Community-acquired pneumonia with Staphylococcus aureus and viral co-infection: clinical characteristics and pathogen genomic analysis

Introduction

Respiratory infections have become a leading cause of mortality globally. Over the past decade, numerous studies have reported that bacterial-viral co-infection in pulmonary infections caused by respiratory pathogens has attracted significant attention due to its complex pathological mechanisms and unfavorable prognosis. Staphylococcus aureus (SA) causes a wide spectrum of pathogenic infections, ranging from mild skin and soft tissue infections to severe conditions such as necrotizing pneumonia, sepsis, and infective endocarditis. The incidence of SA infections continues to rise, and the clinical management of SA pneumonia faces severe challenges with the increasing prevalence of multidrug-resistant strains [e.g., methicillin-resistant Staphylococcus aureus (MRSA)] and the growing population of immunocompromised individuals. Concurrently, the widespread transmission of respiratory viruses (e.g., influenza virus, respiratory syncytial virus, severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2)] has further exacerbated the burden of infectious diseases (1). Recent studies demonstrated that 28–50% of adults with community-acquired pneumonia (CAP) have bacterial-viral coinfection (2). Notably, viral-bacterial co-infection is not merely an additive phenomenon. Instead, it creates a synergistic pathogenic effect through mechanisms such as disrupting host defense barriers, modulating immune responses, and promoting pathogen adaptive evolution, leading to clinical deterioration and increased treatment difficulty (3). However, systematic research on the clinical characteristics and interaction mechanisms of SA and viral co-infection pneumonia remains relatively scarce.

Previous studies indicate that viruses and bacteria can promote co-infection through various pathways. For instance, influenza virus infection can damage respiratory ciliated epithelial cells, exposing basement membrane components (such as fibronectin and laminin), which provide receptors for SA adhesion. Simultaneously, the virus-induced type I interferon (IFN-I) response may suppress neutrophil chemotaxis function, thereby weakening the host’s ability to clear SA (4). On the other hand, virulence factors secreted by SA [such as α-toxin and phenol-soluble modulins (PSMs)] may further exacerbate tissue damage by activating inflammasomes or interfering with antiviral immunity (5,6). However, most current research is confined to animal models or in vitro experiments. The specific characteristics, risk factors, and impact on prognosis of SA-viral co-infection in clinical populations have not yet been fully elucidated. Furthermore, differences in the interactions between different virus types [e.g. influenza A virus (IAV) and SARS-CoV-2] and SA, as well as the pathways through which co-infection leads to alterations in SA drug resistance phenotypes or virulence gene expression, require further exploration.

In current clinical practice, the diagnosis and treatment of SA and viral co-infection face multiple challenges. Firstly, the non-specific symptoms during early infection often led to underdiagnosis of bacterial/viral co-infection, delaying the appropriate use of antibiotics and antiviral drugs. Secondly, virus-mediated immune dysregulation may alter the clinical phenotype of SA infection (e.g., causing atypical dynamic changes in inflammatory markers), increasing the difficulty of disease assessment.

In light of these challenges, this study was designed to systematically compare the clinical characteristics between survivors and non-survivors with Staphylococcus aureus community-acquired pneumonia (SA-CAP). Besides, the study seeks to investigate the potential influence of SA-specific factors, particularly antimicrobial resistance profiles and virulence signatures, on the occurrence and clinical course of viral co-infection. The findings are expected to provide a crucial theoretical foundation for the precise diagnosis and management of co-infection pneumonia, while also establishing essential clinical groundwork for subsequent mechanistic research. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2791/rc).

Methods

This study enrolled a total of 118 hospitalized patients diagnosed with CAP between November 2022 and April 2025 at The Fourth Affiliated Hospital of Soochow University and The First Affiliated Hospital of Soochow University. Medical records of all patients were reviewed by two physicians from The Fourth Affiliated Hospital of Soochow University.

Patients diagnosed with “pulmonary infection” or “pneumonia” were initially identified through the hospital electronic medical record system. The cohort was then constructed by applying the predefined inclusion and exclusion criteria. A total of 118 patients who met the diagnostic criteria for SA-CAP were ultimately enrolled (Figure 1). Based on in-hospital mortality, SA-CAP patients were divided into survivors and non-survivors. The aim was to explore independent risk factors for in-hospital mortality among SA-CAP patients, with a specific focus on whether respiratory viral co-infection serves as an independent risk factor for death. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All protocols in this study have been approved by the ethics committee of The Fourth Affiliated Hospital of Soochow University (No. 241294). Informed consent was obtained from all patients for being included in the study.

