Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer
Autoantibodies against tumor-associated antigens (TAAs) are attractive non-invasive biomarkers for detection of cancer due to their inherently stable in serum. Serum autoantibodies against 9 TAAs from gastric cancer (GC) patients and healthy controls were measured by enzyme-linked immunosorbent assa...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-08-01
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| Series: | OncoImmunology |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/2162402X.2018.1452582 |
| _version_ | 1818139411303366656 |
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| author | Shuaibing Wang Jiejie Qin Hua Ye Keyan Wang Jianxiang Shi Yan Ma Yitao Duan Chunhua Song Xiao Wang Liping Dai Kaijuan Wang Peng Wang Jianying Zhang |
| author_facet | Shuaibing Wang Jiejie Qin Hua Ye Keyan Wang Jianxiang Shi Yan Ma Yitao Duan Chunhua Song Xiao Wang Liping Dai Kaijuan Wang Peng Wang Jianying Zhang |
| author_sort | Shuaibing Wang |
| collection | DOAJ |
| description | Autoantibodies against tumor-associated antigens (TAAs) are attractive non-invasive biomarkers for detection of cancer due to their inherently stable in serum. Serum autoantibodies against 9 TAAs from gastric cancer (GC) patients and healthy controls were measured by enzyme-linked immunosorbent assay (ELISA). A logistic regression model predicting the risk of being diagnosed with GC in the training cohort (n = 558) was generated and then validated in an independent cohort (n = 372). Area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Finally, an optimal prediction model with 6 TAAs (p62, c-Myc, NPM1, 14-3-3ξ, MDM2 and p16) showed a great diagnostic performance of GC with AUC of 0.841 in the training cohort and 0.856 in the validation cohort. The proportion of subjects being correctly defined were 78.49% in the training cohort and 81.99% in the validation cohort. This prediction model could also differentiate early-stage (stage I-II) GC patients from healthy controls with sensitivity/specificity of 76.60%/72.34% and 80.56%/79.17% in the training and validation cohort, respectively, and the overall sensitivity/specificity for early-stage GC were 78.92%/74.70% when being combined with two cohorts. This prediction model presented no significant difference for the diagnostic accuracy between early-stage and late-stage (stage III – IV) GC patients. The model with 6 TAAs showed a high diagnostic performance for GC detection, particularly for early-stage GC. This study further supported the hypothesis that a customized array of multiple TAAs was able to enhance autoantibody detection in the immunodiagnosis of GC. |
| first_indexed | 2024-12-11T10:27:40Z |
| format | Article |
| id | doaj.art-1d7f5baafb8949ddac5ba0d15381eaea |
| institution | Directory Open Access Journal |
| issn | 2162-402X |
| language | English |
| last_indexed | 2024-12-11T10:27:40Z |
| publishDate | 2018-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | OncoImmunology |
| spelling | doaj.art-1d7f5baafb8949ddac5ba0d15381eaea2022-12-22T01:11:06ZengTaylor & Francis GroupOncoImmunology2162-402X2018-08-017810.1080/2162402X.2018.14525821452582Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancerShuaibing Wang0Jiejie Qin1Hua Ye2Keyan Wang3Jianxiang Shi4Yan Ma5Yitao Duan6Chunhua Song7Xiao Wang8Liping Dai9Kaijuan Wang10Peng Wang11Jianying Zhang12College of Public Health, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityHenan Academy of Medical and Pharmaceutical Sciences, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityHenan Academy of Medical and Pharmaceutical Sciences, Zhengzhou UniversityHenan Key Laboratory of Tumor EpidemiologyCollege of Public Health, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityAutoantibodies against tumor-associated antigens (TAAs) are attractive non-invasive biomarkers for detection of cancer due to their inherently stable in serum. Serum autoantibodies against 9 TAAs from gastric cancer (GC) patients and healthy controls were measured by enzyme-linked immunosorbent assay (ELISA). A logistic regression model predicting the risk of being diagnosed with GC in the training cohort (n = 558) was generated and then validated in an independent cohort (n = 372). Area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Finally, an optimal prediction model with 6 TAAs (p62, c-Myc, NPM1, 14-3-3ξ, MDM2 and p16) showed a great diagnostic performance of GC with AUC of 0.841 in the training cohort and 0.856 in the validation cohort. The proportion of subjects being correctly defined were 78.49% in the training cohort and 81.99% in the validation cohort. This prediction model could also differentiate early-stage (stage I-II) GC patients from healthy controls with sensitivity/specificity of 76.60%/72.34% and 80.56%/79.17% in the training and validation cohort, respectively, and the overall sensitivity/specificity for early-stage GC were 78.92%/74.70% when being combined with two cohorts. This prediction model presented no significant difference for the diagnostic accuracy between early-stage and late-stage (stage III – IV) GC patients. The model with 6 TAAs showed a high diagnostic performance for GC detection, particularly for early-stage GC. This study further supported the hypothesis that a customized array of multiple TAAs was able to enhance autoantibody detection in the immunodiagnosis of GC.http://dx.doi.org/10.1080/2162402X.2018.1452582autoantibodycancer immunodiagnosisgastric cancertumor-associated antigens |
| spellingShingle | Shuaibing Wang Jiejie Qin Hua Ye Keyan Wang Jianxiang Shi Yan Ma Yitao Duan Chunhua Song Xiao Wang Liping Dai Kaijuan Wang Peng Wang Jianying Zhang Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer OncoImmunology autoantibody cancer immunodiagnosis gastric cancer tumor-associated antigens |
| title | Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer |
| title_full | Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer |
| title_fullStr | Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer |
| title_full_unstemmed | Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer |
| title_short | Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer |
| title_sort | using a panel of multiple tumor associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer |
| topic | autoantibody cancer immunodiagnosis gastric cancer tumor-associated antigens |
| url | http://dx.doi.org/10.1080/2162402X.2018.1452582 |
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