Proteomics-based machine learning model for predicting secondary infection in HBV-related liver failure

Domain: chemistry
Published: 2026-03-03
Editorial outcome: major_revision

Abstract
Patients with Hepatitis B Virus-related liver failure are highly vulnerable to secondary infections (SI), yet early predictive tools remain limited. In this work, we aim to develop and validate a plasma proteomics–based model for early SI risk assessment. In a prospective multicenter study, 114 patients are enrolled in the discovery cohort, 60 each in two validation cohorts. Untargeted proteomics is used to identify SI-related proteins, followed by Minimum Redundancy Maximum Relevance based feature selection and logistic regression modeling. Targeted proteomics and ELISA are applied for external validation. Inflammatory and coagulation pathway dysregulation is strongly associated with SI. A final model including Lysozyme (LYZ), Calmodulin 1 (CALM1), Serpin Family D Member 1 (SERPIND1), Dermatopontin (DPT), total bilirubin, and AST show excellent discrimination (area under the receiver operating characteristic curve (AUROC) 0.980 in discovery; 0.873 in validation), outperforming C-reactive protein (CRP), white blood cell (WBC), and Neutrophil percentage (NE%). It also predicts 28-day mortality better than Chronic Liver Failure–Consortium Acute-on-Chronic Liver Failure score (CLIF-C ACLF) and Model for End-Stage Liver Disease (MELD). ELISA measurements in validation cohort 2 yield consistent trends, and an ELISA-based model achieve an AUROC of 0.883. This proteomics-derived model reliably identifies patients at high SI risk and supports early clinical intervention.

Introduction
Hepatitis B virus (HBV) infection remains a major global public health burden. As the disease progresses to liver cirrhosis, patients frequently develop serious complications, including ascites, bacterial infections (BI), hepatic encephalopathy, and variceal bleeding. Studies have shown that the incidence of BI in cirrhotic patients ranges from 32% to 40%1,2. Compared to those without BI, patients who develop BI show an approximately 30% increase in mortality, a significantly lower rate of clinical remission, and a markedly higher incidence of shock and new-onset organ failure. The initial episode of BI often marks a critical turning point in disease progression3. Notably, approximately one-third of BI are secondary infections (SI) that emerge after hospital admission, without clinical signs of infection at the time of admission1,4. Therefore, early identification of patients at high risk for SI, even in the absence of initial signs, holds considerable clinical significance.

Specific risk factors for SI in hospitalized cirrhotic patients remain poorly defined and require further investigation. Several studies have indicated that empirical administration of antibiotics or proton pump inhibitors (PPIs) at admission may increase the risk of infection, particularly when the daily defined dose (DDD) of PPIs exceeds one5. A retrospective analysis of 2,864 cirrhotic patients identified baseline bacterial infection, Model for End-Stage Liver Disease (MELD) score >20, systemic inflammatory response syndrome (SIRS), and use of lactulose or rifaximin as independent risk factors for SI. However, the prediction model based on these variables demonstrated limited accuracy, with sensitivity and specificity of 0.67 and 0.63, respectively6. A separate study employing machine learning reported an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.784, along with sensitivity and specificity of 0.712 and 0.702, respectively, which remained below the threshold for optimal predictive performance7. Most existing models depend on non-specific variables, reducing their accuracy in identifying high-risk individuals. Thus, there is an urgent need to explore more specific and predictive biomarkers to enhance early identification and risk stratification of patients susceptible to SI.

With advances in proteomics, identifying potential biomarkers and elucidating disease mechanisms at the molecular level has become increasingly feasible8. Proteomics-based prediction models offer superior specificity for disease forecasting and enable mechanistic insights into disease progression. Previous studies have applied proteomic strategies to develop noninvasive diagnostic models for evaluating liver fibrosis, inflammation, and steatosis in patients with alcoholic liver disease (ALD), offering new perspectives for clinical screening. Additional research has identified protein biomarkers associated with infection susceptibility in cirrhotic patients, thereby advancing the understanding of underlying molecular mechanisms10. Given these unique advantages, this study aims to identify and validate proteomic biomarkers predictive of SI in patients with HBV-related cirrhosis through a prospective, multicenter design. In this work, a prediction model integrating both proteomic and clinical features will be constructed to support early detection and risk stratification of high-risk individuals.

Results
Baseline Characteristics
A total of 114 patients were enrolled in the discovery cohort, 60 patients were included in the validation cohort 1, and another 60 patients were included in the validation cohort 2. In all cohorts, patients who developed SI were matched to non-infected controls at a 1:2 or 1:1 ratio. The two groups were comparable in terms of sex and age distribution. In the discovery cohort, the proportion of male patients was 70%, with a median age of 55 years. In the validation cohort 1, 75% were male, with a median age of 50 years. In the validation cohort 2, 80% were male, with a median age of 51 years. Among liver-related complications, ascites was frequently observed, with a prevalence of 68% in the discovery cohort, 63% in the validation cohort 1, and 67% in the validation cohort 2. Most enrolled patients exhibited signs of severe liver dysfunction, as reflected by significantly elevated levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (Tbil), and international normalized ratio (INR), indicating hepatocellular injury, impaired bilirubin metabolism, and coagulopathy. Inflammatory markers related to infection, including white blood cell count (WBC), neutrophil percentage (NE%), and C-reactive protein (CRP), were relatively low at admission, suggesting that no patients presented with active infection at the time of enrollment. Both the MELD score and the CLIF-C Acute-on-Chronic Liver Failure (CLIF-C ACLF) score were elevated, indicating a poor overall prognosis in the study population. In-hospital mortality reached 8% in the discovery cohort, 10% in the validation cohort 1, and 11% in the validation cohort 2(Table 1). Regarding the distribution of SI sites, 16 patients in the discovery cohort developed peritoneal infections, 15 had pulmonary infections, 2 had cholecystitis, 4 developed urinary infections, and 3 had bloodstream infections (Figure S2a). In the validation cohort 1, 9 patients developed peritoneal infections, 5 had pulmonary infections, 4 had cholecystitis, 1 developed urinary infection, and 1 had bloodstream infections (Figure S2b). In the validation cohort 2, 9 patients developed peritoneal infections, 5 had pulmonary infections, 4 had cholecystitis, 1 developed urinary infection, and 1 had bloodstream infections (Figure S2c). In the discovery cohort, the median time from baseline to the occurrence of SI was 6.5 (5–9) days; In validation cohort 1, the median time was 7 (5–10.25) days; and in validation cohort 2, it was 6.5 (5–9.25) days (Figure S2d). Overall, peritoneal and pulmonary infections were the most common types of SI, consistent with the known infection patterns in patients with HBV-related liver failure 5.

Subsequently, we compared the baseline biochemical characteristics between patients with and without SI across the three cohorts. The results showed consistent patterns among all cohorts: patients who developed SI exhibited worse liver function, higher levels of jaundice and inflammatory markers, and more impaired coagulation function compared with non-SI patients. Moreover, the 28-day mortality rate was significantly higher in SI patients, indicating that patients who developed SI were in poorer baseline condition (Supplementary Data 1).

Specific proteins were then selected using the minimum redundancy maximum relevance (mRMR) algorithm combined with machine learning methods to construct a predictive model. In the validation cohort, plasma samples were prospectively collected and analyzed using targeted proteomics to quantify the expression levels of the candidate proteins, thereby evaluating the model's predictive performance. The overall study design and analytical workflow are illustrated in Figure 1.

Plasma Untargeted Proteomic Features in Patients with SI
First, we performed quality control on the proteomic data. As shown in Figure S3a–b, the peptide length distribution exhibited the expected tryptic pattern, and proteins displayed a physiologically consistent molecular weight–pI distribution. After applying data-completeness filtering, 4,901 proteins were retained for downstream analysis (Figure S3c). Protein coverage categories were well balanced, and most proteins were supported by multiple unique peptides (Figure S3d). Principal component analysis (PCA) based on all quantified proteins showed no evident site-driven separation among samples from the three study centers. Samples from Beijing Ditan Hospital (site 1), Huashan Hospital (site 2), and the First Affiliated Hospital of Zhejiang University School of Medicine (site 3) were largely intermingled across PC1, PC2, and PC3, indicating minimal batch effects attributable to collection location. Moreover, GR and NGR samples did not cluster by site, further supporting that inter-center differences did not introduce systematic variability into the proteomic profiles. (Figure S3e). In the discovery cohort, untargeted plasma proteomic analysis identified 974 differentially expressed proteins (DEPs), including 159 upregulated and 815 downregulated proteins in patients with SI. (Figure 2a-b and Supplementary Data 2). Functional categorization revealed that 27% of the DEPs were associated with inflammation, 19% with metabolic processes, and 9% with coagulation (Figure 2c). Further pathway enrichment analysis (KEGG and Wiki Pathway) showed significant enrichment in platelet activation, complement and coagulation cascades, and various inflammation-related signaling pathways (Figure S4a–b, Supplementary Data 3-4). Gene set enrichment analysis (GSEA) further confirmed significant enrichment of coagulation- and platelet activation-related gene sets in the SI group (Figure S4c), suggesting a potential role for coagulation system activation in the development of SI in patients with cirrhosis. These findings indicated that inflammatory and coagulation pathways may contribute to SI pathogenesis in patients with HBV-related liver failure. Previous studies have demonstrated a bidirectional interaction between inflammation and coagulation, wherein inflammatory stimuli activate the coagulation cascade, and coagulation proteases can trigger inflammatory signaling via cell surface receptors 11.

To further investigate the interplay between inflammation and coagulation pathways, PPI analysis was performed on the DEPs. Functional enrichment focused on proteins involved in complement activation, coagulation, and platelet function. Several immunoregulatory proteins, including PLAT, CFHR3, VSIG4, and CLU, were enriched within complement and coagulation networks (Figure S4d). In addition, PTGS1, an inflammation-associated protein, was enriched in the platelet activation pathway, suggesting that this pathway may link inflammatory and coagulative responses and support the hypothesis of cross-regulation (Figure S4d).

To determine the relationships between commonly used clinical markers of inflammation and coagulation and the identified proteins, Spearman correlation analyses were conducted to evaluate associations between protein levels and CRP, NE%, INR, and prothrombin time (PT). Among the top 20 proteins correlated with CRP, PLAT (a key protein in fibrinolysis) showed a significant negative correlation (Figure 2d, Supplementary Data 5). Previous studies have reported decreased PLAT expression during BI and systemic inflammatory responses, which potentially exacerbates the inflammatory state12. In contrast, scatter plot analysis revealed strong positive correlations between CRP and several inflammatory proteins, including CALCA, CRP, and SAA2 (P<0.001) (Figure 2e).

Among proteins correlated with NE%, HRG and PLG, both closely related to coagulation, exhibited negative correlations, whereas PLAUR, a fibrinolysis-related protein, showed a positive correlation (Figure 2d, Supplementary Data 6). Previous studies have indicated that PLG and PLAUR contribute to the resolution of inflammation and may exert protective effects in infectious diseases characterized by inflammatory responses13. In contrast, HRG has been associated with macrophage activation and inflammation in chronic liver disease14. Further analysis showed a significant positive correlation between NE% and IL1RL1, a pro-inflammatory receptor (P<0.001) (Figure 2f).

Correlation analyses of PT and INR identified a range of inflammatory modulators among the top 20 associated proteins, including TNFAIP6, IL1RL1, CLEC11A, and SELL, all of which demonstrated significant positive correlations (Figure 2d, Supplementary Data 7-8). Downregulation of TNFAIP6 has been shown to promote fibrinolytic pathway activation 15, while IL1RL1 has been incorporated into biomarker models for predicting venous thromboembolism. The top three proteins most strongly correlated with PT and INR also exhibited strong positive associations with inflammation-related proteins in scatter plot analyses(P<0.001) (Figure 2e–h).