Figure 1 The study cohort. This study enrolled hospitalized patients diagnosed with SA-CAP from The Fourth Affiliated Hospital and The First Affiliated Hospital of Soochow University between November 2022 and April 2025. CAP, community-acquired pneumonia; IAV, influenza A virus; SA, Staphylococcus aureus; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

Inclusion criteria

Diagnosis of CAP: met the diagnostic criteria for pneumonia as defined by the Chinese Guidelines for the Diagnosis and Management of Adult CAP (7).

Evidence of SA infection: positive culture of respiratory specimens (sputum or bronchoalveolar lavage fluid) with clinical determination as the causative pathogen (colonization excluded).

Viral co-infection: respiratory viral infection [e.g., influenza virus, SARS-CoV-2, respiratory syncytial virus (RSV), adenovirus] diagnosed by Polymerase chain reaction (PCR) or antigen testing within the same period (within 7 days of pneumonia onset).

Exclusion criteria

• Mixed infection with other bacteria/fungi/mycoplasma [e.g., positive culture for Streptococcus pneumoniae or Pseudomonas aeruginosa, positive G test or GM test, positive mycoplasma immunoglobulin M (IgM) antibody, etc.].
• Immunodeficiency disorders [e.g., human immunodeficiency virus (HIV) infection, post solid organ or hematopoietic stem cell transplantation].
• Missing data for key variables (e.g., incomplete pathogen results or records of underlying diseases).

Data collection

Clinical data for all patients were collected through the hospital electronic medical record system.

Patient baseline characteristics: gender, age, underlying diseases (hypertension, diabetes, chronic obstructive pulmonary disease, hepatitis B, history of heart disease, history of kidney disease, history of neurological disease), smoking history.

Infection characteristics: type of viral infection, presence of SA bloodstream infection.

Clinical parameters on admission: respiratory rate, pH, fraction of inspired oxygen (FiO₂), partial pressure of oxygen (PaO₂), partial pressure of carbon dioxide (PaCO₂), lactate (Lac), base excess (BE), white blood cell count (WBC), lymphocyte count (L), neutrophil count, platelet count (PLT), hemoglobin (Hb), C-reactive protein (CRP), procalcitonin (PCT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin (Alb), prealbumin, total bilirubin, creatinine (Cr), fibrinogen, activated partial thromboplastin time (APTT), prothrombin time (PT), D-dimer, high-sensitive troponin T (hs-TnT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), interleukin-6 (IL-6), ferritin, erythrocyte sedimentation rate (ESR), sequential organ failure assessment (SOFA) score (8).

Clinical complications: myocardial injury (9), empyema, septic shock (8), acute respiratory distress syndrome (ARDS) (10), acute kidney injury (AKI) (11).

Relevant treatments: use of vancomycin, administration of antiviral therapy, use of extracorporeal membrane oxygenation (ECMO), use of continuous renal replacement therapy (CRRT), highest level of oxygen therapy required (no oxygen therapy, nasal cannula oxygen, high-flow nasal cannula, non-invasive mechanical ventilation, invasive mechanical ventilation).

Prognostic indicators: in-hospital mortality, intensive care unit (ICU) length of stay (days), duration of mechanical ventilation (hours).

Statistical analysis

A database was established using Excel 2019 for data entry and summarization. Variables for which the missing data exceeding 5% were excluded from the analysis. For variables with missing data less than or equal to 5%, missing values were imputed using the mode. Statistical management and analyses were performed using SPSS 23.0 and SPSS PRO. A two-tailed P value <0.05 was considered statistically significant. The normality of continuous variables was assessed using the Shapiro-Wilk and Kolmogorov-Smirnov tests. Continuous variables not conforming to a normal distribution were presented as median and interquartile range and were compared using the Mann-Whitney U test. Normally distributed continuous variables were presented as mean ± standard deviation and were compared using the t-test. Categorical variables were presented as proportions and were compared using the Chi-squared test.

To mitigate the Type I error (false-positive rate) resulting from multiple comparisons across variables, the Holm-Bonferroni correction was applied to continuous clinical indicators (e.g., coagulation, inflammatory, and blood gas parameters). Variables eligible for correction were ranked by their original P values, and the adjusted significance threshold was computed dynamically to assess statistical significance. Correction was performed exclusively for continuous and binary clinical indicators, while variables lacking clinical relevance were excluded. A fully adjusted table was provided. The adjusted threshold was defined as α_i= 0.05/(k − i + 1), where k denotes the total number of variables undergoing correction and i represents the rank order of each variable. A result was deemed statistically significant after correction if the original P value ≤ the corresponding α_i. For clinically established core prognostic indicators, the principle of a priori clinical relevance was applied, and strict statistical correction was not imposed.

