Cargando…
Distinct metabolic biomarkers to distinguish IgG4-related disease from Sjogren’s syndrome and pancreatic cancer and predict disease prognosis
BACKGROUND: The pathogenesis of immunoglobulin G4-related disease (IgG4-RD) remains unclear. IgG4-RD often mimics other diseases, including pancreatic cancer (PC) and Sjogren’s syndrome (SS), which may easily lead to misdiagnosis. This study was performed to explore the metabolite changes and potent...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795602/ https://www.ncbi.nlm.nih.gov/pubmed/36575511 http://dx.doi.org/10.1186/s12916-022-02700-x |
_version_ | 1784860297330163712 |
---|---|
author | Yan, Songxin Peng, Yu Wu, Ziyan Cheng, Linlin Li, Haolong Xu, Honglin Huang, Yuan Zhang, Wen Li, Yongzhe |
author_facet | Yan, Songxin Peng, Yu Wu, Ziyan Cheng, Linlin Li, Haolong Xu, Honglin Huang, Yuan Zhang, Wen Li, Yongzhe |
author_sort | Yan, Songxin |
collection | PubMed |
description | BACKGROUND: The pathogenesis of immunoglobulin G4-related disease (IgG4-RD) remains unclear. IgG4-RD often mimics other diseases, including pancreatic cancer (PC) and Sjogren’s syndrome (SS), which may easily lead to misdiagnosis. This study was performed to explore the metabolite changes and potential biomarkers of IgG4-RD and other misdiagnosed diseases. METHODS: Untargeted liquid chromatography–tandem mass spectrometry metabolomics profiling of plasma samples from a cohort comprising healthy controls (HCs) and patients with IgG4-RD (n = 87), PC (n = 33), and SS (n = 31) was performed. A random forest machine learning model was used to verify the relevance of the identified metabolites in the diagnosis of different diseases and the prediction of disease prognosis. RESULTS: The ATP-binding cassette transporter pathway was found to be most closely related to IgG4-RD, which was significantly up-regulated in the IgG4-RD group than in all the matched groups. Five metabolites were proved to be valuable biomarkers for IgG4-RD. Caftaric acid, maltotetraose, d-glutamic acid, 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine, and hydroxyproline were useful in distinguishing between IgG4-RD, PC, SS, and HC [area under the curve (AUC) = 1]. A combination of phenylalanine betaine, 1-(1z-hexadecenyl)-sn-glycero-3-phosphocholine, Pi 40:8, uracil, and N1-methyl-2-pyridone-5-carboxamide showed a moderate value in predicting relapse in patients with IgG4-RD (AUC = 0.8). CONCLUSIONS: Our findings revealed the metabolite changes of IgG4-RD and provide new insights for deepening our understanding of IgG4-RD despite the lack of validation in external cohorts. Metabolomic biomarkers have significance in the clinical diagnosis and disease prognosis of IgG4-RD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02700-x. |
format | Online Article Text |
id | pubmed-9795602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97956022022-12-29 Distinct metabolic biomarkers to distinguish IgG4-related disease from Sjogren’s syndrome and pancreatic cancer and predict disease prognosis Yan, Songxin Peng, Yu Wu, Ziyan Cheng, Linlin Li, Haolong Xu, Honglin Huang, Yuan Zhang, Wen Li, Yongzhe BMC Med Research Article BACKGROUND: The pathogenesis of immunoglobulin G4-related disease (IgG4-RD) remains unclear. IgG4-RD often mimics other diseases, including pancreatic cancer (PC) and Sjogren’s syndrome (SS), which may easily lead to misdiagnosis. This study was performed to explore the metabolite changes and potential biomarkers of IgG4-RD and other misdiagnosed diseases. METHODS: Untargeted liquid chromatography–tandem mass spectrometry metabolomics profiling of plasma samples from a cohort comprising healthy controls (HCs) and patients with IgG4-RD (n = 87), PC (n = 33), and SS (n = 31) was performed. A random forest machine learning model was used to verify the relevance of the identified metabolites in the diagnosis