Collectively, these findings revealed strong associations between inflammation, coagulation, and their regulatory proteins, suggesting that the pathogenesis of SI may be driven by concurrent dysregulation of both pathways. These results underscore the potential utility of jointly targeting inflammatory and coagulation-related proteins to improve the identification of high-risk patients. To further screen for clinically applicable biomarkers, targeted proteomic analyses were conducted, focusing on proteins related to inflammation, coagulation, and liver function, to construct a more specific and robust predictive model.

Targeted Plasma Proteomic Features in Patients with SI
A total of 60 patients were prospectively enrolled from three clinical centers, and plasma samples were collected within 48 hours of admission for targeted proteomic analysis to validate DEPs identified during the untargeted discovery phase. In the validation cohort, 31 DEPs were successfully detected, including 5 upregulated and 26 downregulated proteins in patients with SI. Protein abundance changes were visualized using heatmaps (Figure 3a–b), with detailed information provided in Supplementary Data 9. Among these 31 proteins, 24% were related to inflammation or immune responses, while 27% were involved in coagulation pathways, together accounting for over half of the identified proteins. These findings further emphasized the pivotal role of inflammation–coagulation imbalance in the development of SI in patients with HBV-related cirrhosis (Figure 3c).

To clarify the relationship between protein expression and clinical inflammatory/coagulation markers, we performed Spearman correlation analyses between protein levels and CRP, NE%, INR, and PT. The top 20 proteins correlated with each marker were identified (Supplementary Data 10-13). Among the proteins most strongly correlated with CRP and NE%, several were coagulation-related, including F2, ITGA2B, SERPINC1, TF, and PLG (Figure 3d). F2 serves as the precursor of thrombin and has been implicated in the progression of HBV-related liver disease 16. ITGA2B has been identified as a key component of monocyte–platelet aggregates in single-cell RNA sequencing studies of CHLB17. SERPINC1 has been reported to attenuate hepatic inflammation by inhibiting leukocyte adhesion and migration18. Both TF and PLG are key components of the coagulation and fibrinolytic systems, and their decreased expression in infected patients suggested a close link between coagulation dysfunction and SI. Correlation scatter plots confirmed significant positive associations between CRP, NE%, and these inflammation/coagulation-related proteins (P < 0.001) (Figure 3e–f).

Among the top proteins correlated with PT and INR, several acute-phase inflammatory proteins (such as SAA1, S100A9, CRP, and LYZ) showed strong positive associations (Figure 2d). SAA1 and CRP are classic acute-phase reactants, and their strong correlation with coagulation parameters reflects the synergistic role of systemic inflammation and coagulation dysfunction in disease progression during infection. LYZ, a lysozyme with broad-spectrum antibacterial activity, has been shown in previous studies to promote hemostasis and wound healing when combined with Cu2+-loaded nanofibers, suggesting its potential role in coagulation regulation 19. S100A9 has also been reported in multiple proteomic studies to covary with coagulation-associated proteins20. Scatter plots for the top three most strongly correlated proteins further confirmed these associations, all showing significant positive correlations (P < 0.001) (Figure 3e–h).

Identification of Predictive Factors for SI
To more effectively identify high-risk patients with HBV-related cirrhosis who may develop SI, we constructed a machine learning prediction model based on proteomic data. Partial least squares discriminant analysis (PLS-DA) was first applied to reduce dimensionality and classify protein expression profiles. The analysis revealed a clear separation between the SI and non-SI groups at the proteomic level, indicating strong discriminatory power of protein features (Figure 4a).

Among the five machine learning models, the LR algorithm demonstrated the best overall performance and was selected as the core classifier for model construction (Figure 4b–c). The mRMR algorithm was applied to select the top 20 candidate proteins most relevant to SI (Supplementary Data 14). These proteins were sequentially incorporated into the LR model, and five-fold cross-validation was repeated 10 times to calculate average AUROC values across different combinations. The model achieved the highest AUROC when 10 proteins were included (Figure 4d, Supplementary Data 15). As a result, a final panel of 10 proteins was selected as model input features: CALM1, LYZ, TMSB4X, GC, CRP, SERPIND1, DPT, A2M, PF4V1, and SNCA. Among them, CRP and LYZ were upregulated in the SI group, while the remaining eight proteins were downregulated (Figure 4e). All 10 proteins showed significant differences in expression between the SI and non-SI groups, indicating their strong discriminatory ability (Figure 4f).

To enhance the clinical applicability of the model, conventional laboratory parameters were integrated with proteomic biomarkers. Following normalization of protein expression data, univariate logistic regression analyses were conducted, incorporating both proteomic and clinical variables. Significant associations with secondary infection were identified for the following factors: ascites (OR: 2.73, 95% CI: 1.07–7.01, P = 0.036), ALT (OR: 1.01, 95% CI: 1.01–1.01, P = 0.024), AST (OR: 1.01, 95% CI: 1.01–1.01, P = 0.022), TBIL (OR: 1.01, 95% CI: 1.01–1.01, P < 0.001), INR (OR: 2.74, 95% CI: 1.50–4.99, P = 0.001), WBC (OR: 1.19, 95% CI: 1.06–1.34, P = 0.003), NE% (OR: 1.04, 95% CI: 1.01–1.08, P = 0.031), and the 10 candidate proteins (Table 2). Subsequently, variables with P < 0.05 were included in a multivariate logistic regression model. This analysis identified six independent predictors: four protein biomarkers—CALM1 (OR: 0.01, 95% CI: 0.00–0.14, P < 0.001), LYZ (OR: 17.26, 95% CI: 2.13–140.02, P = 0.008), SERPIND1 (OR: 0.06, 95% CI: 0.00–0.14, P = 0.001), DPT (OR: 0.36, 95% CI: 0.02–0.61, P = 0.012)—and two clinical indicators: AST (OR: 1.01, 95% CI: 1.01–1.01, P = 0.030) and Tbil (OR: 1.01, 95% CI: 1.01–1.01, P = 0.006)(Table 2).

Construction of ML Models for Predicting SI
Based on the results of prior analyses, two predictive models for SI were constructed. Model 1 combined two clinical indicators (AST and Tbil) with four protein biomarkers (CALM1, LYZ, SERPIND1, and DPT), whereas Model 2 incorporated the previously identified panel of 10 candidate proteins. To evaluate predictive performance, both models were compared with conventional inflammatory markers (CRP, WBC, NE%) by plotting ROC curves (Figure 5a). The area under the ROC curve (AUROC) for Model 1 reached 0.980, significantly outperforming CRP (AUROC: 0.645, P < 0.001), WBC (AUROC: 0.674, P < 0.001), and NE% (AUROC: 0.642, P < 0.001). Although the performance of Model 1 was statistically comparable to that of Model 2 (AUROC: 0.973, P = 0.323), the reduced number of protein inputs in Model 1 suggested greater feasibility for clinical implementation (Supplementary Data 16). Further comparison of performance metrics, including F1 score, balanced accuracy, positive predictive value (PPV), negative predictive value (NPV), and recall, revealed that Model 1 outperformed all other comparator models across these measures (Figure 5b–c; Figure S5a; Supplementary Data).

To assess prognostic potential, all patients were followed for 28 days, and model performance in predicting short-term mortality was evaluated (Figure 5d). The AUROC of Model 1 for 28-day mortality prediction reached 0.883, significantly higher than that of the CLIF-C ACLF score24 (AUROC: 0.545, P < 0.001), a prognostic tool for HBV-related acute-on-chronic liver failure (ACLF). Although no statistically significant differences were found when compared with the MELD score (AUROC: 0.824, P = 0.959) or Model 2 (AUROC: 0.937, P = 0.252), Model 1 maintained excellent predictive performance (Supplementary Data 16). Further comparison of F1 score, PPV, NPV, and recall indicated that Model 1 outperformed both MELD and CLIF-C AD scores and performed comparably to Model 2 (Figure 5d–f; Figure S5b; Supplementary Data 16).

Model 1 correctly identified 32 of 38 patients with SI, yielding a prediction accuracy of 84% and correctly identified 9 of 10 patients who died within 28 days, achieving a mortality prediction accuracy of 90% (Figure 5 g-h). Analysis of feature importance within Model 1 revealed that the four protein biomarkers contributed most substantially to predictive performance, highlighting their central role in identifying high-risk individuals (Figure 5i). The DCA confirmed that both Model 1 and Model 2 provided greater net clinical benefit compared with conventional clinical indicators, with Model 1 showing a slight advantage over Model 2 (Figure S5c). Moreover, the calibration curve demonstrated strong concordance between predicted probabilities and observed outcomes (Figure S5d). These results highlight the substantial clinical value of integrating proteomic features with routine laboratory parameters for the early prediction of SI in HBV-related cirrhosis.

Validation of the ML Model for Predicting SI
To assess the generalizability of Model 1 across independent cohorts, we conducted external validation cohort 1 using a prospective, multicenter cohort comprising 60 patients with HBV-related cirrhosis. Model performance in predicting SI was first evaluated by comparing Model 1 with other models. ROC curves (Figure 6a). Model 1 achieved an AUROC of 0.873, which was significantly higher than that of traditional inflammatory markers, including WBC (AUROC: 0.702, P = 0.027), NE% (AUROC: 0.793, P = 0.035), and CRP (AUROC: 0.694, P = 0.026). Moreover, Model 1 also outperformed Model 2 (AUROC: 0.815, P = 0.042), further validating its accuracy and robustness in predicting SI (Supplementary Data 17). Evaluation of other performance metrics, including F1 score, balanced accuracy, PPV, NPV, and recall, also demonstrated that Model 1 outperformed all comparator models (Figure 6b–c; Figure S6a; Supplementary Data 17).

In addition, the ability of Model 1 to predict 28-day mortality was assessed in the validation cohort. ROC curve analysis (Figure 6d) showed that the AUROC of Model 1 reached 0.957, significantly higher than that of the CLIF-C AD score (AUROC: 0.651, P = 0.018) and MELD score (AUROC: 0.864, P = 0.036). While no statistically significant difference was observed between Model 1 and Model 2 (AUROC: 0.874, P = 0.154), Model 1 exhibited superior accuracy and sensitivity (Supplementary Data 17). Further comparisons of F1 score, balanced accuracy, PPV, NPV, and recall also indicated that Model 1 outperformed both the CLIF-C AD and MELD scores and showed comparable performance to Model 2 (Figure 6d–f; Figure S6b; Supplementary Data 17). The confusion matrices of Model 1 for predicting SI and 28-day mortality in the validation cohort 1 further demonstrated the model’s discriminatory performance (Figure 6g-h). In this cohort, targeted proteomic analysis confirmed that all four proteins included in Model 1 (LYZ, CALM1, SERPIND1, and DPT) were significantly differentially expressed between patients with and without SI (Figure 6i). Specifically, LYZ levels were elevated, while CALM1, SERPIND1, and DPT levels were reduced in the SI group, consistent with findings from the discovery cohort. Overall, these findings validated the predictive performance and clinical utility of Model 1 in real-world patients with advanced HBV-related liver failure, supporting its potential application for early identification of SI and prediction of short-term mortality risk in clinical practice.

To validate the clinical applicability of these four biomarkers, we utilized an additional validation cohort 2 consisting of 30 SI patients and 30 non-SI patients. Plasma levels of the four biomarkers were measured using ELISA. Consistent with the findings from the discovery and validation cohorts, the level of LYZ was significantly higher in SI patients than in non-SI patients (P < 0.05; Figure S7a), while CALM1, SERPIND1, and DPT levels were significantly lower in SI patients compared with non-SI patients (P < 0.05; Figures S7b–d). Furthermore, an LR model constructed using these four biomarkers demonstrated robust discrimination between SI and non-SI patients, with an AUROC of 0.883 (0.798–0.968) based on ELISA-measured levels (Figure S7e). These results indicate that the four biomarkers can effectively distinguish SI from non-SI patients, and that ELISA provides a convenient approach for clinical application.

