Identification of early prediction biomarkers of severity in patients with severe fever with thrombocytopenia syndrome based on plasma proteomics
BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is a newly emerging infectious disease. Given its rapid disease progression and high mortality rate, early warning is crucial in improving the outcomes, However, to date, relevant comprehensive predictors or an effective prediction model a...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2025.1514388/full |
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| author | Qian Zhang Qian Zhang Zhengyi Jiang Nan Jiang Luchen Shi Jiaying Zhao Jie Zhao Jie Zhao Ke Ouyang Ke Ouyang Huaying Huang Huaying Huang Yaqin Zhang Yan Dai Nannan Hu Ping Shi Yaping Han Ke Jin Jun Li |
| author_facet | Qian Zhang Qian Zhang Zhengyi Jiang Nan Jiang Luchen Shi Jiaying Zhao Jie Zhao Jie Zhao Ke Ouyang Ke Ouyang Huaying Huang Huaying Huang Yaqin Zhang Yan Dai Nannan Hu Ping Shi Yaping Han Ke Jin Jun Li |
| author_sort | Qian Zhang |
| collection | DOAJ |
| description | BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is a newly emerging infectious disease. Given its rapid disease progression and high mortality rate, early warning is crucial in improving the outcomes, However, to date, relevant comprehensive predictors or an effective prediction model are still poorly explored.MethodsA plasma proteomic profile was performed at early stages in patients with SFTS. Functional clustering analysis was used to select the candidate proteins and then validate their expression by ELISA. A cohort consisting of 190 patients with SFTS was used to develop the predictive model for severe illness and subsequently validate it in a new cohort consisting of 93 patients with SFTS.ResultsA significant increase in plasma proteins associated with various functional clusters, such as the proteasomal protein catabolic process, phagocytosis, and humoral immune response, was observed in severe SFTS patients. High levels of four proteins including NID1, HSP90α, PSMA1, and VCAM1 were strongly correlated with multi-organ damage and disease progression. A prediction model was developed at the early stage to accurately predict severe conditions with the area under the curve of 0.931 (95% CI, 0.885, 0.963).ConclusionThe proteomic signatures identified in this study provide insights into the potential pathogenesis of SFTS. The predictive models have substantial clinical implications for the early identification of SFTS patients who may progress to severe conditions. |
| format | Article |
| id | doaj-art-22ca4f06d26b4bbf813755d392e7c2b4 |
| institution | Kabale University |
| issn | 1664-302X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Microbiology |
| spelling | doaj-art-22ca4f06d26b4bbf813755d392e7c2b42025-02-05T07:32:47ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2025-02-011610.3389/fmicb.2025.15143881514388Identification of early prediction biomarkers of severity in patients with severe fever with thrombocytopenia syndrome based on plasma proteomicsQian Zhang0Qian Zhang1Zhengyi Jiang2Nan Jiang3Luchen Shi4Jiaying Zhao5Jie Zhao6Jie Zhao7Ke Ouyang8Ke Ouyang9Huaying Huang10Huaying Huang11Yaqin Zhang12Yan Dai13Nannan Hu14Ping Shi15Yaping Han16Ke Jin17Jun Li18Department of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Respiratory Disease, Yixing No. 2 People’s Hospital, Yixing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, Nanjing Second Hospital, Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaSchool of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Infectious Disease, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaBackgroundSevere fever with thrombocytopenia syndrome (SFTS) is a newly emerging infectious disease. Given its rapid disease progression and high mortality rate, early warning is crucial in improving the outcomes, However, to date, relevant comprehensive predictors or an effective prediction model are still poorly explored.MethodsA plasma proteomic profile was performed at early stages in patients with SFTS. Functional clustering analysis was used to select the candidate proteins and then validate their expression by ELISA. A cohort consisting of 190 patients with SFTS was used to develop the predictive model for severe illness and subsequently validate it in a new cohort consisting of 93 patients with SFTS.ResultsA significant increase in plasma proteins associated with various functional clusters, such as the proteasomal protein catabolic process, phagocytosis, and humoral immune response, was observed in severe SFTS patients. High levels of four proteins including NID1, HSP90α, PSMA1, and VCAM1 were strongly correlated with multi-organ damage and disease progression. A prediction model was developed at the early stage to accurately predict severe conditions with the area under the curve of 0.931 (95% CI, 0.885, 0.963).ConclusionThe proteomic signatures identified in this study provide insights into the potential pathogenesis of SFTS. The predictive models have substantial clinical implications for the early identification of SFTS patients who may progress to severe conditions.https://www.frontiersin.org/articles/10.3389/fmicb.2025.1514388/fullsevere fever with thrombocytopenia syndromeproteomicsbiomarkerpredictionprognosis |
| spellingShingle | Qian Zhang Qian Zhang Zhengyi Jiang Nan Jiang Luchen Shi Jiaying Zhao Jie Zhao Jie Zhao Ke Ouyang Ke Ouyang Huaying Huang Huaying Huang Yaqin Zhang Yan Dai Nannan Hu Ping Shi Yaping Han Ke Jin Jun Li Identification of early prediction biomarkers of severity in patients with severe fever with thrombocytopenia syndrome based on plasma proteomics Frontiers in Microbiology severe fever with thrombocytopenia syndrome proteomics biomarker prediction prognosis |
| title | Identification of early prediction biomarkers of severity in patients with severe fever with thrombocytopenia syndrome based on plasma proteomics |
| title_full | Identification of early prediction biomarkers of severity in patients with severe fever with thrombocytopenia syndrome based on plasma proteomics |
| title_fullStr | Identification of early prediction biomarkers of severity in patients with severe fever with thrombocytopenia syndrome based on plasma proteomics |
| title_full_unstemmed | Identification of early prediction biomarkers of severity in patients with severe fever with thrombocytopenia syndrome based on plasma proteomics |
| title_short | Identification of early prediction biomarkers of severity in patients with severe fever with thrombocytopenia syndrome based on plasma proteomics |
| title_sort | identification of early prediction biomarkers of severity in patients with severe fever with thrombocytopenia syndrome based on plasma proteomics |
| topic | severe fever with thrombocytopenia syndrome proteomics biomarker prediction prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2025.1514388/full |
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