of different diseases and the prediction of disease prognosis. RESULTS: The ATP-binding cassette transporter pathway was found to be most closely related to IgG4-RD, which was significantly up-regulated in the IgG4-RD group than in all the matched groups. Five metabolites were proved to be valuable biomarkers for IgG4-RD. Caftaric acid, maltotetraose, d-glutamic acid, 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoserine, and hydroxyproline were useful in distinguishing between IgG4-RD, PC, SS, and HC [area under the curve (AUC) = 1]. A combination of phenylalanine betaine, 1-(1z-hexadecenyl)-sn-glycero-3-phosphocholine, Pi 40:8, uracil, and N1-methyl-2-pyridone-5-carboxamide showed a moderate value in predicting relapse in patients with IgG4-RD (AUC = 0.8). CONCLUSIONS: Our findings revealed the metabolite changes of IgG4-RD and provide new insights for deepening our understanding of IgG4-RD despite the lack of validation in external cohorts. Metabolomic biomarkers have significance in the clinical diagnosis and disease prognosis of IgG4-RD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02700-x. BioMed Central 2022-12-27 /pmc/articles/PMC9795602/ /pubmed/36575511 http://dx.doi.org/10.1186/s12916-022-02700-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Article Yan, Songxin Peng, Yu Wu, Ziyan Cheng, Linlin Li, Haolong Xu, Honglin Huang, Yuan Zhang, Wen Li, Yongzhe Distinct metabolic biomarkers to distinguish IgG4-related disease from Sjogren’s syndrome and pancreatic cancer and predict disease prognosis |
title | Distinct metabolic biomarkers to distinguish IgG4-related disease from Sjogren’s syndrome and pancreatic cancer and predict disease prognosis |
title_full | Distinct metabolic biomarkers to distinguish IgG4-related disease from Sjogren’s syndrome and pancreatic cancer and predict disease prognosis |
title_fullStr | Distinct metabolic biomarkers to distinguish IgG4-related disease from Sjogren’s syndrome and pancreatic cancer and predict disease prognosis |
title_full_unstemmed | Distinct metabolic biomarkers to distinguish IgG4-related disease from Sjogren’s syndrome and pancreatic cancer and predict disease prognosis |
title_short | Distinct metabolic biomarkers to distinguish IgG4-related disease from Sjogren’s syndrome and pancreatic cancer and predict disease prognosis |
title_sort | distinct metabolic biomarkers to distinguish igg4-related disease from sjogren’s syndrome and pancreatic cancer and predict disease prognosis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795602/ https://www.ncbi.nlm.nih.gov/pubmed/36575511 http://dx.doi.org/10.1186/s12916-022-02700-x |
work_keys_str_mv | AT yansongxin distinctmetabolicbiomarkerstodistinguishigg4relateddiseasefromsjogrenssyndromeandpancreaticcancerandpredictdiseaseprognosis AT pengyu distinctmetabolicbiomarkerstodistinguishigg4relateddiseasefromsjogrenssyndromeandpancreaticcancerandpredictdiseaseprognosis AT wuziyan distinctmetabolicbiomarkerstodistinguishigg4relateddiseasefromsjogrenssyndromeandpancreaticcancerandpredictdiseaseprognosis AT chenglinlin distinctmetabolicbiomarkerstodistinguishigg4relateddiseasefromsjogrenssyndromeandpancreaticcancerandpredictdiseaseprognosis AT lihaolong distinctmetabolicbiomarkerstodistinguishigg4relateddiseasefromsjogrenssyndromeandpancreaticcancerandpredictdiseaseprognosis AT xuhonglin distinctmetabolicbiomarkerstodistinguishigg4relateddiseasefromsjogrenssyndromeandpancreaticcancerandpredictdiseaseprognosis AT huangyuan distinctmetabolicbiomarkerstodistinguishigg4relateddiseasefromsjogrenssyndromeandpancreaticcancerandpredictdiseaseprognosis AT zhangwen distinctmetabolicbiomarkerstodistinguishigg4relateddiseasefromsjogrenssyndromeandpancreaticcancerandpredictdiseaseprognosis AT liyongzhe distinctmetabolicbiomarkerstodistinguishigg4relateddiseasefromsjogrenssyndromeandpancreaticcancerandpredictdiseaseprognosis |