The differences in cytokine signatures between severe fever with thrombocytopenia syndrome (SFTS) and hemorrhagic fever with renal syndrome (HFRS).

Severe fever with thrombocytopenia syndrome (SFTS) virus and hantavirus are categorized under the Bunyavirales order. The severe disease progression in both SFTS and hemorrhagic fever with renal syndrome (HFRS) is associated with cytokine storms. This study aimed to explore the differences in cytokine profiles and immune responses between the two diseases. A cross-sectional, single-center study involved 100 participants, comprising 46 SFTS patients, 48 HFRS patients, and 6 healthy controls. The study employed the Luminex cytokine detection platform to measure 48 cytokines. The differences in cytokine profiles and immune characteristics between the two diseases were further analyzed using multiple linear regression, principal component analysis, and random forest method. Among the 48 cytokines tested, 30 showed elevated levels in SFTS and/or HFRS compared to the healthy control group. Furthermore, there were 19 cytokines that exhibited significant differences between SFTS and HFRS. Random forest analysis suggested that TRAIL and CTACK were predictive of SFTS, while IL2Ralpha, MIG, IL-8, IFNalpha2, HGF, SCF, MCP-3, and PDGFBB were more common with HFRS. It was further verified by the receiver operating characteristic with area under the curve >0.8 and P-values <0.05, except for TRAIL. Significant differences were observed in the cytokine profiles of SFTS and HFRS, with TRAIL, IL2Ralpha, MIG, and IL-8 being the top 4 cytokines that most clearly distinguished the two diseases. IMPORTANCE: SFTS and HFRS differ in terms of cytokine immune characteristics. TRAIL, IL-2Ralpha, MIG, and IL-8 were the top 4 that differed markedly between SFTS and HFRS.

Identification of CCL20 as a prognostic predictor for severe fever with thrombocytopenia syndrome based on plasma proteomics.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS), a lethal tick-borne hemorrhagic fever, prompted our investigation into prognostic predictors and potential drug targets using plasma Olink Proteomics. METHODS: Employing the Olink assay, we analyzed 184 plasma proteins in 30 survivors and 8 non-survivors of SFTS. Validation was performed in a cohort of 154 SFTS patients using enzyme-linked immunosorbent assay. We utilized the Drug Gene Interaction database to identify protein-drug interactions. RESULTS: Non-survivors exhibited 110 differentially expressed proteins (DEPs) compared to survivors, with functional enrichment in the cell chemotaxis-related pathway. Thirteen DEPs, including C-C motif chemokine 20 (CCL20), calcitonin gene-related peptide alpha and Pleiotrophin, were associated with multiple organ dysfunction syndrome. CCL20 emerged as the top predictor of death, demonstrating an area under the curve of 1 (P = .0004) and 0.9033 (P < .0001) in the discovery and validation cohort, respectively. Patients with CCL20 levels exceeding 45.74 pg/mL exhibited a fatality rate of 45.65%, while no deaths occurred in those with lower CCL20 levels. Furthermore, we identified 202 FDA-approved drugs targeting 37 death-related plasma proteins. CONCLUSIONS: Distinct plasma proteomic profiles characterize SFTS patients with different outcomes, with CCL20 emerging as a novel, sensitive, accurate, and specific biomarker for predicting SFTS prognosis.

MCP-3 as a prognostic biomarker for severe fever with thrombocytopenia syndrome: a longitudinal cytokine profile study.

