Omics in IgG4-related disease.
10.1097/CM9.0000000000003320
- Author:
Shaozhe CAI
1
;
Yu CHEN
1
;
Ziwei HU
1
;
Shengyan LIN
1
;
Rongfen GAO
1
;
Bingxia MING
1
;
Jixin ZHONG
1
;
Wei SUN
2
;
Qian CHEN
3
;
John H STONE
4
;
Lingli DONG
1
Author Information
1. Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
2. Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430014, China.
3. The Division of Gastroenterology, Department of Internal Medicine at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
4. Division of Rheumatology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02301, USA.
- Publication Type:Review
- Keywords:
High-throughput;
IgG4-related disease;
Machine learning;
Omics
- MeSH:
Humans;
Immunoglobulin G4-Related Disease/metabolism*;
Immunoglobulin G/metabolism*;
Machine Learning;
Animals;
Proteomics/methods*
- From:
Chinese Medical Journal
2025;138(14):1665-1675
- CountryChina
- Language:English
-
Abstract:
Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and further to investigating key pathogenic mechanisms. Challenges in gaining a better understanding of the disease, however, stem from its relative rarity-potentially attributed to underrecognition-and the absence of ideal experimental animal models. Recently, with the development of various high-throughput techniques, "omics" studies at different levels (particularly the single-cell omics) have shown promise in providing detailed molecular features of IgG4-RD. While, the application of omics approaches in IgG4-RD is still at an early stage. In this paper, we review the current progress of omics research in IgG4-RD and discuss the value of machine learning methods in analyzing the data with high dimensionality.
