Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma
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...
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.872253/full |
| _version_ | 1818048719420915712 |
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| author | Manli Luo Manli Luo Manli Luo Songmei Wu Songmei Wu Yan Ma Hong Liang Yage Luo Wentao Gu Lijuan Fan Lijuan Fan Yang Hao Yang Hao Haiting Li Haiting Li Linbo Xing Linbo Xing |
| author_facet | Manli Luo Manli Luo Manli Luo Songmei Wu Songmei Wu Yan Ma Hong Liang Yage Luo Wentao Gu Lijuan Fan Lijuan Fan Yang Hao Yang Hao Haiting Li Haiting Li Linbo Xing Linbo Xing |
| author_sort | Manli Luo |
| collection | DOAJ |
| description | 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. |
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| id | doaj.art-df1d54a3bf7c42a0b1345ee4a5aea417 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-10T10:26:09Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-df1d54a3bf7c42a0b1345ee4a5aea4172022-12-22T01:52:44ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-04-011310.3389/fgene.2022.872253872253Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human OsteosarcomaManli Luo0Manli Luo1Manli Luo2Songmei Wu3Songmei Wu4Yan Ma5Hong Liang6Yage Luo7Wentao Gu8Lijuan Fan9Lijuan Fan10Yang Hao11Yang Hao12Haiting Li13Haiting Li14Linbo Xing15Linbo Xing16Luoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaHenan Provincial Rehabilitation Hospital, Luoyang, ChinaHenan University of Chinese Medicine, Zhengzhou, ChinaLuoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaHenan University of Chinese Medicine, Zhengzhou, ChinaLuoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaLuoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaLuoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaLuoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaLuoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaHenan University of Chinese Medicine, Zhengzhou, ChinaLuoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaHenan University of Chinese Medicine, Zhengzhou, ChinaLuoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaHenan University of Chinese Medicine, Zhengzhou, ChinaLuoyang Orthopedic Hospital of Henan Province (Orthopedic Hospital of Henan Province), Luoyang, ChinaHenan University of Chinese Medicine, Zhengzhou, ChinaBackground: 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.https://www.frontiersin.org/articles/10.3389/fgene.2022.872253/fullosteosarcomatumor-associated antigenautoantibodydetectionearly diagnosispanel |
| spellingShingle | Manli Luo Manli Luo Manli Luo Songmei Wu Songmei Wu Yan Ma Hong Liang Yage Luo Wentao Gu Lijuan Fan Lijuan Fan Yang Hao Yang Hao Haiting Li Haiting Li Linbo Xing Linbo Xing Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma Frontiers in Genetics osteosarcoma tumor-associated antigen autoantibody detection early diagnosis panel |
| title | Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma |
| title_full | Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma |
| title_fullStr | Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma |
| title_full_unstemmed | Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma |
| title_short | Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma |
| title_sort | evaluating a panel of autoantibodies against tumor associated antigens in human osteosarcoma |
| topic | osteosarcoma tumor-associated antigen autoantibody detection early diagnosis panel |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.872253/full |
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