{"title":"IgG4 相关疾病的分子生物学","authors":"Shaozhe Cai, Yu Chen, Ziwei Hu, Shengyan Lin, Rongfen Gao, Bingxia Ming, Jixin Zhong, Wei Sun, Qian Chen, John H Stone, Lingli Dong","doi":"10.1097/CM9.0000000000003320","DOIUrl":null,"url":null,"abstract":"<p><strong>Abstract: </strong>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.</p>","PeriodicalId":10183,"journal":{"name":"Chinese Medical Journal","volume":" ","pages":"1665-1675"},"PeriodicalIF":7.3000,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273674/pdf/","citationCount":"0","resultStr":"{\"title\":\"Omics in IgG4-related disease.\",\"authors\":\"Shaozhe Cai, Yu Chen, Ziwei Hu, Shengyan Lin, Rongfen Gao, Bingxia Ming, Jixin Zhong, Wei Sun, Qian Chen, John H Stone, Lingli Dong\",\"doi\":\"10.1097/CM9.0000000000003320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Abstract: </strong>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.</p>\",\"PeriodicalId\":10183,\"journal\":{\"name\":\"Chinese Medical Journal\",\"volume\":\" \",\"pages\":\"1665-1675\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2025-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273674/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chinese Medical Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/CM9.0000000000003320\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Medical Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/CM9.0000000000003320","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/25 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
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.
期刊介绍:
The Chinese Medical Journal (CMJ) is published semimonthly in English by the Chinese Medical Association, and is a peer reviewed general medical journal for all doctors, researchers, and health workers regardless of their medical specialty or type of employment. Established in 1887, it is the oldest medical periodical in China and is distributed worldwide. The journal functions as a window into China’s medical sciences and reflects the advances and progress in China’s medical sciences and technology. It serves the objective of international academic exchange. The journal includes Original Articles, Editorial, Review Articles, Medical Progress, Brief Reports, Case Reports, Viewpoint, Clinical Exchange, Letter,and News,etc. CMJ is abstracted or indexed in many databases including Biological Abstracts, Chemical Abstracts, Index Medicus/Medline, Science Citation Index (SCI), Current Contents, Cancerlit, Health Plan & Administration, Embase, Social Scisearch, Aidsline, Toxline, Biocommercial Abstracts, Arts and Humanities Search, Nuclear Science Abstracts, Water Resources Abstracts, Cab Abstracts, Occupation Safety & Health, etc. In 2007, the impact factor of the journal by SCI is 0.636, and the total citation is 2315.