p < 0.01; and p < 0.05. OS, osteosarcoma; OC, osteochondroma; NHS, normal human sera.
FIGURE 2
FIGURE 2
Receiver operating characteristic (ROC) curves of eight candidate tumor-associated autoantibodies (TAAbs).
FIGURE 3
FIGURE 3
ROC curve analysis of the prediction model with the TAA panel of OS detection. (A) Prediction model with three TAAs for OS detection in healthy controls. (B) Prediction model with three TAAs for OS detection in OC. (C) Prediction model with three TAAs for OC detection in healthy controls.
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