Introduction: Severe fever with thrombocytopenia syndrome (SFTS) is characterized by a high mortality rate and is associated with immune dysregulation. Cytokine storms may play an important role in adverse disease regression, this study aimed to assess the validity of MCP-3 in predicting adverse outcomes in SFTS patients and to investigate the longitudinal cytokine profile in SFTS patients. Methods: The prospective study was conducted at Yantai Qishan Hospital from May to November 2022. We collected clinical data and serial blood samples during hospitalization, patients with SFTS were divided into survival and non-survival groups based on the clinical prognosis. Results: The levels of serum 48 cytokines were measured using Luminex assays. Compared to healthy controls, SFTS patients exhibited higher levels of most cytokines. The non-survival group had significantly higher levels of 32 cytokines compared to the survival group. Among these cytokines, MCP-3 was ranked as the most significant variable by the random forest (RF) model in predicting the poor prognosis of SFTS patients. Additionally, we validated the predictive effects of MCP-3 through receiver operating characteristic (ROC) curve analysis with an AUC of 0.882 (95% CI, 0.787-0.978, P <0.001), and the clinical applicability of MCP-3 was assessed favorably based on decision curve analysis (DCA). The Spearman correlation analysis indicated that the level of MCP-3 was positively correlated with ALT, AST, LDH, α-HBDH, APTT, D-dimer, and viral load (P<0.01). Discussion: For the first time, our study identified and validated that MCP-3 could serve as a meaningful biomarker for predicting the fatal outcome of SFTS patients. The longitudinal cytokine profile analyzed that abnormally increased cytokines were associated with the poor prognosis of SFTS patients. Our study provides new insights into exploring the pathogenesis of cytokines with organ damage and leading to adverse effects.

Development and validation of the PLNA score to predict cytokine storm in acute-phase SFTS patients: A single-center cohort study.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease known for its high mortality rate and its correlation with Cytokine Storms (CS). Timely detection of CS is crucial for improving the prognosis of the disease. The objective of this investigation was to develop a model for identifying cytokine storms in the acute phase of SFTS. METHODS: A total of 245 patients diagnosed with SFTS were included in this study between January 2020 and July 2022. Among them, 184 patients were part of the training set, while 61 patients were part of the validation set. Variables identified by LASSO were subsequently included in a multivariate logistic regression analysis to determine independent predictors. Subsequently, a nomogram was then developed to predict the likelihood of CS in SFTS patients. The predictive efficacy and clinical applicability of the nomogram model were further assessed through ROC analysis and the DCA curve. RESULTS: Following LASSO analysis, a total of 11 indicators were included in multivariate logistic regression analysis. The findings indicated that PLT (OR 0.865, P < 0.001), LDH (OR 1.002, P < 0.001), Na+ (OR 1.155, P = 0.005), and ALT (OR 1.019, P < 0.001) serve as independently predictors of CS in the acute phase of SFTS. Furthermore, a nomogram named the PLNA was constructed by integrating these four factors. The PLNA model exhibited favorable predictive accuracy with an AUC of 0.958. Moreover, the PLNA model exhibited excellent clinical applicability in both the training and validation sets, as evidenced by the DCA curve. CONCLUSIONS: The PLNA model, constructed using clinical indicators, can predict the probability of cytokine storm in the acute phase of SFTS patients.

A model based on meta-analysis to evaluate poor prognosis of patients with severe fever with thrombocytopenia syndrome.

Background: Early identification of risk factors associated with poor prognosis in Severe fever with thrombocytopenia syndrome (SFTS) patients is crucial to improving patient survival. Method: Retrieve literature related to fatal risk factors in SFTS patients in the database, extract the risk factors and corresponding RRs and 95% CIs, and merge them. Statistically significant factors were included in the model, and stratified and assigned a corresponding score. Finally, a validation cohort from Yantai Qishan Hospital in 2021 was used to verify its predictive ability. Result: A total of 24 articles were included in the meta-analysis. The model includes six risk factors: age, hemorrhagic manifestations, encephalopathy, Scr and BUN. The analysis of lasso regression and multivariate logistic regression shows that model score is an independent risk factor (OR = 1.032, 95% CI 1.002-1.063, p = 0.034). The model had an area under the curve (AUC) of 0.779 (95% CI 0.669-0.889, P<0.001). The validation cohort was divided into four risk groups with cut-off values. Compared with the low-medium risk group, the mortality rate of high-risk and very high-risk patients was more significant (RR =5.677, 95% CI 4.961-6.496, P<0.001). Conclusion: The prediction model for the fatal outcome of SFTS patients has shown positive outcomes.Systematic review registration:https://www.crd.york.ac.uk/prospero/ (CRD42023453157).