Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies
Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1342429/full |
| _version_ | 1797366725743738880 |
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| author | Hong-Long Ji Hong-Long Ji Nan Mile S. Xi Chandra Mohan Xiting Yan Krishan G. Jain Krishan G. Jain Qun Sophia Zang Qun Sophia Zang Vivian Gahtan Vivian Gahtan Runzhen Zhao Runzhen Zhao |
| author_facet | Hong-Long Ji Hong-Long Ji Nan Mile S. Xi Chandra Mohan Xiting Yan Krishan G. Jain Krishan G. Jain Qun Sophia Zang Qun Sophia Zang Vivian Gahtan Vivian Gahtan Runzhen Zhao Runzhen Zhao |
| author_sort | Hong-Long Ji |
| collection | DOAJ |
| description | Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular endotypes is sorely needed. In this study, we systematically evaluate the biomarkers identified through Omics and non-Omics approaches in sarcoidosis. Most of the currently documented biomarkers for sarcoidosis are mainly identified through conventional “one-for-all” non-Omics targeted studies. Although the application of machine learning algorithms to identify biomarkers and endotypes from unbiased comprehensive Omics studies is still in its infancy, a series of biomarkers, overwhelmingly for diagnosis to differentiate sarcoidosis from healthy controls have been reported. In view of the fact that current biomarker profiles in sarcoidosis are scarce, fragmented and mostly not validated, there is an urgent need to identify novel sarcoidosis biomarkers and molecular endotypes using more advanced Omics approaches to facilitate disease diagnosis and prognosis, resolve disease heterogeneity, and facilitate personalized medicine. |
| first_indexed | 2024-03-08T17:07:42Z |
| format | Article |
| id | doaj.art-5eed752bbab34a1c94379bfc9a370040 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-08T17:07:42Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-5eed752bbab34a1c94379bfc9a3700402024-01-04T04:21:10ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-01-011410.3389/fimmu.2023.13424291342429Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studiesHong-Long Ji0Hong-Long Ji1Nan Mile S. Xi2Chandra Mohan3Xiting Yan4Krishan G. Jain5Krishan G. Jain6Qun Sophia Zang7Qun Sophia Zang8Vivian Gahtan9Vivian Gahtan10Runzhen Zhao11Runzhen Zhao12Burn and Shock Trauma Research Institute, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesDepartment of Surgery, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesDepartment of Mathematics and Statistics at Loyola University Chicago, Chicago, IL, United StatesBiomedical Engineering & Medicine, University of Houston, Houston, TX, United StatesDepartment of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, United StatesBurn and Shock Trauma Research Institute, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesDepartment of Surgery, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesBurn and Shock Trauma Research Institute, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesDepartment of Surgery, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesBurn and Shock Trauma Research Institute, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesDepartment of Surgery, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesBurn and Shock Trauma Research Institute, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesDepartment of Surgery, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United StatesSarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular endotypes is sorely needed. In this study, we systematically evaluate the biomarkers identified through Omics and non-Omics approaches in sarcoidosis. Most of the currently documented biomarkers for sarcoidosis are mainly identified through conventional “one-for-all” non-Omics targeted studies. Although the application of machine learning algorithms to identify biomarkers and endotypes from unbiased comprehensive Omics studies is still in its infancy, a series of biomarkers, overwhelmingly for diagnosis to differentiate sarcoidosis from healthy controls have been reported. In view of the fact that current biomarker profiles in sarcoidosis are scarce, fragmented and mostly not validated, there is an urgent need to identify novel sarcoidosis biomarkers and molecular endotypes using more advanced Omics approaches to facilitate disease diagnosis and prognosis, resolve disease heterogeneity, and facilitate personalized medicine.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1342429/fullsarcoidosisbiomarkerendotypeomicsmachine learning algorithms |
| spellingShingle | Hong-Long Ji Hong-Long Ji Nan Mile S. Xi Chandra Mohan Xiting Yan Krishan G. Jain Krishan G. Jain Qun Sophia Zang Qun Sophia Zang Vivian Gahtan Vivian Gahtan Runzhen Zhao Runzhen Zhao Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies Frontiers in Immunology sarcoidosis biomarker endotype omics machine learning algorithms |
| title | Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies |
| title_full | Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies |
| title_fullStr | Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies |
| title_full_unstemmed | Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies |
| title_short | Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies |
| title_sort | biomarkers and molecular endotypes of sarcoidosis lessons from omics and non omics studies |
| topic | sarcoidosis biomarker endotype omics machine learning algorithms |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1342429/full |
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