Table 1.
Baseline Characteristics of SFTS Patients.
Fig 1.
Study Design Schema: Multi-Cohort Patient Allocation for Transcriptomic Discovery and Serum Biomarker Validation in SFTS Prognosis.
Center A (the First Affiliated Hospital, College of Medicine, Zhejiang University): Discovery Cohort (n = 15) for transcriptomic analysis; Validation Cohort 1 (n = 103) for clinical correlation; Validation Cohort 2A (n = 43) for initial serum biomarker testing. Center B (Nanjing Drum Tower Hospital): Validation Cohort 2B (n = 108) for external serum verification. Differential analysis was performed according to the final outcome (recovery/ death) of patients in all validation cohorts.
Fig 2.
Transcriptomic differences between the deceased group and the recovered group.
A Heat map of the z-scores for 1924 significantly differentially expressed genes identified using RNA-seq (P < 0.05, exact test, likelihood ratio test, and quasi-likelihood F test) shows that genes distinguish the overall Deceased patients’ samples from the Recovered patients’ samples. Red represents increased relative expression, and blue represents decreased relative expression. B Volcano plot of differential gene expression in overall SFTS deceased group and recovered group with P < 0.05, exact test, likelihood ratio test, and quasi-likelihood F test. This analysis identified 1,445 upregulated (red) and 479 downregulated (blue) genes in the deceased group, highlighting a widespread transcriptomic dysregulation associated with fatal outcomes. FC, fold change; CPM, read count per million. C Gene Ontology (GO) enrichment analysis of all DEGs. The bar chart shows the top 20 enriched biological process pathways, with the most significant finding being the enrichment of pathways related to nervous system development in the deceased group. D Heat map of the z-scores for 146 DEGs identified using RNA-seq (P < 0.05, exact test, likelihood ratio test, and quasi-likelihood F test) shows that genes distinguish the Deceased patients’ samples from the Recovered patients’ samples at S1. E Volcano plot of DEGs between deceased and SFTS recovered groups at the S1 stage. This identified 146 DEGs, indicating that significant transcriptomic changes predictive of outcome are already present early in the disease course. F Top 10 Enriched Biological Process pathways by GO analysis of DEGs between Deceased patients and Recovered patients at S1. This finding is crucial as it shows that early-stage differences are enriched in pathways related to coagulation and brain development, consistent with the clinical progression of severe SFTS. Hierarchical diagram from the GO database illustrating the relationship between the enriched nervous system pathways. This visualizes how “brain development” (identified in the S1 analysis) is a specific sub-pathway of “nervous system development” (identified in the overall analysis). H Gene Set Enrichment Analysis (GSEA) plot for the “brain development” pathway using all samples. The negative enrichment score (NES) confirms that genes associated with this pathway are significantly downregulated in the deceased group compared to the recovered group over the entire disease course. I GSEA plot for the “brain development” pathway using only S1 stage samples. The result demonstrates that the downregulation of these crucial neural development genes is a very early event, detectable in the initial febrile stage of the illness in patients with a fatal prognosis.
Table 2.
Comparison of Neurological Symptoms and Cerebrospinal Fluid (CSF) Parameters of Patients with SFTS between the Recovered and Deceased groups of the Sampling SFTS Patients, Including Subgroup Analysis in the S1 Stage.
Table 3.
Comparison of Neurological Symptoms and Cerebrospinal Fluid (CSF) Parameters of Patients with SFTS Between the Recovered and Deceased Groups of the Validation Cohort 1, Including Subgroup Analysis in the S1 Stage.
Fig 3.
The Proportion of Different Neurological Symptoms in The Deceased Group and The Recovered Group of the Validation Cohort 1 during S1 and Overall Stage Respectively.
The Vertical Axis Represents the percentage of patients in this group who exhibited certain neurological symptom. A,B,C,D,E,F represents the proportion of patients with consciousness disorder, headache, restlessness, tremor, convulsion and mental state change in all patients and patients in S1 respectively. Statistical significance between the deceased and recovered groups for each symptom was determined using Fisher’s exact test.
Fig 4.
Variations of MMP8 levels in gene expression and serum concentrations.
A Histogram of gene expression differences between the neurological symptom-positive (NS) and negative (noNS) groups. Red indicates increased relative expression, and blue indicates decreased relative expression. B Volcano plot of differential gene-expression in SFTS patients with neurological symptoms compared to those without. Genes with a q-value < 0.05 are highlighted. Red and blue points indicate significant upregulation or downregulation, respectively, while gray points are not significant. FC, fold change. C Comparison of serum MMP8 concentrations between patient groups with lethal neurological symptoms, those without, and healthy controls. D Comparison of serum MMP8 concentrations among the deceased group, the recovered group, and healthy controls. For (C) and (D), statistical significance across the three groups was assessed using the Kruskal-Wallis test with Dunn’s multiple comparisons test. E Receiver operating characteristic (ROC) curve demonstrating the predictive ability of MMP8 for mortality in Validation Cohort 2A. The area under the curve (AUC) and optimal cut-off value are provided. F ROC curve analysis of MMP8 expression for distinguishing non-survivors from survivors in the Validation Cohort 2B.
Table 4.
Baseline Characteristics of SFTS Patients in Validation Cohort 2A and Validation Cohort 2B.
Fig 5.
Nomogram for 30-day mortality risk prediction.
The nomogram assigns points for clinical variables (MMP8, CK, Cr, D-dimer) based on patient values. Points are summed to calculate Total Points (range: 0-180), which correspond to a Linear Predictor value. The Linear Predictor is then converted to predicted 30-day Mortality Risk (range: 0.1-0.9).
Fig 6.
Model Performance Evaluation: Discrimination and Calibration.
A Receiver operating characteristic (ROC) curves demonstrating the discrimination ability of different prognostic models for 30-day clinical outcomes. The MCCD model (AUC = 0.843) shows superior predictive performance compared to MMP8 (AUC = 0.827), APTT (AUC = 0.726), creatinine (Cr, AUC = 0.713), creatine kinase-based (CK, AUC = 0.705), and D-dimer (AUC = 0.701) models. B Calibration plot assessing agreement between predicted probabilities and observed outcomes. Black stepped line represents actual event rates, gray diagonal denotes perfect calibration, and blue curve shows bias-corrected calibration through bootstrapping (n = 50). Model calibration metrics include a mean absolute error of 0.04 and median (0.5 quantile) absolute error of 0.071, indicating excellent agreement between predicted and observed event probabilities.