In conclusion, targeted proteomic validation highlighted the critical interplay between inflammation and the coagulation system. In patients with HBV-related liver failure, coagulation dysfunction may amplify inflammatory responses by promoting the recruitment of inflammatory mediators, while persistent inflammation may aggravate coagulation abnormalities, forming a vicious cycle that drives the onset and progression of SI. Additionally, the use of ELISA in validation cohort 2 further confirmed the potential of these four biomarkers for predicting the occurrence of SI, demonstrating their feasibility for clinical application.

Methods
Patients
This study was approved by the Ethics Committees of Beijing Ditan Hospital, Capital Medical University (DTECKY2023-045-01), Huashan Hospital, Fudan University (KY2020-679), and the First Affiliated Hospital, Zhejiang University (IIT0230519B-R1). A prospective, multicenter cohort study was conducted. Patients in the discovery cohort were enrolled from Beijing Ditan Hospital (Capital Medical University), the First Affiliated Hospital of Zhejiang University School of Medicine, and Huashan Hospital (Fudan University) between June 2024 and February 2025. The validation cohort 1 was recruited from February to April 2025. The validation cohort 2 was recruited from May to June 2025. In the discovery cohort, validation cohort, and validation cohort 2, plasma samples were collected within 48 hours of admission and analyzed using untargeted proteomics to identify candidate biomarkers associated with SI. Inclusion criteria were as follows: (1) age between 18 and 75 years; (2) diagnosis of liver failure based on the European Association for the Study of the Liver (EASL) guidelines32; (3) absence of clinical or laboratory evidence of bacterial infection at admission; and (4) provision of written informed consent. Exclusion criteria included: (1) age <18 or >75 years; (2) coexistence of other liver diseases (e.g., hepatitis C, alcoholic, drug-induced, autoimmune, or hereditary/metabolic liver diseases); (3) presence of hepatocellular carcinoma (HCC) or other malignancies; (4) co-infection with human immunodeficiency virus (HIV); and (5) pregnancy or lactation. The written informed consent was obtained from all participants prior to enrollment in the study. This study had been registered in the Chinese National Medical Research Registration Information System (MR-11-24-043987).

Diagnosis of BI was based on established criteria33：(1) Spontaneous bacterial peritonitis: ascitic fluid polymorphonuclear leukocyte count >250/µL, regardless of culture results; (2) Pneumonia: new pulmonary infiltrates on imaging plus either (a) at least one respiratory symptom (e.g., cough, sputum production, dyspnea, chest pain), or (b) at least one physical sign (e.g., rales, crackles) or systemic infection indicator (e.g., body temperature >38°C or <36°C, chills, WBC >10,000/mm³ or <4,000/mm³), after excluding antibiotic interference; (3) Enteric infection: diarrhea or dysentery with stool cultures positive for Salmonella, Shigella, Campylobacter, or pathogenic Escherichia coli; (4) Skin infection: fever with signs of cellulitis; (5) Urinary tract infection: symptomatic presentation with pyuria (urine WBC >15/high-power field) and a positive Gram stain or urine culture; (6) Bloodstream infection: positive blood culture in the absence of an identifiable infection source elsewhere.

Proteomics
Plasma sample preparation
Plasma samples from patients at the three participating centers were collected following standardized SOPs to ensure the accuracy of proteomic analyses. Blood samples were collected within 48 hours of patient admission using EDTA anticoagulant tubes. Within 4 hours of collection, samples were centrifuged at 750 xg for 10 minutes in the laboratory, and the resulting plasma was stored at –80 °C.

For each sample, 5 μL of plasma was denatured in 50 μL of buffer containing 8 M urea and 100 mM ammonium bicarbonate. Proteins were reduced with 10 mM DTT at 37 °C for 30 minutes and subsequently alkylated with 50 mM IAA in the dark at room temperature. The solution was then diluted with 50 mM ammonium bicarbonate, followed by digestion with trypsin (Promega) at a protein-to-enzyme ratio of 50:1 overnight at 37 °C. Peptides were desalted using C18 StageTips and prepared for LC-MS analysis. Peptide concentrations were measured at 280 nm using a Nanodrop One spectrophotometer.

LC-MS/MS Analysis for DIA
LC–MS/MS analysis was performed using an Orbitrap Astral mass spectrometer coupled with a Vanquish Neo UHPLC system (Thermo Fisher Scientific). Peptides from each sample were loaded onto a 50 cm Low-Load µPAC™ Neo HPLC column (Thermo Scientific) at a flow rate of 2.2 μL/min. The reversed-phase HPLC mobile phases consisted of 0.1% formic acid in water (buffer A) and 0.1% formic acid in 80% acetonitrile (buffer B). Peptides were separated over 8 minutes using a linear gradient of buffer B at 1.25 μL/min. The gradient was programmed as follows: 0–0.1 min, 4% to 6% B; 0.1–1.1 min, 6% to 12% B; 1.1–4.3 min, 12% to 25% B; 4.3–6.1 min, 25% to 45% B; 6.1–6.5 min, 45% to 99% B; 6.5–8 min, held at 99% B. The eluted peptides were analyzed in data-independent acquisition (DIA) mode. The survey scan was acquired from 380 to 980 m/z at a resolution of 240,000, with an AGC target of 500% and an injection time of 5 ms. DIA MS/MS scans were acquired from 150 to 2000 m/z using a 2 m/z isolation window, an AGC target of 500%, and an injection time of 3 ms. Normalized collision energy was set to 25, and the total cycle time was 0.6 seconds. Full MS scans were recorded in profile mode, while DIA MS/MS spectra were recorded in centroid mode.

Plasma samples (50 μL) were processed using the DBE kit following the manufacturer's instructions (Shanghai Bioprofile, China). The magnetic bead–based protein enrichment procedure followed the methodology described by Omid et al34. First, 20 μL of magnetic beads were washed three times with 200 μL of Washing-Buffer in tubes. Each wash involved mixing at 317 xg for 30 seconds, followed by magnetic separation using a magnetic rack for 1 min and removal of the supernatant. This washing step was repeated for a total of three washes. Then, 50 μL of plasma and 50 μL of Incubation-Buffer were both added to the beads and mixed at 317 xg for 10 min at room temperature. Tubes were placed on the magnetic rack and the supernatant was discarded. Samples were washed with 150 μL of Washing-Buffer for a total of four washes. For lysis, 40 μL of Elution Buffer was added to the beads and mixed at 317 xg for 20 min at room temperature. For protein digestion, 1µL 400mM TCEP and 2µL 800mM CAA were added into the eluted protein buffer, vortexed, and 95°C for 3min (Reduction and Alkylation). Then 160 µL of 50 mM NH₄HCO₃ was added, and then proteins were digested with 0.5 µg Lys-C/Trypsin (1:2) at 37°C overnight. The digestion reaction was stopped by adding 60 μL of STOP solution and by mixing the tubes at 317 xg for 1 min at RT. The whole mixture was transferred to the C18 stage tip to clean the peptides. Samples were washed sequentially with 100 μL of methanol and 100 μL of 0.1% TFA, and peptides were eluted with 100 μL of 50% Acetonitrile ( 0.1% TFA) twice in the same collection tube. Samples were dried down in a SpeedVac (Eppendorf), and peptides were redissolved in 10 μL of buffer with 2 % ACN, 0.1 %FA. Peptide concentration was determined using a Nanodrop and then analyzed by LC-MS/MS.

The DIA MS data were analyzed using DIA-NN 1.8.1. MS data were searched against the UniProtKB reviewed (Swiss-Prot) database (Homo sapiens (species), 20422 total entries, downloaded 12/2024. The trypsin was selected as the digestion enzyme. The maximum of 1 missed cleavage sites and a mass tolerance of 10 ppm for precursor ions and 10 ppm for fragment ions were defined for database search. Carbamidomethylation of cysteines was defined as a fixed modification, while acetylation of protein N-terminal, oxidation of Methionine was set as variable modifications for database searching. The maximum number of variable modifications was 1. The peptide length range was set from 7-30. The charge of the peptide was from 1 to 4. The fragment ion m/z range was from 150-2000. The database search results were filtered and exported with <1% false discovery rate (FDR) at the peptide-spectrum-matched level and the protein level, respectively. Quantification was based on MS1 signal intensity. A schematic workflow is presented in Figure S1. The figures were generated using BioRender.

Targeted Proteomics (PRM)
PRM analyses were performed on a Thermo Scientific Q-Exactive HF-X mass spectrometer using MaxQuant 2.0.1.0. Proteins were digested with trypsin allowing up to two missed cleavages. Precursor mass tolerances were set to 20 ppm for the first search and 4.5 ppm for the main search; MS/MS tolerance was 20 ppm. Carbamidomethylation of cysteine was set as a fixed modification, and oxidation (M) and N-terminal acetylation were variable modifications. Database searching used the Homo sapiens SwissProt database (9606, 20,418 entries, downloaded 2025-04-14) with a target-reverse pattern. PSM, protein, and site-level FDR were all controlled at 1%.

Quality Control
Peptide selection for protein analysis followed the criteria outlined below. Peptides were required to be 6–24 amino acids in length, contain no missed K/R cleavages (missed cleavages = 0), and be protein-specific (unique peptides). Peptides containing modification-prone residues such as methionine or cysteine were excluded. In addition, peptide scores were required to meet the following thresholds: Mascot ion score >20 and MaxQuant score >40. After filtering peptides based on these criteria, proteins were further enriched according to the number of remaining unique peptides.For proteins, those with >50% missing values across samples were removed, whereas proteins with <50% missing values were retained and subjected to imputation. Imputation was performed only when a protein was detected in at least two treatment groups. If this condition was met, missing values for that protein were imputed by replacing them with the minimum observed intensity within each group35. Using this workflow, a total of 4,901 proteins were ultimately retained for downstream analyses.

Bioinformatics analysis
Bioinformatic analysis was carried out with Microsoft Excel and R statistical computing software. Sequence annotation was performed based on information retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. KEGG pathway enrichment analysis was conducted using Fisher’s exact test, followed by false discovery rate (FDR) correction to account for multiple testing. Pathways with a Fisher’s exact test p-value < 0.01 were considered nominally significant. Protein–protein interaction (PPI) networks were constructed using the STRING database and visualized with Cytoscape software.

Machine learning models
Next, we systematically evaluated the performance of five mainstream machine learning algorithms: Logistic Regression (LR), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Evaluation metrics included AUROC, accuracy, balanced accuracy, precision, recall, and F1 score. By comparing these metrics, we will select the most suitable machine learning algorithm for constructing the predictive model.

ELISA analysis
The ELISA kits used for the validation experiments were purchased from Abcam, including LYZ (ab267798), DPT (ab272206), and SERPIND1 (ab277402). The CALM1 kit was purchased from Cloud Clone (catalog no. SEG095Hu). ELISA readings were performed using a BioTek 800 TS microplate reader. Samples, reagents, and standards were prepared according to the manufacturer’s instructions. Specific antibodies against the target proteins were coated onto the microplate wells. Standards and samples were then added to the wells, allowing the immobilized antibodies to bind the target proteins. After washing, a horseradish peroxidase (HRP) conjugate mixture was added, and the optical density (OD) was measured at 450 nm using the microplate reader.

