A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma
Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease that requires precise diagnosis for effective treatment. However, the diagnostic value of carbohydrate antigen 19 − 9 (CA19-9) is limited. Therefore, this study aims to identify novel tumor-associated autoantibodies...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | Cancer Cell International |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12935-023-03107-1 |
| _version_ | 1797556314903150592 |
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| author | Tiandong Li Junfen Xia Huan Yun Guiying Sun Yajing Shen Peng Wang Jianxiang Shi Keyan Wang Hongwei Yang Hua Ye |
| author_facet | Tiandong Li Junfen Xia Huan Yun Guiying Sun Yajing Shen Peng Wang Jianxiang Shi Keyan Wang Hongwei Yang Hua Ye |
| author_sort | Tiandong Li |
| collection | DOAJ |
| description | Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease that requires precise diagnosis for effective treatment. However, the diagnostic value of carbohydrate antigen 19 − 9 (CA19-9) is limited. Therefore, this study aims to identify novel tumor-associated autoantibodies (TAAbs) for PDAC diagnosis. Methods A three-phase strategy comprising discovery, test, and validation was implemented. HuProt™ Human Proteome Microarray v3.1 was used to screen potential TAAbs in 49 samples. Subsequently, the levels of potential TAAbs were evaluated in 477 samples via enzyme-linked immunosorbent assay (ELISA) in PDAC, benign pancreatic diseases (BPD), and normal control (NC), followed by the construction of a diagnostic model. Results In the discovery phase, protein microarrays identified 167 candidate TAAbs. Based on bioinformatics analysis, fifteen tumor-associated antigens (TAAs) were selected for further validation using ELISA. Ten TAAbs exhibited differentially expressed in PDAC patients in the test phase (P < 0.05), with an area under the curve (AUC) ranging from 0.61 to 0.76. An immunodiagnostic model including three TAAbs (anti-HEXB, anti-TXLNA, anti-SLAMF6) was then developed, demonstrating AUCs of 0.81 (58.0% sensitivity, 86.0% specificity) and 0.78 (55.71% sensitivity, 87.14% specificity) for distinguishing PDAC from NC. Additionally, the model yielded AUCs of 0.80 (58.0% sensitivity, 86.25% specificity) and 0.83 (55.71% sensitivity, 100% specificity) for distinguishing PDAC from BPD in the test and validation phases, respectively. Notably, the combination of the immunodiagnostic model with CA19-9 resulted in an increased positive rate of PDAC to 92.91%. Conclusion The immunodiagnostic model may offer a novel serological detection method for PDAC diagnosis, providing valuable insights into the development of effective diagnostic biomarkers. |
| first_indexed | 2024-03-10T17:00:07Z |
| format | Article |
| id | doaj.art-9d2859cdcf0945dd81666642c6c4c721 |
| institution | Directory Open Access Journal |
| issn | 1475-2867 |
| language | English |
| last_indexed | 2024-03-10T17:00:07Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj.art-9d2859cdcf0945dd81666642c6c4c7212023-11-20T10:59:49ZengBMCCancer Cell International1475-28672023-11-0123111310.1186/s12935-023-03107-1A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinomaTiandong Li0Junfen Xia1Huan Yun2Guiying Sun3Yajing Shen4Peng Wang5Jianxiang Shi6Keyan Wang7Hongwei Yang8Hua Ye9College of Public Health, Zhengzhou UniversityOffice of Health Care, The Third Affiliated Hospital of Zhengzhou UniversityZhengzhou UniversityCollege of Public Health, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityHenan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou UniversityHenan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou UniversityDepartment of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou UniversityCollege of Public Health, Zhengzhou UniversityAbstract Background Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease that requires precise diagnosis for effective treatment. However, the diagnostic value of carbohydrate antigen 19 − 9 (CA19-9) is limited. Therefore, this study aims to identify novel tumor-associated autoantibodies (TAAbs) for PDAC diagnosis. Methods A three-phase strategy comprising discovery, test, and validation was implemented. HuProt™ Human Proteome Microarray v3.1 was used to screen potential TAAbs in 49 samples. Subsequently, the levels of potential TAAbs were evaluated in 477 samples via enzyme-linked immunosorbent assay (ELISA) in PDAC, benign pancreatic diseases (BPD), and normal control (NC), followed by the construction of a diagnostic model. Results In the discovery phase, protein microarrays identified 167 candidate TAAbs. Based on bioinformatics analysis, fifteen tumor-associated antigens (TAAs) were selected for further validation using ELISA. Ten TAAbs exhibited differentially expressed in PDAC patients in the test phase (P < 0.05), with an area under the curve (AUC) ranging from 0.61 to 0.76. An immunodiagnostic model including three TAAbs (anti-HEXB, anti-TXLNA, anti-SLAMF6) was then developed, demonstrating AUCs of 0.81 (58.0% sensitivity, 86.0% specificity) and 0.78 (55.71% sensitivity, 87.14% specificity) for distinguishing PDAC from NC. Additionally, the model yielded AUCs of 0.80 (58.0% sensitivity, 86.25% specificity) and 0.83 (55.71% sensitivity, 100% specificity) for distinguishing PDAC from BPD in the test and validation phases, respectively. Notably, the combination of the immunodiagnostic model with CA19-9 resulted in an increased positive rate of PDAC to 92.91%. Conclusion The immunodiagnostic model may offer a novel serological detection method for PDAC diagnosis, providing valuable insights into the development of effective diagnostic biomarkers.https://doi.org/10.1186/s12935-023-03107-1Pancreatic ductal adenocarcinomaAutoantibodyDiagnosisImmunodiagnosticModel |
| spellingShingle | Tiandong Li Junfen Xia Huan Yun Guiying Sun Yajing Shen Peng Wang Jianxiang Shi Keyan Wang Hongwei Yang Hua Ye A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma Cancer Cell International Pancreatic ductal adenocarcinoma Autoantibody Diagnosis Immunodiagnostic Model |
| title | A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma |
| title_full | A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma |
| title_fullStr | A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma |
| title_full_unstemmed | A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma |
| title_short | A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma |
| title_sort | novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma |
| topic | Pancreatic ductal adenocarcinoma Autoantibody Diagnosis Immunodiagnostic Model |
| url | https://doi.org/10.1186/s12935-023-03107-1 |
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