Plasma proteomics-based biomarkers for predicting response to mesenchymal stem cell therapy in severe COVID-19
Abstract Background The objective of this study was to identify potential biomarkers for predicting response to MSC therapy by pre-MSC treatment plasma proteomic profile in severe COVID-19 in order to optimize treatment choice. Methods A total of 58 patients selected from our previous RCT cohort wer...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | Stem Cell Research & Therapy |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13287-023-03573-4 |
| _version_ | 1797388494073495552 |
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| author | Tian-Tian Li Wei-Qi Yao Hai-Bo Dong Ze-Rui Wang Zi-Ying Zhang Meng-Qi Yuan Lei Shi Fu-Sheng Wang |
| author_facet | Tian-Tian Li Wei-Qi Yao Hai-Bo Dong Ze-Rui Wang Zi-Ying Zhang Meng-Qi Yuan Lei Shi Fu-Sheng Wang |
| author_sort | Tian-Tian Li |
| collection | DOAJ |
| description | Abstract Background The objective of this study was to identify potential biomarkers for predicting response to MSC therapy by pre-MSC treatment plasma proteomic profile in severe COVID-19 in order to optimize treatment choice. Methods A total of 58 patients selected from our previous RCT cohort were enrolled in this study. MSC responders (n = 35) were defined as whose resolution of lung consolidation ≥ 51.99% (the median value for resolution of lung consolidation) from pre-MSC to 28 days post-MSC treatment, while non-responders (n = 23) were defined as whose resolution of lung consolidation < 51.99%. Plasma before MSC treatment was detected using data-independent acquisition (DIA) proteomics. Multivariate logistic regression analysis was used to identify pre-MSC treatment plasma proteomic biomarkers that might distinguish between responders and non-responders to MSC therapy. Results In total, 1101 proteins were identified in plasma. Compared with the non-responders, the responders had three upregulated proteins (CSPG2, CTRB1, and OSCAR) and 10 downregulated proteins (ANXA1, AGRG6, CAPG, DDX55, KV133, LEG10, OXSR1, PICAL, PTGDS, and S100A8) in plasma before MSC treatment. Using logistic regression model, lower levels of DDX55, AGRG6, PICAL, and ANXA1 and higher levels of CTRB1 pre-MSC treatment were predictors of responders to MSC therapy, with AUC of the ROC at 0.910 (95% CI 0.818–1.000) in the training set. In the validation set, AUC of the ROC was 0.767 (95% CI 0.459–1.000). Conclusions The responsiveness to MSC therapy appears to depend on baseline level of DDX55, AGRG6, PICAL, CTRB1, and ANXA1. Clinicians should take these factors into consideration when making decision to initiate MSC therapy in patients with severe COVID-19. |
| first_indexed | 2024-03-08T22:41:38Z |
| format | Article |
| id | doaj.art-a74c9b25ca614aa7b23375336555ea90 |
| institution | Directory Open Access Journal |
| issn | 1757-6512 |
| language | English |
| last_indexed | 2024-03-08T22:41:38Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Stem Cell Research & Therapy |
| spelling | doaj.art-a74c9b25ca614aa7b23375336555ea902023-12-17T12:08:43ZengBMCStem Cell Research & Therapy1757-65122023-12-0114111110.1186/s13287-023-03573-4Plasma proteomics-based biomarkers for predicting response to mesenchymal stem cell therapy in severe COVID-19Tian-Tian Li0Wei-Qi Yao1Hai-Bo Dong2Ze-Rui Wang3Zi-Ying Zhang4Meng-Qi Yuan5Lei Shi6Fu-Sheng Wang7Senior Department of Infectious Diseases, The Fifth Medical Centre of PLA General Hospital, National Clinical Research Center for Infectious DiseasesDepartment of Biology and Medicine, Hubei University of TechnologyWuhan Optics Valley Vcanbio Cell & Gene Technology Co., Ltd.Department of Gastroenterology, First Medical Center of Chinese, PLA General HospitalSenior Department of Infectious Diseases, The Fifth Medical Centre of PLA General Hospital, National Clinical Research Center for Infectious DiseasesSenior Department of Infectious Diseases, The Fifth Medical Centre of PLA General Hospital, National Clinical Research Center for Infectious DiseasesSenior Department of Infectious Diseases, The Fifth Medical Centre of PLA General Hospital, National Clinical Research Center for Infectious DiseasesSenior Department of Infectious Diseases, The Fifth Medical Centre of PLA General Hospital, National Clinical Research Center for Infectious DiseasesAbstract Background The objective of this study was to identify potential biomarkers for predicting response to MSC therapy by pre-MSC treatment plasma proteomic profile in severe COVID-19 in order to optimize treatment choice. Methods A total of 58 patients selected from our previous RCT cohort were enrolled in this study. MSC responders (n = 35) were defined as whose resolution of lung consolidation ≥ 51.99% (the median value for resolution of lung consolidation) from pre-MSC to 28 days post-MSC treatment, while non-responders (n = 23) were defined as whose resolution of lung consolidation < 51.99%. Plasma before MSC treatment was detected using data-independent acquisition (DIA) proteomics. Multivariate logistic regression analysis was used to identify pre-MSC treatment plasma proteomic biomarkers that might distinguish between responders and non-responders to MSC therapy. Results In total, 1101 proteins were identified in plasma. Compared with the non-responders, the responders had three upregulated proteins (CSPG2, CTRB1, and OSCAR) and 10 downregulated proteins (ANXA1, AGRG6, CAPG, DDX55, KV133, LEG10, OXSR1, PICAL, PTGDS, and S100A8) in plasma before MSC treatment. Using logistic regression model, lower levels of DDX55, AGRG6, PICAL, and ANXA1 and higher levels of CTRB1 pre-MSC treatment were predictors of responders to MSC therapy, with AUC of the ROC at 0.910 (95% CI 0.818–1.000) in the training set. In the validation set, AUC of the ROC was 0.767 (95% CI 0.459–1.000). Conclusions The responsiveness to MSC therapy appears to depend on baseline level of DDX55, AGRG6, PICAL, CTRB1, and ANXA1. Clinicians should take these factors into consideration when making decision to initiate MSC therapy in patients with severe COVID-19.https://doi.org/10.1186/s13287-023-03573-4COVID-19Mesenchymal stem cellsProteomicsPredictive model |
| spellingShingle | Tian-Tian Li Wei-Qi Yao Hai-Bo Dong Ze-Rui Wang Zi-Ying Zhang Meng-Qi Yuan Lei Shi Fu-Sheng Wang Plasma proteomics-based biomarkers for predicting response to mesenchymal stem cell therapy in severe COVID-19 Stem Cell Research & Therapy COVID-19 Mesenchymal stem cells Proteomics Predictive model |
| title | Plasma proteomics-based biomarkers for predicting response to mesenchymal stem cell therapy in severe COVID-19 |
| title_full | Plasma proteomics-based biomarkers for predicting response to mesenchymal stem cell therapy in severe COVID-19 |
| title_fullStr | Plasma proteomics-based biomarkers for predicting response to mesenchymal stem cell therapy in severe COVID-19 |
| title_full_unstemmed | Plasma proteomics-based biomarkers for predicting response to mesenchymal stem cell therapy in severe COVID-19 |
| title_short | Plasma proteomics-based biomarkers for predicting response to mesenchymal stem cell therapy in severe COVID-19 |
| title_sort | plasma proteomics based biomarkers for predicting response to mesenchymal stem cell therapy in severe covid 19 |
| topic | COVID-19 Mesenchymal stem cells Proteomics Predictive model |
| url | https://doi.org/10.1186/s13287-023-03573-4 |
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