Statistical Analysis
All statistical analyses were performed using R software (version 4.4.0). Model performance was assessed using receiver operating characteristic (ROC) curves and area under the ROC curve (AUROC). Comparisons between models were conducted using the DeLong test. Clinical utility and diagnostic accuracy were further evaluated using decision curve analysis (DCA) and calibration plots. A two-sided P value < 0.05 was considered statistically significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study by Feixiang Xiong et al. aimed to develop and validate a multi-parameter predictive model based on plasma proteomic profiling to assess the risk of secondary infection (SI) in patients with HBV-related liver cirrhosis, using machine learning approaches. To this end, the authors conducted a plasma proteomic analysis of blood plasma samples from 174 patients, subdivided into a retrospective discovery cohort of 114 patients and a prospective validation cohort of 60 patients. The authors then developed a predictive model consisting of the expression of four blood protein markers identified by proteomics, combined with total bilirubin and aspartate aminotransferase (AST). This model accurately identified patients with HBV-related liver failure at risk of SI.\nThis study is potentially highly relevant and significant to the field, as the prognostic performance of this model seems to outperform classical scoring systems such as CLIF-C, ACLF and MELD in this setting. However, the cost-benefit of this new model is unclear, as this 'outperformance' is narrow and seemingly relies on proteomic equipment and expertise. The suggested four-protein panel should therefore be translated into a multiplex immunoassay capable of replicating the predictive performance (see specific comments).\nSpecific comments\n(Major)\n1) The 4-protein panel suggested by the authors must be validated using a immunoassay (ideally as multiplex), which can be more easily translated into the clinics. Can these results be reproduced in the validation cohort with this immuno assay combined with the predictive model?\n2) The authors must clearly indicate how the plasma samples have been taken in the three participating centres and patient cohorts. Which anti-coagulant have been used to generate plasma? Variable blood processing procedures may contribute to plasma proteomic variability (PMID 33468058) and must therefore be controlled and discussed if this was not standardized in this study.\n3) Line 113 mentions “early identification” but how much time passed between sample analysis at baseline and SI? This must be indicated.\n4) Material and Methods (lines 164-168): The manuscript suggests that liver tissue have been analysed but this seems not be the case. This part must be clarified or deleted.\n5) The supplementary tables require proof reading for recurrent typos, i.e., “plamsa” or “Aabundant”, and “feathers”. Moreover, the S table titles must be better distinguishable stating the measured cohorts and explaining the used abbreviations in the table itself as no specific legend has been provided. They should be understandable without reading the main text. Moreover, FDR or adj.P must be provided in tables S1 and S8.\n6) Line 254-254: Mislabelling of Figure 2A-B instead Figure 1A-B; Figure 2C instead Figure 1C.\n7) Generally, many figure panels (main and supplements) are hardly readable as labels, legends, and/or font sizes are too small. This concerns Figs. 2EH, 3B, 3E-H, 4B-F, 5 A, 5D, 5G-I, 6A, 6D, 6G-I. Also, the used color codes for the ROC curves in Figs. 5-6 are hardly distinguishable and must be improved.\n8) Lines 350: What was the rational for selecting the 10 proteins? Supplementary table S13 suggests a ranking, however, if sorted according to adj.P, this ranking is not supported anymore. Why was TF, SerpinA5, and C84 not included although possessing a better adj.P? This must be clearly described.\n9) Figure 3: The text (line 321) mentions PLG by citing Fig. 3E-F. But PLG is not depicted in this figure. Similar in line 334, where S100A9 is not depicted in the scatter blots of Fig. 3E-H.\n(Minor)\n1) Figure S1: The authors must state how the images have been generated (Biorender?)\n2) Figure S2A-B: y-axis labels should be provided in the graph.\n3) Typos at line 262 “s”, lines 294 and 368: missing spaces\n4) Line 270: a figure should be cited here after “..networks”.\n5) The figure legends should indicate how the biological processes have been determined in Figs. 2C and 3C. Are these based on GSEA with GO or KEGG gene sets?\n6) The figures should be cited in the order of appearance. Figure 5G was cited before Figs. 5B-F.\n7) Fig. 2B and Fig. 3B should be clustered and represented in a similar way and similar order: up proteins on the top and down proteins on the bottom or vice versa. As mentioned above the legend is too small and hardly readable in Fig. 3B. What is the dimension of the heatmap scale or differentially expressed proteins (adj.P, log2.FC, etc)?\n8) The x-axis “features” should match the legend and the main text “proteins”\n9) The authors introduced discovery cohort and validation cohort in the beginning but later. It is recommended to stringently refer to them. E.g., line 404 may suggest the analysis of yet another cohort.", "reviewer_2": "The study successfully develops and validates a high-performance, proteomics-based machine learning model (Model 1) to predict secondary infections (SI) in patients with HBV-related liver failure. The model integrates four plasma proteins with clinical indicators and demonstrates superior predictive accuracy compared to conventional inflammatory markers and established prognostic scores.\nStrengths:\nA prognostic tool for predicting infection before clinical signs appear is highly valuable in clinical settings.\nThe authors have conducted a multicohort study, which shows the robustness of their pipeline.\nThe authors have conducted appropriate feature selection and benchmarked multiple machine learning methods.\nThe demonstrated performance clearly outperforms existing metrics in both infection prediction and 28-day mortality prediction.\nLimitations/comments:\nThe model relied on measuring specific plasma proteins, obtained through profiling. However, the feasibility of such experiments is not discussed thoroughly, and therefore, the resulting adaptability of this model to clinical settings.\nAs noted by the authors, there’s no internationally recognised tool for SI risk prediction, and existing approaches are based on standard markers. Therefore, the lack of established benchmarks makes it hard to contextualize their model’s performance increase, despite high performance.\nHow many samples/individuals are sufficient to conduct such studies? Also, based on Figure 5I, the four protein biomarkers hold sufficient predictive power. Therefore, how many features are sufficient to develop a predictive model?", "reviewer_3": "This study investigates plasma proteomic changes associated with secondary infection in HBV-related liver failure, identifying biological processes such as coagulation, platelet activation, inflammation, and metabolism, and developing predictive models from 114 patients validated in 60 patients. While the study addresses an important clinical question and presents potentially useful insights and predictive models, there are major concerns regarding the proteomics methodology, data quality, interpretation and consistency of reporting.\nThe Methods section is insufficiently described, particularly with regard to the proteomics workflow and statistical analyses. The quality of the proteomics data is concerning for reasons detailed below. Additional issues include numerous mistakes throughout the manuscript—such as errors in titles, inconsistencies between text and figures, and inaccurate representations of the workflow. Some findings or interpretations are not supported by the data.\nOverall, despite the development and validation of predictive models, the above concerns substantially undermine confidence in the quality of the data, the analyses, and the manuscript’s conclusions. Major revisions are required before the study can be considered reliable.\nMajor points:\n1. The proteomics methodology requires substantial improvement and clarification. For both untargeted and targeted analyses, please state the software used for protein identification and quantification, including protein sequence database version, software version, and parameter settings. Indicate whether quantification is based on MS1 or MS2 signals. In addition, the targeted proteomics workflow lacks critical details: instrument type, software, quantification strategy, peptide selection criteria, and analysis parameters should be provided.\n2. The LC–MS/MS section is confusing. For example: “Total cycle time was 0.6 seconds” appears to refer to MS1; please clarify. The DIA precursor m/z range is not specified. The FAIMS setting was not mentioned. Methods mention a 3 ms injection time, while raw files suggest 3.5 ms.\n3. The Methods described two sets of proteomics sample preparation protocols. Were the plasma samples processed differently? If yes, how does this affect the results? Did the authors investigate potential batch effect?\n4. The manuscript reports that predictive models trained on DIA data generalized well to targeted proteomics (likely PRM); please describe any normalization procedures applied between datasets.\n5. The reported 8-minute LC gradient yielding ~5,000 plasma protein groups is unexpected. Were depletion or enrichment strategies applied, or was neat plasma analyzed? Please clarify and also include details on plasma sample collection, e.g., anti-coagulants and centrifugation conditions.\n6. With such short gradients, sufficient data points across chromatographic peaks are essential for accurate quantification. Please report median peak widths, number of data points per peak, and injection amount.\n7. The Methods should include a section on data quality: number of quantified proteins overall and per sample, proportion of missing values, and how missing values were handled. From scatterplots, it appears missing values were replaced with zero, which is problematic since missingness in DIA usually reflects protein levels below detection limits and can distort effect sizes. Please provide a rationale for this imputation strategy and discuss the potential consequences.\n8. Correlation with clinical markers is modest: e.g., CRP measured by MS correlated only moderately with clinical CRP (Spearman R=0.59), while CALCA correlated most strongly with clinical CRP. This raises concerns about data quality; please discuss potential causes.\n9. The authors interpret increased coagulation activity in secondary infection, but the proteomics data appear to show reduced coagulation-related proteins alongside increased inflammatory proteins. Please reconcile these discrepancies with the cited literature.\n10. Table 1 should specify units and data format (mean ± s.d., or median with CI). Consider presenting baseline characteristics stratified by secondary infection status in both discovery and validation cohorts to aid interpretation of predictive features.\n11. In targeted proteomics, 31 DEPs were “successfully detected.” Please indicate how many proteins were originally selected for validation and explain why the remainder could not be detected or validated.\n12. The untargeted and targeted proteomics sections (Figures 2 and 3) appear highly similar. By design, targeted proteomics should serve as validation of a subset of proteins identified in the discovery cohort, and therefore the focus should be on validation outcomes rather than presenting the results in an exploratory, discovery-style manner. In addition, parts of the text are overly descriptive (e.g., listing top correlating proteins with clinical variables) without providing sufficient interpretation. A stronger emphasis on the implications of these findings would improve clarity and strengthen the overall narrative.\n13. Feature selection (using univariate and multivariate logistic regression) and final model construction were both performed on the same discovery cohort. This risks information leakage and may inflate reported performance in the discovery cohort. To properly demonstrate superiority over clinical variables within the discovery cohort, the authors should designate a left-out test set or adopt an alternative strategy (e.g., cross-validation selecting features within each fold) to avoid bias.\nMinor points:\n1. Please report the statistical tests and thresholds used to identify differentially expressed proteins.\n2. Line 280 the mentioned proteins do not match those presented in Figure 2E.\n3. Figure 3H: the claimed correlation seems to be driven by outliers. Please re-examine this analysis and consider presenting an additional visualization (e.g., zoomed-in view excluding extreme values) to better illustrate the relationship among the majority of samples.\n4. Functional enrichment analysis (Lines 266–272) is unclear: how was this performed on top of the protein interaction analysis? Please clarify. Please include figure legend for Supplementary Figure 3D.\n5. The argument that targeted proteomics “emphasizes the pivotal role of inflammation–coagulation imbalance” appears circular, since the targeted panel was pre-selected based on those processes.\n6. Scatter plots (Line 323) are simply alternative visualizations of the same data and should not be described as independent confirmation.\n7. Figure 2E–H: please include y-axis scales.\n8. Supplementary Figure 1: title should read “proteomics,” not “metabolomics.” The workflow diagram contains errors: Orbitrap Astral was used, yet it seems a Tribrid instrument icon is shown; there are duplicated MS instruments and digestion steps.\n9. Supplementary Figure 5: duplicated NPV plots in panel B.\n10. Figure 4A: please state what separates individuals along PLS Component 1.\n11. Supplementary Tables 4–7: “nr. Observations” appears to denote sample size but is inconsistent (e.g., 974 = number of DEPs). Please correct. Also annotate how fold changes were calculated.\n12. Supplementary Table 7: header inaccurate.