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A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma

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)...

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Autores principales: Li, Tiandong, Xia, Junfen, Yun, Huan, Sun, Guiying, Shen, Yajing, Wang, Peng, Shi, Jianxiang, Wang, Keyan, Yang, Hongwei, Ye, Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10655307/
https://www.ncbi.nlm.nih.gov/pubmed/37974212
http://dx.doi.org/10.1186/s12935-023-03107-1
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author Li, Tiandong
Xia, Junfen
Yun, Huan
Sun, Guiying
Shen, Yajing
Wang, Peng
Shi, Jianxiang
Wang, Keyan
Yang, Hongwei
Ye, Hua
author_facet Li, Tiandong
Xia, Junfen
Yun, Huan
Sun, Guiying
Shen, Yajing
Wang, Peng
Shi, Jianxiang
Wang, Keyan
Yang, Hongwei
Ye, Hua
author_sort Li, Tiandong
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-023-03107-1.
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spelling pubmed-106553072023-11-16 A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma Li, Tiandong Xia, Junfen Yun, Huan Sun, Guiying Shen, Yajing Wang, Peng Shi, Jianxiang Wang, Keyan Yang, Hongwei Ye, Hua Cancer Cell Int Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-023-03107-1. BioMed Central 2023-11-16 /pmc/articles/PMC10655307/ /pubmed/37974212 http://dx.doi.org/10.1186/s12935-023-03107-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Tiandong
Xia, Junfen
Yun, Huan
Sun, Guiying
Shen, Yajing
Wang, Peng
Shi, Jianxiang
Wang, Keyan
Yang, Hongwei
Ye, Hua
A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma
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 Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10655307/
https://www.ncbi.nlm.nih.gov/pubmed/37974212
http://dx.doi.org/10.1186/s12935-023-03107-1
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