Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis
ObjectiveThe study aimed to investigate the serum antigenomic profiling in rheumatoid arthritis (RA) and determine potential diagnostic biomarkers using label-free proteomic technology implemented with machine-learning algorithm.MethodSerum antigens were captured from a cohort consisting of 60 RA pa...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.884462/full |
| _version_ | 1811330279684440064 |
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| author | Peng Han Chao Hou Xi Zheng Xi Zheng Lulu Cao Xiaomeng Shi Xiaohui Zhang Hua Ye Hudan Pan Liang Liu Tingting Li Fanlei Hu Fanlei Hu Fanlei Hu Zhanguo Li Zhanguo Li Zhanguo Li |
| author_facet | Peng Han Chao Hou Xi Zheng Xi Zheng Lulu Cao Xiaomeng Shi Xiaohui Zhang Hua Ye Hudan Pan Liang Liu Tingting Li Fanlei Hu Fanlei Hu Fanlei Hu Zhanguo Li Zhanguo Li Zhanguo Li |
| author_sort | Peng Han |
| collection | DOAJ |
| description | ObjectiveThe study aimed to investigate the serum antigenomic profiling in rheumatoid arthritis (RA) and determine potential diagnostic biomarkers using label-free proteomic technology implemented with machine-learning algorithm.MethodSerum antigens were captured from a cohort consisting of 60 RA patients (45 ACPA-positive RA patients and 15 ACPA-negative RA patients), together with sex- and age-matched 30 osteoarthritis (OA) patients and 30 healthy controls. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then performed. The significantly upregulated and downregulated proteins with fold change > 1.5 (p < 0.05) were selected. Based on these differentially expressed proteins (DEPs), a machine learning model was trained and validated to classify RA, ACPA-positive RA, and ACPA-negative RA.ResultsWe identified 62, 71, and 49 DEPs in RA, ACPA-positive RA, and ACPA-negative RA, respectively, as compared to OA and healthy controls. Typical pathway enrichment and protein–protein interaction networks were shown among these DEPs. Three panels were constructed to classify RA, ACPA-positive RA, and ACPA-negative RA using random forest models algorithm based on the molecular signature of DEPs, whose area under curve (AUC) were calculated as 0.9949 (95% CI = 0.9792–1), 0.9913 (95% CI = 0.9653–1), and 1.0 (95% CI = 1–1).ConclusionThis study illustrated the serum auto-antigen profiling of RA. Among them, three panels of antigens were identified as diagnostic biomarkers to classify RA, ACPA-positive, and ACPA-negative RA patients. |
| first_indexed | 2024-04-13T15:59:39Z |
| format | Article |
| id | doaj.art-8dcfd4e6fb5d45cfa959e97d1a9f6e42 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-13T15:59:39Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-8dcfd4e6fb5d45cfa959e97d1a9f6e422022-12-22T02:40:35ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-04-011310.3389/fimmu.2022.884462884462Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid ArthritisPeng Han0Chao Hou1Xi Zheng2Xi Zheng3Lulu Cao4Xiaomeng Shi5Xiaohui Zhang6Hua Ye7Hudan Pan8Liang Liu9Tingting Li10Fanlei Hu11Fanlei Hu12Fanlei Hu13Zhanguo Li14Zhanguo Li15Zhanguo Li16Department of Rheumatology and Immunology, Peking University People’s Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, ChinaDepartment of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, ChinaDepartment of Rheumatology and Immunology, Peking University People’s Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, ChinaPeking-Tsinghua Center for Life Sciences, Peking University, Beijing, ChinaDepartment of Rheumatology and Immunology, Peking University People’s Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, ChinaState Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, ChinaState Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, ChinaDepartment of Rheumatology and Immunology, Peking University People’s Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, ChinaState Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, ChinaState Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, ChinaDepartment of Rheumatology and Immunology, Peking University People’s Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, ChinaState Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, ChinaDepartment of Integration of Chinese and Western Medicine, School of Basic Medical Sciences, Peking University, Beijing, ChinaDepartment of Rheumatology and Immunology, Peking University People’s Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, ChinaPeking-Tsinghua Center for Life Sciences, Peking University, Beijing, ChinaState Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, ChinaObjectiveThe study aimed to investigate the serum antigenomic profiling in rheumatoid arthritis (RA) and determine potential diagnostic biomarkers using label-free proteomic technology implemented with machine-learning algorithm.MethodSerum antigens were captured from a cohort consisting of 60 RA patients (45 ACPA-positive RA patients and 15 ACPA-negative RA patients), together with sex- and age-matched 30 osteoarthritis (OA) patients and 30 healthy controls. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then performed. The significantly upregulated and downregulated proteins with fold change > 1.5 (p < 0.05) were selected. Based on these differentially expressed proteins (DEPs), a machine learning model was trained and validated to classify RA, ACPA-positive RA, and ACPA-negative RA.ResultsWe identified 62, 71, and 49 DEPs in RA, ACPA-positive RA, and ACPA-negative RA, respectively, as compared to OA and healthy controls. Typical pathway enrichment and protein–protein interaction networks were shown among these DEPs. Three panels were constructed to classify RA, ACPA-positive RA, and ACPA-negative RA using random forest models algorithm based on the molecular signature of DEPs, whose area under curve (AUC) were calculated as 0.9949 (95% CI = 0.9792–1), 0.9913 (95% CI = 0.9653–1), and 1.0 (95% CI = 1–1).ConclusionThis study illustrated the serum auto-antigen profiling of RA. Among them, three panels of antigens were identified as diagnostic biomarkers to classify RA, ACPA-positive, and ACPA-negative RA patients.https://www.frontiersin.org/articles/10.3389/fimmu.2022.884462/fullrheumatoid arthritisantigenomebiomarkersmass spectrometryrandom forest |
| spellingShingle | Peng Han Chao Hou Xi Zheng Xi Zheng Lulu Cao Xiaomeng Shi Xiaohui Zhang Hua Ye Hudan Pan Liang Liu Tingting Li Fanlei Hu Fanlei Hu Fanlei Hu Zhanguo Li Zhanguo Li Zhanguo Li Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis Frontiers in Immunology rheumatoid arthritis antigenome biomarkers mass spectrometry random forest |
| title | Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis |
| title_full | Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis |
| title_fullStr | Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis |
| title_full_unstemmed | Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis |
| title_short | Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis |
| title_sort | serum antigenome profiling reveals diagnostic models for rheumatoid arthritis |
| topic | rheumatoid arthritis antigenome biomarkers mass spectrometry random forest |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.884462/full |
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