\n13. Supplementary Table 13: likely typo (“MRMR” should replace “MRME”).\n14. Please add the abbreviation for GOV."}, "author_response": "Response to Reviewers\nReviewer #1 (Remarks to the Author):\nThe study by Feixiang Xiong et al. aimed to develop and validate a multi-parameter\npredictive model based on plasma proteomic profiling to assess the risk of secondary\ninfection (SI) in patients with HBV-related liver cirrhosis, using machine learning\napproaches. To this end, the authors conducted a plasma proteomic analysis of blood\nplasma samples from 174 patients, subdivided into a retrospective discovery cohort of\n114 patients and a prospective validation cohort of 60 patients. The authors then\ndeveloped a predictive model consisting of the expression of four blood protein\nmarkers identified by proteomics, combined with total bilirubin and aspartate\naminotransferase (AST). This model accurately identified patients with HBV-related\nliver failure at risk of SI.\nThis study is potentially highly relevant and significant to the field, as the prognostic\nperformance of this model seems to outperform classical scoring systems such as\nCLIF-C, ACLF and MELD in this setting. However, the cost-benefit of this new\nmodel is unclear, as this 'outperformance' is narrow and seemingly relies on proteomic\nequipment and expertise. The suggested four-protein panel should therefore be\ntranslated into a multiplex immunoassay capable of replicating the predictive\nperformance (see specific comments).\nSpecific comments\n(Major)\n1) The 4-protein panel suggested by the authors must be validated using a\nimmunoassay (ideally as multiplex), which can be more easily translated into the\nclinics. Can these results be reproduced in the validation cohort with this immuno\nassay combined with the predictive model?\nResponse: Thank you for highlighting this important aspect. We further included an\nadditional validation cohort consisting of 30 patients with secondary infection (SI) and\n30 without infection. The plasma levels of LYZ, CALM1, SERPIND1, and DPT were\nmeasured using ELISA. The results showed that LYZ was elevated in SI patients,\nwhereas the other three biomarkers were decreased, consistent with the findings from\nthe discovery and validation cohort 1. Furthermore, a logistic regression (LR) model\nwas constructed based on the ELISA-measured levels of these four biomarkers to\npredict SI, achieving an AUROC of 0.883 (0.798–0.968), indicating good predictive\nperformance for SI patients. These findings demonstrate that our results can be\nreproduced by ELISA, providing a foundation for clinical translation (lines 455-464).\n2) The authors must clearly indicate how the plasma samples have been taken in the\nthree participating centres and patient cohorts. Which anti-coagulant have been used to\ngenerate plasma? Variable blood processing procedures may contribute to plasma\nproteomic variability (PMID 33468058) and must therefore be controlled and discussed\nif this was not standardized in this study.\nResponses: Thank you for your constructive advice. Plasma samples from patients at\nthe three participating centers were collected following standardized SOPs to ensure\nthe accuracy of proteomic analyses. Blood samples were collected within 48 hours of\npatient admission using EDTA anticoagulant tubes. Within 4 hours of collection,\nsamples were centrifuged at 2000 rpm for 10 minutes in the laboratory, and the resulting\nplasma was stored at –80 °C. Once a sufficient number of samples had been collected,\nproteomic analysis was performed in batches to minimize variability. By adhering to a\nconsistent plasma collection protocol, we reduced potential errors and improved the\nreliability of the proteomic results.\n3) Line 113 mentions “early identification” but how much time passed between sample\nanalysis at baseline and SI? This must be indicated.\nResponse: Thank you for the valuable input. We have added a graphical representation\nof the time from baseline sampling to the occurrence of SI for each SI patient across\nthe three cohorts in Supplementary Figure 2C. The results showed that the median\ntime from baseline to SI onset was 6.5 (5–9) days in the discovery cohort, 7 (5–10.25)\ndays in validation cohort 1, and 6.5 (5–9.25) days in validation cohort 2, indicating that\nthe median interval from baseline to SI occurrence was approximately one week. This\ninformation has been added to the revised manuscript (lines 259-261)\n4) Material and Methods (lines 164-168): The manuscript suggests that liver tissue have\nbeen analysed but this seems not be the case. This part must be clarified or deleted.\nResponse: Thank you for pointing that out. This section was mistakenly included due\nto an editing error and has now been removed from the revised manuscript.\n5) The supplementary tables require proof reading for recurrent typos, i.e., “plamsa” or\n“Aabundant”, and “feathers”. Moreover, the S table titles must be better distinguishable\nstating the measured cohorts and explaining the used abbreviations in the table itself as\nno specific legend has been provided. They should be understandable without reading\nthe main text. Moreover, FDR or adj.P must be provided in tables S1 and S8.\nResponse: Thank you for your suggestive comments. We have corrected the inaccurate\ndescriptions in the supplementary tables and provided legends for all abbreviations. In\naddition, P values have been added to Tables S1 and S8.\n6) Line 254-254: Mislabelling of Figure 2A-B instead Figure 1A-B; Figure 2C instead\nFigure 1C.\nResponse: Thank you for pointing that out. We have revised the Figure citation.\n7) Generally, many figure panels (main and supplements) are hardly readable as\nlabels, legends, and/or font sizes are too small. This concerns Figs. 2EH, 3B, 3E-H, 4B-F, 5 A,\n5D, 5G-I, 6A, 6D, 6G-I. Also, the used color codes for the ROC curves in Figs. 5-6 are\nhardly distinguishable and must be improved.\nResponse: Thanks for the advice. We have increased the font size of the figure legends\nand adjusted the colors of the ROC curves to make the figures easier to distinguish and\nread.\n8) Lines 350: What was the rational for selecting the 10 proteins? Supplementary table\nS13 suggests a ranking, however, if sorted according to adj.P, this ranking is not\nsupported anymore. Why was TF, SerpinA5, and C84 not included although possessing\na better adj.P? This must be clearly described.\nResponse: This is an excellent point. We first used the mRMR algorithm to identify the\ntop 20 proteins most strongly associated with SI, as shown in Supplementary Table 13.\nTo further determine a smaller protein panel with optimal predictive performance, we\nsequentially added these proteins and calculated the AUROC values for different\ncombinations in predicting SI occurrence. As shown in Supplementary Table 14, the\ncombination of 10 proteins achieved the highest AUROC value for predicting SI.\nTherefore, we selected these 10 proteins as the biomarker panel for SI prediction.\n9) Figure 3: The text (line 321) mentions PLG by citing Fig. 3E-F. But PLG is not\ndepicted in this figure. Similar in line 334, where S100A9 is not depicted in the scatter\nblots of Fig. 3E-H.\nResponse: Thank you for the valuable opinion. Our intention was to show that PLG, as\na coagulation-related protein, exhibited a strong correlation with clinical inflammation\nmarkers, while S100A9, as an inflammation-related protein, showed a strong\ncorrelation with clinical coagulation markers. However, in the scatter plots, we only\ndisplayed the top three protein markers that were most closely correlated with the\ncorresponding clinical indicators. Although PLG and S100A9 demonstrated strong\ncorrelations, they were not among the top three and therefore were not included in the\nscatter plots. Nevertheless, we highlighted PLG and S100A9 to illustrate that, in\npatients with HBV-related liver failure, the interplay between inflammatory activation\nand coagulation dysfunction contributes to the development of bacterial infection.\n(Minor)\n1) Figure S1: The authors must state how the images have been generated (Biorender?)\nResponse: Thank you for your valuable recommendation. We have added an\nexplanation describing how these images were generated (line 188)\n2) Figure S2A-B: y-axis labels should be provided in the graph.\nResponse: Thank you for your suggestive comments. We have added legends to the Y-\naxis in the figures.\n3) Typos at line 262 “s”, lines 294 and 368: missing spaces\nResponse: Thank you for your suggestive comments. We have corrected this error.\n4) Line 270: a figure should be cited here after “..networks”.\nResponse: Thank you for your comments. We have cited Figure S3D in the manuscript.\n5) The figure legends should indicate how the biological processes have been\ndetermined in Figs. 2C and 3C. Are these based on GSEA with GO or KEGG gene sets?\nResponse: This is a critical consideration. These represent KEGG pathway enrichment\nanalyses based on the differentially expressed proteins, and we have described this part\nin the figure legend.\n6) The figures should be cited in the order of appearance. Figure 5G was cited before\nFigs. 5B-F.\nResponse: Thank you for your rigorous advice. We have rearranged the manuscript so\nthat the figures are cited in alphabetical order.\n7) Fig. 2B and Fig. 3B should be clustered and represented in a similar way and similar\norder: up proteins on the top and down proteins on the bottom or vice versa. As\nmentioned above the legend is too small and hardly readable in Fig. 3B. What is the\ndimension of the heatmap scale or differentially expressed proteins (adj.P, log2.FC, etc)?\nResponse: Thank you for your careful reading of our manuscript. We have adjusted the\nprotein heatmaps in Figure 2B and Figure 3B to a top-down arrangement for\nconsistency between the two figures. In addition, the heatmaps were generated based\non enrichment using log2 fold change (log2.FC) values.\n8) The x-axis “features” should match the legend and the main text “proteins”\nResponse: Thank you for your insightful suggestion. We have changed the term\n\"features\" to \"proteins\" in the figures to maintain consistency with the main text.\n9) The authors introduced discovery cohort and validation cohort in the beginning but\nlater. It is recommended to stringently refer to them. E.g., line 404 may suggest the\nanalysis of yet another cohort.\nResponse: Thank you for your suggestion. We have revised the manuscript to\nconsistently describe all cohorts as discovery or validation cohorts throughout the text.\nReviewer #2 (Remarks to the Author):\nThe study successfully develops and validates a high-performance, proteomics-based\nmachine learning model (Model 1) to predict secondary infections (SI) in patients with\nHBV-related liver failure. The model integrates four plasma proteins with clinical\nindicators and demonstrates superior predictive accuracy compared to conventional\ninflammatory markers and established prognostic scores.\nStrengths:\nA prognostic tool for predicting infection before clinical signs appear is highly valuable\nin clinical settings.\nThe authors have conducted a multicohort study, which shows the robustness of their\npipeline.\nThe authors have conducted appropriate feature selection and benchmarked multiple\nmachine learning methods.\nThe demonstrated performance clearly outperforms existing metrics in both infection\nprediction and 28-day mortality prediction.\nResponse: Many thanks for your kind words.\nLimitations/comments:\nThe model relied on measuring specific plasma proteins, obtained through profiling.\nHowever, the feasibility of such experiments is not discussed thoroughly, and therefore,\nthe resulting adaptability of this model to clinical settings.\nResponse: Thank you for your detailed and constructive feedback. We further included\nan additional validation cohort consisting of 30 patients with secondary infection (SI)\nand 30 without infection. The plasma levels of LYZ, CALM1, SERPIND1, and DPT\nwere measured using ELISA. The results showed that LYZ was elevated in SI patients,\nwhereas the other three biomarkers were decreased, consistent with the findings from\nthe discovery and validation cohort 1. Furthermore, a logistic regression (LR) model\nwas constructed based on the ELISA-measured levels of these four biomarkers to\npredict SI, achieving an AUROC of 0.883 (0.798–0.968), indicating good predictive\nperformance for SI patients. These findings demonstrate that our results can be\nreproduced by ELISA, providing a foundation for clinical translation (lines 455-464).\nAs noted by the authors, there’s no internationally recognised tool for SI risk prediction,\nand existing approaches are based on standard markers. Therefore, the lack of\nestablished benchmarks makes it hard to contextualize their model’s performance\nincrease, despite high performance.\nResponse: This is a critical consideration. Indeed, there is currently no internationally\nrecognized tool for predicting the risk of secondary infection (SI), and existing\napproaches rely primarily on standard clinical markers. This lack of established\nbenchmarks limits direct comparison. Clinically, bacterial infection is usually assessed\nbased on inflammatory indicators such as WBC and CRP, which reflect an already\nestablished infection. In contrast, our study aims to identify patients at risk before the\nonset of infection, enabling early intervention and potentially reducing disease burden.\nOur model demonstrates high predictive performance with an AUROC of 0.980,\nhighlighting its potential utility. Additionally, ELISA validation was performed,\nfacilitating clinical application and decision-making. This study emphasizes the\npotential of these biomarkers for predicting SI, although broader recognition and\nvalidation of the model will require further investigation. We have added a statement in\nthe revised manuscript to clarify this context and emphasize the novelty and clinical\nrelevance of our findings.\nHow many samples/individuals are sufficient to conduct such studies? Also, based on\nFigure 5I, the four protein biomarkers hold sufficient predictive power. Therefore, how\nmany features are sufficient to develop a predictive model?\nResponse: This is an excellent point and is well taken. In a prospective cohort of over\n1,000 patients from three centers, 38 patients developed secondary infection (SI) in the\ndiscovery cohort, 20 patients in validation cohort 1, and 30 patients in validation cohort\n2. To ensure comparability, propensity score matching (PSM) was performed based on\nage and sex: a 1:2 matching was applied in the discovery and validation cohort 1, and\na 1:1 matching in validation cohort 2. This approach reduces bias and meets the study\nrequirements.\nRegarding biomarker selection, we first used the mRMR algorithm to identify the top\n20 proteins most strongly associated with SI, as shown in Supplementary Table 13. To\ndetermine a smaller protein panel with optimal predictive performance, we sequentially\nadded these proteins and calculated AUROC values for different combinations in\npredicting SI occurrence. As shown in Supplementary Table 14, a combination of 10\nproteins achieved the highest AUROC for SI prediction.\nHowever, the clinical application of a 10-protein panel remains challenging. Therefore,\nwe explored whether routine clinical biochemical indicators could partially replace\nsome proteins in prediction. Through univariate and multivariate logistic regression\nanalyses, we ultimately included the four proteins most strongly associated with SI (the\ntop four in Figure 5I), along with AST and total bilirubin. This selection allows for the\ndevelopment of a biomarker panel more suitable for clinical use. Consequently, the final\npredictive model features a combination of these four proteins plus AST and total\nbilirubin.\nReviewer #3 (Remarks to the Author):\nThis study investigates plasma proteomic changes associated with secondary infection\nin HBV-related liver failure, identifying biological processes such as coagulation,\nplatelet activation, inflammation, and metabolism, and developing predictive models\nfrom 114 patients validated in 60 patients. While the study addresses an important\nclinical question and presents potentially useful insights and predictive models, there\nare major concerns regarding the proteomics methodology, data quality, interpretation\nand consistency of reporting.\nThe Methods section is insufficiently described, particularly with regard to the\nproteomics workflow and statistical analyses. The quality of the proteomics data is\nconcerning for reasons detailed below. Additional issues include numerous mistakes\nthroughout the manuscript—such as errors in titles, inconsistencies between text and\nfigures, and inaccurate representations of the workflow. Some findings or\ninterpretations are not supported by the data.\nOverall, despite the development and validation of predictive models, the above\nconcerns substantially undermine confidence in the quality of the data, the analyses,\nand the manuscript’s conclusions. Major revisions are required before the study can be\nconsidered reliable.\nMajor points:\n1. The proteomics methodology requires substantial improvement and clarification. For\nboth untargeted and targeted analyses, please state the software used for protein\nidentification and quantification, including protein sequence database version, software\nversion, and parameter settings. Indicate whether quantification is based on MS1 or\nMS2 signals. In addition, the targeted proteomics workflow lacks critical details:\ninstrument type, software, quantification strategy, peptide selection criteria, and\nanalysis parameters should be provided.\nResponse: This is an excellent point. The following are the relevant details of our\nproteomics analysis: Untargeted Proteomics (DIA): Plasma samples were analyzed\nusing DIA-NN software against the Homo sapiens SwissProt database (9606, 20,422\nentries, downloaded on 2024-12-04 from https://www.uniprot.org/taxonomy/9606).\nProteins were digested with trypsin allowing a maximum of one missed cleavage.\nCarbamidomethylation of cysteine was set as a fixed modification, and oxidation of\nmethionine and N-terminal acetylation were set as variable modifications. The database\nsearch followed a target-reverse pattern, with peptide-spectrum match (PSM) and\nprotein-level false discovery rates (FDR) controlled at 1%. Quantification was based\non MS1 signal intensity.\nTargeted Proteomics (PRM): PRM analyses were performed on a Thermo Scientific\nQ-Exactive HF-X mass spectrometer using MaxQuant 2.0.1.0. Proteins were digested\nwith trypsin allowing up to two missed cleavages. Precursor mass tolerances were set\nto 20 ppm for the first search and 4.5 ppm for the main search; MS/MS tolerance was\n20 ppm. Carbamidomethylation of cysteine was set as a fixed modification, and\noxidation (M) and N-terminal acetylation were variable modifications. Database\nsearching used the Homo sapiens SwissProt database (9606, 20,418 entries,\ndownloaded 2025-04-14) with a target-reverse pattern. PSM, protein, and site-level\nFDR were all controlled at 1%.\nPeptides selected for PRM analysis met the following criteria: length of 6–24 amino\nacids, no missed K/R cleavages, protein-specific (unique) peptides, and absence of\nmodification-prone residues such as methionine or cysteine. Peptides also passed\nscoring thresholds (Mascot ion score >20, MaxQuant score >40). After peptide-level\nfiltering, proteins were further selected based on the number of remaining unique\npeptides (lines 166-201)\nFurthermore, FAIMS settings are shown in the figure below:\n2. The LC–MS/MS section is confusing. For example: “Total cycle time was 0.6\nseconds” appears to refer to MS1; please clarify. The DIA precursor m/z range is not\nspecified. The FAIMS setting was not mentioned. Methods mention a 3 ms injection\ntime, while raw files suggest 3.5 ms.\nResponse: This is a critical consideration. The total cycle time for LC-MS/MS was 0.6\nseconds, which includes both the MS1 and subsequent MS2 scans. For DIA, the\nprecursor m/z range was 380–980 m/z, with an injection time of 3.5 ms (exact values\nas recorded in the raw data).\n3. The Methods described two sets of proteomics sample preparation protocols. Were\nthe plasma samples processed differently? If yes, how does this affect the results? Did\nthe authors investigate potential batch effect?\nResponse: Thank you for your constructive advice. We only prepared a single set of\nprotein samples and have removed any ambiguous statements to improve the clarity of\nthe manuscript. Regarding plasma sample preparation, plasma from patients at the three\nparticipating centers was collected following standardized SOPs to ensure accurate\nproteomic analysis. Blood samples were obtained within 48 hours of patient admission\nusing EDTA anticoagulant tubes. Within 4 hours of collection, samples were\ncentrifuged at 2000 rpm for 10 minutes, and the resulting plasma was stored at –80 °C.\nProteomic analyses were performed in batches once a sufficient number of samples had\nbeen collected, minimizing variability. By following a consistent plasma collection\nprotocol, we reduced potential errors and improved the reliability of the proteomic\nresults.\n4. The manuscript reports that predictive models trained on DIA data generalized well\nto targeted proteomics (likely PRM); please describe any normalization procedures\napplied between datasets.\nResponse: Thank you for your suggestion regarding the term of the model construction.\nThe DIA and PRM data were not directly integrated. However, to validate the model\nconstructed from the discovery cohort using validation cohort 1, the protein data\nobtained from both DIA and PRM were standardized. This normalization brought the\ndata onto the same scale, ensuring consistency between model training and validation.\n5. The reported 8-minute LC gradient yielding ~5,000 plasma protein groups is\nunexpected. Were depletion or enrichment strategies applied, or was neat plasma\nanalyzed? Please clarify and also include details on plasma sample collection, e.g., anti-\ncoagulants and centrifugation conditions.\nResponse: Thank you for highlighting this important aspect. We enriched low-\nabundance proteins using a serum low-abundance protein enrichment kit (DBE Kit-\nSHBP0818). This kit employs selectively affinity-based superparamagnetic nanoprobes\nwith a particle diameter of only 200 nm and absolute uniformity. The nanoparticle\nsurfaces are chemically conjugated with protein enrichment probes, with approximately\n300,000 protein-binding sites per nanobead for human proteome enrichment. Each\nnanoprobe occupies only 10 femtoliters of physical space, which is 10 billion times\nsmaller than a traditional 100 μL system, resulting in a 1-million-fold increase in\nsensitivity for detecting low-abundance proteins. Consequently, proteins that are\nundetectable by conventional mass spectrometry-based proteomics can be identified,\nenabling large-scale parallel detection and coverage over an ultra-wide abundance\nrange. Using this approach, our study was able to enrich over 5,000 proteins for\nproteomic analysis.\n6. With such short gradients, sufficient data points across chromatographic peaks are\nessential for accurate quantification. Please report median peak widths, number of data\npoints per peak, and injection amount.\nResponse: Thank you for the valuable input. The injection amount was 300 ng of\npeptides. The median peak width is shown in the figure below, and the number of data\npoints per peak, along with the injection amount, can be found in the attached Report\nfile.\n7. The Methods should include a section on data quality: number of quantified proteins\noverall and per sample, proportion of missing values, and how missing values were\nhandled. From scatterplots, it appears missing values were replaced with zero, which is\nproblematic since missingness in DIA usually reflects protein levels below detection\nlimits and can distort effect sizes. Please provide a rationale for this imputation strategy\nand discuss the potential consequences.\nResponse: For handling missing values in the dataset, samples with more than 50%\nmissing values were removed, while samples with less than 50% missing values were\nimputed. Specifically, the data preprocessing steps were as follows[1]: a.Visualization:\nPlot intensity vs. missing value proportion to confirm that missingness is primarily\nMNAR (Missing Not At Random). b.Filtering: Retain only proteins with at least 2\nobserved values in at least one treatment group. c.Imputation: Replace all missing\nvalues with SampMin, i.e., the minimum intensity value observed in the corresponding\nsample. d.DEP Analysis: DEP analysis was performed using the Limma package, with\nlinear regression. Proteins with an adjusted P-value <0.05 and |log2 fold change| >1\nwere considered significant DEPs.\n[1]Liu M, Dongre A. Proper imputation of missing values in proteomics datasets for\ndifferential expression analysis. Brief Bioinform. 2021 May 20;22(3):bbaa112. doi:\n10.1093/bib/bbaa112. PMID: 32520347.\n8. Correlation with clinical markers is modest: e.g., CRP measured by MS correlated\nonly moderately with clinical CRP (Spearman R=0.59), while CALCA correlated most\nstrongly with clinical CRP. This raises concerns about data quality; please discuss\npotential causes.\nResponse: This is an excellent point. CALCA is a calcitonin-related peptide. During\ninfection, changes in PCT (procalcitonin) are more pronounced than changes in CRP.\nIn patients with bacterial infection, PCT levels begin to rise within 2–4 hours and are\nnot affected by renal function, whereas CRP increases 24–48 hours after the onset of\ninflammation, and its rise is slower than that of PCT. Additionally, CRP reflects\ninflammation related to immune response and host defense mechanisms, while PCT is\na highly sensitive biomarker for bacterial infection. This may explain why the PCT-\nrelated peptide CALCA shows a stronger correlation with clinical CRP levels.\n9. The authors interpret increased coagulation activity in secondary infection, but the\nproteomics data appear to show reduced coagulation-related proteins alongside\nincreased inflammatory proteins. Please reconcile these discrepancies with the cited\nliterature.\nResponse: Thank you for your suggestion regarding the term of our manuscript. In our\nstudy, we observed that PLAT and PLG, whose elevated expression is typically\nassociated with thrombosis, were negatively correlated with inflammatory markers.\nThis suggests that bacterial infection induced inflammation can lead to a reduction in\ncoagulation function. This observation is consistent with previous findings (Reference\n15), and Reference 16 further demonstrated that increased PLG can promote the\nresolution of inflammation. Additionally, Reference 18 reported that a decline in\ninflammatory markers can facilitate activation of the fibrinolytic system. In KEGG\npathway analysis, the platelet activation pathway was enriched with decreased pathway\nactivity, which may result in reduced platelet counts and an increased risk of bleeding,\nrather than enhanced coagulation activity. We have elaborated this point in more detail\nin the manuscript to avoid any ambiguity. Taken together, our proteomics data suggest\nthat the observed reduction in coagulation-related proteins is associated with increased\ninflammation, and we will further clarify this relationship in the manuscript to ensure\nthat the intended meaning is conveyed clearly.\n10. Table 1 should specify units and data format (mean ± s.d., or median with CI).\nConsider presenting baseline characteristics stratified by secondary infection status in\nboth discovery and validation cohorts to aid interpretation of predictive features.\nResponse: Thank you for your constructive advice. In Table 1, we have specified the\ndata presentation format as mean ± s.d. Similarly, in Supplementary Table 1, we\ncompared the baseline characteristics of SI and non-SI patients in both the discovery\ncohort and validation cohort 1.\n11. In targeted proteomics, 31 DEPs were “successfully detected.” Please indicate how\nmany proteins were originally selected for validation and explain why the remainder\ncould not be detected or validated.\nResponse: Your suggestion makes our article more rigorous. In the untargeted\nproteomics analysis, we initially selected over 170 proteins for PRM. To identify\nproteins that were stably expressed across all patients, we excluded proteins that could\nnot be enriched in the shotgun experiment and those with fewer than three detected ions.\nAs a result, 31 reliable proteins were ultimately selected. These stably expressed\nproteins across all patients can serve as candidates for biomarker development.\n12. The untargeted and targeted proteomics sections (Figures 2 and 3) appear highly\nsimilar. By design, targeted proteomics should serve as validation of a subset of proteins\nidentified in the discovery cohort, and therefore the focus should be on validation\noutcomes rather than presenting the results in an exploratory, discovery-style manner.\nIn addition, parts of the text are overly descriptive (e.g., listing top correlating proteins\nwith clinical variables) without providing sufficient interpretation. A stronger emphasis\non the implications of these findings would improve clarity and strengthen the overall\nnarrative.\nResponse: Thank you for your suggestion. Due to the difficulty of timely collecting\nbaseline blood samples and following patients until the occurrence of secondary\ninfection (SI), the sample size of the validation cohorts was limited. In the future, we\nplan to collect additional samples for PRM analysis to further validate both the\nbiomarkers and the predictive model. Additionally, in the Results section, we cited\nreferences 14–23 to support the relevance of highly correlated proteins with validation\nand coagulation. Based on these references, we further explained the potential\nconnections, thereby reinforcing our conclusions.\n13. Feature selection (using univariate and multivariate logistic regression) and final\nmodel construction were both performed on the same discovery cohort. This risks\ninformation leakage and may inflate reported performance in the discovery cohort. To\nproperly demonstrate superiority over clinical variables within the discovery cohort, the\nauthors should designate a left-out test set or adopt an alternative strategy (e.g., cross-\nvalidation selecting features within each fold) to avoid bias.\nResponse: Thank you for your insightful suggestion. To minimize potential bias in\nselecting protein biomarkers, we implemented the alternative strategy suggested by the\nreviewer. Each time a new protein was added, we performed 5-fold cross-validation,\nrepeated the analysis 10 times, and calculated the average AUC. This approach allowed\nus to identify potential protein biomarkers for subsequent logistic regression analysis.\nTo further validate the accuracy of the biomarkers, an independent validation cohort 2\ncomprising 60 patients was analyzed using ELISA. The protein levels measured by\nELISA were consistent with those observed in both the discovery and validation cohort\n1. Based on these results, the logistic regression (LR) model achieved an AUROC of\n0.883, demonstrating good discriminative performance for secondary infection (SI).\nAdditionally, the model constructed in the discovery cohort was validated in\nindependent validation cohort 1, achieving an AUROC of 0.873, with other model\nevaluation metrics also at high levels. Together, these steps—cross-validation, ELISA\nvalidation, and independent cohort testing—helped minimize potential errors and\nconfirm the robustness of our findings (lines 455-464).\nMinor points:\n1. Please report the statistical tests and thresholds used to identify differentially\nexpressed proteins.\nResponse: Thank you for your careful reading of our manuscript. The threshold for\ndifferentially expressed proteins (DEPs) was set as P < 0.05 and |log2 fold change| > 1.5.\n2. Line 280 the mentioned proteins do not match those presented in Figure 2E.\nResponse: Thank you for your rigorous advice. We have revised the wording in the\nmanuscript and sincerely apologize again for the oversight that led to this writing error.\n3. Figure 3H: the claimed correlation seems to be driven by outliers. Please re-examine\nthis analysis and consider presenting an additional visualization (e.g., zoomed-in view\nexcluding extreme values) to better illustrate the relationship among the majority of\nsamples.\nResponse: This is an excellent point and is well taken. We removed the outliers and\nregenerated the figure. The updated results show that INR remains strongly correlated\nwith the three proteins, confirming the robustness of our findings.\n4. Functional enrichment analysis (Lines 266–272) is unclear: how was this performed\non top of the protein interaction analysis? Please clarify. Please include figure legend\nfor Supplementary Figure 3D.\nResponse: This is a critical consideration. In the PPI analysis, red lines indicate proteins\nenriched in the pathways, while blue lines represent protein–protein interactions. We\nhave added this legend to Figure S3D in the revised manuscript to make the figure and\nour interpretation clearer.\n5. The argument that targeted proteomics “emphasizes the pivotal role of inflammation–\ncoagulation imbalance” appears circular, since the targeted panel was pre-selected\nbased on those processes.\nResponse: Thank you for your detailed and constructive feedback. In the discovery\ncohort, we initially found that 27% of thDEPs were inflammation-related and 9.5%\nwere coagulation-related. KEGG, GSEA, and PPI network analyses further revealed a\nstrong connection between inflammatory activation and coagulation dysfunction,\nsuggesting that this interaction may play a key role in the development of SI. To identify\npotential biomarkers, we applied the mRMR algorithm followed by a stepwise addition\napproach to predict SI occurrence, ultimately identifying a panel of 10 proteins with the\nhighest predictive performance. Notably, more than half of these proteins were related\nto inflammation and coagulation, further supporting their biological relevance. Based\non these findings, we conducted PRM to confirm the results obtained from the\ndiscovery cohort. Therefore, the PRM analysis represents a validation process rather\nthan a circular approach.\n6. Scatter plots (Line 323) are simply alternative visualizations of the same data and\nshould not be described as independent confirmation.\nResponse: We totally agree with you. We plotted scatter plots of the top three ranked\nproteins against key clinical indicators to further explore whether the proteins most\nstrongly associated with clinical indices could support the observed link between\ncoagulation and inflammation. Indeed, we found a strong positive correlation between\nINR/PT and IL1RL1, supporting the critical interplay between inflammatory activation\nand coagulation dysfunction in the development of SI.\n7. Figure 2E–H: please include y-axis scales.\nResponse: Thank you for the valuable advice. We have added Y-axis scales to Figure\n2E–H.\n8. Supplementary Figure 1: title should read “proteomics,” not “metabolomics.” The\nworkflow diagram contains errors: Orbitrap Astral was used, yet it seems a Tribrid\ninstrument icon is shown; there are duplicated MS instruments and digestion steps.\nResponse: Thank you for your suggestive comments. We have revised the title wording\nand replaced the instrument image. In addition, we removed duplicate images of the\ninstrument and the enzymatic digestion steps.\n9. Supplementary Figure 5: duplicated NPV plots in panel B.\nResponse: Thank you for your suggestive comments. We have replaced the incorrect\nfigure with the one showing the specificity of the model.\n10. Figure 4A: please state what separates individuals along PLS Component 1.\nResponse: Thanks for the advice. We performed a partial least squares discriminant\nanalysis (PLS-DA) based on protein abundance to visualize the abundance matrix of SI\npatients and non-SI patients. PLS-DA is a supervised dimensionality reduction method\nthat allows for the analysis of overall differences between the SI and non-SI groups.\nThis approach provides an initial assessment of whether proteomic biomarkers can\neffectively distinguish patients with SI from those without SI.\n11. Supplementary Tables 4–7: “nr. Observations” appears to denote sample size but is\ninconsistent (e.g., 974 = number of DEPs). Please correct. Also annotate how fold\nchanges were calculated.\nResponse: Thank you for your insightful suggestion. The number 974 represents the\ndifferentially expressed proteins (DEPs). To clarify the table content, we have replaced\nall related descriptions with “DEPs.” Fold changes (FC) were calculated as the ratio of\nprotein expression levels between the two groups, and log2 transformation was applied\nfor visualization and analysis.\n12. Supplementary Table 7: header inaccurate.\nResponse: Thank you for your careful reading of our manuscript. We have corrected\nthe wrong table headers.\n13. Supplementary Table 13: likely typo (“MRMR” should replace “MRME”).\nResponse: Thank you for pointing that out. We have corrected “MRME” to “mRMER.”\n14. Please add the abbreviation for GOV.\nResponse: This is a critical consideration. We have added the abbreviation “GOV” to\nthe “List of Abbreviations” section (line 33)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The study by Feixiang Xiong et al. aimed to develop and validate a multi-parameter predictive model based on plasma proteomic profiling to assess the risk of secondary infection (SI) in patients with HBV-related liver cirrhosis, using machine learning approaches. To this end, the authors conducted a plasma proteomic analysis of blood plasma samples from 174 patients, subdivided into a retrospective discovery cohort of 114 patients and a prospective validation cohort of 60 patients. The authors then developed a predictive model consisting of the expression of four blood protein markers identified by proteomics, combined with total bilirubin and aspartate aminotransferase (AST). This model accurately identified patients with HBV-related liver failure at risk of SI.\nThis study is highly relevant to the field, as the prognostic performance of this model seems to outperform classical scoring systems such as CLIF-C, ACLF and MELD in this setting. The key points raised in the application have been addressed in the point-by-point response and in the manuscript. This includes a validation of the results using conventional immunoassays demonstrating the applicability of the results with more cost-effective ELISA. Some minor points should be still improved (see specific comments).\n1) The authors must clearly indicate (in the material and methods section) how the plasma samples have been taken in the three participating centres and patient cohorts. Which anti-coagulant have been used to generate plasma? Variable blood processing procedures may contribute to plasma proteomic variability (PMID 33468058) and must therefore be controlled and discussed if this was not standardized in this study. (This point was answered by the authors in the point-by-point response to the reviewer but were not included in the revised manuscript)\n2) Generally, many figure panels (main and supplements) are still hardly readable as labels, legends, and/or font sizes are still too small (especially, when the figures are printed).\n3) Heatmaps (Figures 2 and 3). The authors explained in their response that the heatmaps indicate log2.FC. This must be consistently stated in the respective figure legends as well as within the heatmap figure panels. This has been partially done in the heatmap of Fig. 3 but hardly readable (see also previous comment).\n4) Supplementary figure S2D: the mentioned median SI of the individual cohorts should be indicated within the figure.\n5) The way the figures are ordered at the end of the main MS seem not correct (Figure 2 before Figure 1). Also, Tables are placed between the main figures. As figures themselves are not numbered it is very difficult for reviewers to navigate between the results– this includes also the supplementary figures.", "reviewer_2": "My concerns have been addressed by the authors.", "reviewer_3": "The authors have addressed most of my previous concerns; however, several important issues remain:\n1. The statement “By following a consistent plasma collection protocol, we reduced potential errors and improved the reliability of the proteomic results” could be more convincingly supported by a PCA analysis demonstrating the absence of cohort-specific batch effects. In addition, the authors should elaborate on how samples from different collection sites were randomized throughout the experimental workflow.\n2. Although the authors provided a detailed report describing their data filtering strategy, this information does not appear to have been incorporated into the revised manuscript (unless I missed it). The same applies to the plasma proteome enrichment workflow. The manuscript should explicitly report data quality metrics and clearly state the final number of proteins retained for downstream analyses after filtering for missing values.\n3. While the authors sufficiently explained why CALCA correlates strongly with clinical measurement of CRP, they did not address the more fundamental issue: the only moderate correlation between clinical CRP measurements and MS-based CRP quantification. If samples were collected at the same time, these two orthogonal analytical methods should, in principle, yield highly consistent results. This discrepancy requires clarification. One possible explanation is that the enrichment strategy used prior to MS analysis may have distorted CRP quantification."}, "author_response": "Responses to the Reviewers\nReviewer #1 (Remarks to the Author):\nThe study by Feixiang Xiong et al. aimed to develop and validate a multi-parameter\npredictive model based on plasma proteomic profiling to assess the risk of secondary\ninfection (SI) in patients with HBV-related liver cirrhosis, using machine learning\napproaches. To this end, the authors conducted a plasma proteomic analysis of blood\nplasma samples from 174 patients, subdivided into a retrospective discovery cohort of\n114 patients and a prospective validation cohort of 60 patients. The authors then\ndeveloped a predictive model consisting of the expression of four blood protein\nmarkers identified by proteomics, combined with total bilirubin and aspartate\naminotransferase (AST). This model accurately identified patients with HBV-related\nliver failure at risk of SI.\nThis study is highly relevant to the field, as the prognostic performance of this model\nseems to outperform classical scoring systems such as CLIF-C, ACLF and MELD in\nthis setting. The key points raised in the application have been addressed in the point-\nby-point response and in the manuscript. This includes a validation of the results\nusing conventional immunoassays demonstrating the applicability of the results with\nmore cost-effective ELISA. Some minor points should be still improved (see specific\ncomments).\n1) The authors must clearly indicate (in the material and methods section) how the\nplasma samples have been taken in the three participating centres and patient cohorts.\nWhich anti-coagulant have been used to generate plasma? Variable blood processing\nprocedures may contribute to plasma proteomic variability (PMID 33468058) and must\ntherefore be controlled and discussed if this was not standardized in this study. (This\npoint was answered by the authors in the point-by-point response to the reviewer but\nwere not included in the revised manuscript)\nResponse: Thank you for the valuable input. Plasma samples from patients at the three\nparticipating centers were collected following standardized SOPs to ensure the\naccuracy of proteomic analyses. Blood samples were collected within 48 hours of\npatient admission using EDTA anticoagulant tubes. Within 4 hours of collection,\nsamples were centrifuged at 2000 rpm for 10 minutes in the laboratory, and the resulting\nplasma was stored at –80 °C. We have added a description of the plasma sample\nhandling in the manuscript and demonstrated the absence of batch effects through PCA\nanalysis. (Figure S3e)(lines 468-472)\n2) Generally, many figure panels (main and supplements) are still hardly readable as\nlabels, legends, and/or font sizes are still too small (especially, when the figures are\nprinted).\nResponse: Thank you for your careful reading of our manuscript. We have adjusted the\nsize of the figure legends to make the figures more readable.\n3) Heatmaps (Figures 2 and 3). The authors explained in their response that the\nheatmaps indicate log2.FC. This must be consistently stated in the respective figure\nlegends as well as within the heatmap figure panels. This has been partially done in the\nheatmap of Fig. 3 but hardly readable (see also previous comment).\nResponse: Thank you for pointing that out. We have adjusted the Figure legend.\n4) Supplementary figure S2D: the mentioned median SI of the individual cohorts should\nbe indicated within the figure.\nResponse: Thanks for the advice. We have added the median time to secondary\ninfection for each patient group.\n5) The way the figures are ordered at the end of the main MS seem not correct (Figure\n2 before Figure 1). Also, Tables are placed between the main figures. As figures\nthemselves are not numbered it is very difficult for reviewers to navigate between the\nresults– this includes also the supplementary figures.\nResponse: This is a critical consideration. We have rearranged the order of the figures\nand tables to make the results easier to follow.\nReviewer #3 (Remarks to the Author):\n1. The statement “By following a consistent plasma collection protocol, we reduced\npotential errors and improved the reliability of the proteomic results” could be more\nconvincingly supported by a PCA analysis demonstrating the absence of cohort-specific\nbatch effects. In addition, the authors should elaborate on how samples from different\ncollection sites were randomized throughout the experimental workflow.\nResponse: Thank you for the valuable input. Regarding the concern about potential\ncohort-specific batch effects, we conducted a principal component analysis (PCA) of\nthe plasma proteomic data. The results are presented as a 3D PCA plot (Figure S3E)\n(line 176), which shows no obvious clustering by collection site, indicating minimal\nbatch effects and supporting the reliability of our proteomic measurements.\nTo minimize potential batch and positional effects, we implemented a structured\nrandomization procedure using the sample () function in R. Samples were first stratified\nby center (3 centers) and clinical group (2 groups), and randomization was performed\nwithin each stratum to ensure that each 96-well plate contained balanced proportions of\ncenters and groups. After obtaining the stratified randomization scheme, samples were\nloaded onto the 96-well plates strictly according to the randomized results, resulting in\nan even distribution of different centers and groups across rows and columns. This\napproach ensured balanced sample allocation across plates and prevented center- or\ngroup-specific clustering during data acquisition.\n2. Although the authors provided a detailed report describing their data filtering strategy,\nthis information does not appear to have been incorporated into the revised manuscript\n(unless I missed it). The same applies to the plasma proteome enrichment workflow.\nThe manuscript should explicitly report data quality metrics and clearly state the final\nnumber of proteins retained for downstream analyses after filtering for missing values.\nResponse: We appreciate the reviewer’s valuable comment. The protein enrichment\nworkflow is described in lines 495–506 of the manuscript.\nThe DIA MS data were analyzed using DIA-NN 1.8.1. MS data were searched against\nthe UniProtKB reviewed (Swiss-Prot) database (Homo sapiens (species), 20422 total\nentries, downloaded 12/2024. The trypsin was selected as the digestion enzyme. The\nmaximal 1 missed cleavage sites and the mass tolerance of 10 ppm for precursor ions\nand 10 ppm for fragment ions were defined for database search. Carbamidomethylating\nof cysteines was defined as a fixed modification, while acetylation of protein N-\nterminal, oxidation of Methionine was set as variable modifications for database\nsearching. The maximum number of variable modifications was 1. The peptide length\nrange was set from 7-30. The charge of the peptide was from 1 to 4. The fragment ion\nm/z range was from 150-2000. The database search results were filtered and exported\nwith <1% false discovery rate (FDR) at the peptide-spectrum-matched level and the\nprotein level, respectively.\nData with non-missing values in at least 50% of samples from one group were retained.\nThe remaining missing values were imputed with half the minimum value, followed by\nmedian normalization. After the above steps, 4,901 high-confidence proteins were\nobtained for subsequent data analysis. (lines 516-527)\nAll datasets underwent quality control procedures, as described in lines 170–176 of the\nmanuscript. First, we performed quality control on the proteomic data. As shown in the\nSupplementary Figure S3a–b, the peptide length distribution exhibited the expected\ntryptic pattern, and proteins displayed a physiologically consistent molecular weight–\npI distribution. After applying data-completeness filtering, 4,901 proteins were retained\nfor downstream analysis(Figure S3c). Protein coverage categories were well balanced,\nand most proteins were supported by multiple unique peptides(Figure S3d). The 3D\nPCA plot confirmed tight clustering of SI, NSI, and QC samples, indicating minimal\nbatch effects(Figure S3e)\n3. While the authors sufficiently explained why CALCA correlates strongly with\nclinical measurement of CRP, they did not address the more fundamental issue: the\nonly moderate correlation between clinical CRP measurements and MS-based CRP\nquantification. If samples were collected at the same time, these two orthogonal\nanalytical methods should, in principle, yield highly consistent results. This\ndiscrepancy requires clarification. One possible explanation is that the enrichment\nstrategy used prior to MS analysis may have distorted CRP quantification.\nResponse: This is an excellent point. We concur that when the plasma sample used for\nMS analysis and the clinical CRP test are collected simultaneously, the two independent\nmeasurement approaches should, in principle, provide very similar results. Two factors\nprobably contributed to this observation. First, in a subset of patients, the clinical CRP\ntests were performed at time points that did not precisely match the time of plasma\nsample collection used for MS analysis, and this temporal gap may have introduced\nbiological variability due to the dynamic fluctuations of CRP during acute inflammation.\nSecond, some patients did not undergo clinical CRP testing at the corresponding time\npoint, resulting in incomplete matching between clinical laboratory records and MS-\nderived data. Together, these factors reduced the degree of correlation observed\nbetween the two modalities despite the high analytical precision of the MS-based\nmethod."}, {"round": 3, "reviewer_comments": {"reviewer_3": "1. The authors performed PCA to evaluate potential batch effects arising from different collection sites. However, in Figure S3E, the samples are color-coded by disease group rather than by collection site. Therefore, this figure cannot support the statement that “there is no obvious clustering by collection site, indicating minimal batch effects and supporting the reliability of our proteomic measurements.” In addition, the 3D PCA plot in this case is not ideal for visualizing clustering patterns. We recommend instead providing three 2D plots: PC1 vs PC2, PC1 vs PC3, and PC2 vs PC3.\n2. The authors stated that “the protein enrichment workflow is described in lines 495–506 of the manuscript.” However, the text in lines 495–506 describes data-search parameters rather than the enrichment workflow. In the first-round response, the authors mentioned using a beads-based enrichment workflow(?) that enabled deep proteome coverage, but this methodological detail was not incorporated into the manuscript. Please include a clear description of the enrichment workflow in the Methods section as previously requested.\n3. The authors provided an explanation for the moderate correlation observed between clinical CRP measurements and MS-based proteomics data. However, this explanation has not been added to the manuscript. Please include this point as a limitation in the Discussion."}, "author_response": "Response: Thanks for the advice. We have performed a Principal Component Analysis (PCA) again, based on the sample collection sites. Principal component analysis (PCA) based on all quantified proteins showed no evident site-driven separation among samples from the three study centers. Samples from Beijing Ditan Hospital (site 1), Huashan Hospital (site 2), and the First Affiliated Hospital of Zhejiang University School of Medicine(site 3) were largely intermingled across PC1, PC2, and PC3, indicating minimal batch effects attributable to collection location. Moreover, GR and NGR samples did not cluster by site, further supporting that inter-center differences did not introduce systematic variability into the proteomic profiles. (lines 175-181)\n2. Thank you for the valuable input. The magnetic bead–based protein enrichment procedure followed the methodology described by Omid et al [1]. Plasma samples (50 μL) were processed using the DBE kit following the manufacturer's instructions (Shanghai Bioprofile, China). First, 20 μL of magnetic beads were washed three times with 200 μL of Washing-Buffer in tubes. Each wash involved mixing at 1300 rpm for 30 seconds, followed by magnetic separation using a magnetic rack for 1 min and removal of the supernatant. This washing step was repeated for a total of three washes. Then, 50 μL of plasma and 50 μL of Incubation-Buffer were both added to the beads and mixed at 1300 rpm for 10 min at room temperature. Tubes were placed on the magnetic rack, and the supernatant was discarded. Samples were washed with 150 μL of Washing-Buffer for a total of four washes. For lysis, 40 μL of Elution Buffer was added to the beads and mixed at 1300 rpm for 20 min at room temperature. For protein digestion, 1µL 400mM TCEP and 2µL 800mM CAA were added into the eluted protein buffer, vortexed, and 95°C for 3min (Reduction and Alkylation). Then 160 µL of 50 mM NH₄HCO₃ was added, and then proteins were digested with 0.5 µg Lys-C/Trypsin (1:2) at 37°C overnight. The digestion reaction was stopped by adding 60 μL of STOP solution and by mixing the tubes at 1300 rpm for 1 min at RT. The whole mixture was transferred to the C18 stage tip to clean the peptides. Samples were washed sequentially with 100 μL of methanol and 100 μL of 0.1% TFA, and peptides were eluted with 100 μL of 50% Acetonitrile (0.1% TFA) twice in the same collection tube. Samples were dried down in a SpeedVac (Eppendorf), and peptides were redissolved in 10 μL of buffer with 2 % ACN, 0.1 %FA. Peptide concentration was determined using a Nanodrop and then analyzed by LC-MS/MS. (lines 507-526)\n[1] Blume JE, Manning WC, Troiano G, Hornburg D, Figa M, Hesterberg L, Platt TL, Zhao X, Cuaresma RA, Everley PA, Ko M, Liou H, Mahoney M, Ferdosi S, Elgierari EM, Stolarczyk C, Tangeysh B, Xia H, Benz R, Siddiqui A, Carr SA, Ma P, Langer R, Farias V, Farokhzad OC. Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat Commun. 2020 Jul 22;11(1):3662.\n3. Thank you for your constructive advice. We have added the limitation in the discussion. Besides, the CRP values obtained from routine clinical testing did not show the highest correlation with the MS-derived CRP measurements. One important reason is that the clinical tests were not always performed at the same time as the plasma sampling used for MS, and in some patients, the clinical CRP result was simply not available for that time point. As a result, the normal biological fluctuations of CRP during acute inflammation likely introduced additional variability, which explains the modest concordance observed between the two methods. (lines 428-433)"}]

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