Only the variables that were statistically significant after Holm-Bonferroni correction and had clear clinical significance will be finally included in the subsequent random forest and multivariate regression analyses. The random forest algorithm was chosen for its robustness in handling multidimensional clinical data and identifying non-linear relationships: data resampling, training set to test set ratio of 9:1, 3-fold cross-validation, Gini index as the node splitting criterion, number of decision trees =50, minimum samples per leaf node =3, maximum tree depth =5, and maximum number of leaf nodes =50. The feature importance for each included variable was calculated. The top 10 variables from the feature importance analysis were subsequently entered into a multivariable stepwise logistic regression analysis (forward: conditional). To enhance the results interpretability, continuous variables were converted to categorical variables according to the clinical laboratory reference values prior to this regression analysis. The probabilities for stepwise entry and removal of variables were set at 0.05 and 0.10, respectively. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported.

Additionally, 20 hospitalized SA-CAP patients were selected, and their SA and IAV isolates were submitted for whole-genome sequencing (WGS), with a focus on characteristics such as resistance genes and virulence genes.

Results

Independent risk factors for in-hospital mortality in SA-CAP patients

This study enrolled a total of 118 SA-CAP patients, comprising 74 males and 44 females. The overall in-hospital mortality rate was 30.5%. Compared to the SA monoinfection group, the SA-IAV co-infection group had a significantly higher mortality rate (80.0% vs. 31.3%, P=0.001). Based on in-hospital mortality status, patients were divided to survivors and non-survivors. No statistically significant differences were observed in gender distribution, age, or prevalence of underlying diseases (Table 1).

As shown in Table 2, no statistically significant difference was observed in the rate of respiratory viral co-infection between survivors (38 patients, 46.3%) and non-survivors (16 patients, 44.4%). However, IAV co-infection was significantly more frequent in non-survivors (8 patients, 22.2%) than in survivors (2 patients, 2.4%), P=0.003. In contrast, SARS-CoV-2 detection was higher in survivors (26.8%) compared to non-survivors (5.6%), P=0.004. Furthermore, concurrent SA bloodstream infection occurred markedly more often in non-survivors (14 patients, 38.9%) than in survivors (6 patients, 7.3%), P<0.001.

Compared with survivors, non-survivors presented on admission with significantly higher respiratory rates (20 vs. 16 breaths/min, P=0.03) and lower oxygenation indices (112.9 vs. 323.1 mmHg, P=0.001). Laboratory results showed that non-survivors had significantly lower PLTs (161×10⁹/L vs. 212×10⁹/L, P<0.01), while PCT (1.90 vs. 0.12 ng/mL), AST (52.0 vs. 25.7 U/L), and hs-TNT (74.8 vs. 11.6 pg/mL) levels were all significantly elevated (all P<0.05). Coagulation profiles in non-survivors also showed notable abnormalities, including prolonged APTT (52.3 vs. 36.8 s, P=0.002) and PT (16.2 vs. 14.7 s, P=0.01), along with significantly higher D-dimer levels (6.41 vs. 1.16 mg/L, P<0.001). Notably, the admission SOFA was significantly lower in survivors than in non-survivors (2.0 vs. 8.5, P<0.001).

As shown in Table 3, among SA-CAP patients, the incidence of clinical complications was significantly lower in survivors compared to non-survivors, including ARDS (2.4% vs. 38.9%), septic shock (7.3% vs. 50.0%), AKI (7.3% vs. 55.6%), and myocardial injury (7.3% vs. 27.8%), with all differences being statistically significant (all P<0.05). While there was no significant difference in the use of antiviral therapy between the two groups, non-survivors more frequently received vancomycin and required ECMO and CRRT support. Regarding oxygen therapy needs, 53.7% of survivors required only nasal cannula oxygen or no oxygen supplementation, whereas 83.3% of non-survivors required invasive mechanical ventilation (P<0.001). Additionally, the median duration of mechanical ventilation (0 vs. 118.5 hours, P<0.001) and ICU length of stay (2 vs. 7.5 days, P=0.004) were significantly shorter in survivors than in non-survivors. Non-survivors also received significantly more transfusions of red blood cells, plasma, and platelets.

After Holm-Bonferroni correction for multiple comparisons, 15 variables were identified as statistically significant. FiO₂, PT, and total bilirubin were subsequently excluded due to insufficient clinical prognostic significance, while CRP, Lac, and SOFA were incorporated based on their validated prognostic value in previous studies. A total of 18 variables were finally included in the random forest analysis (Table S1). The random forest algorithm was used to determine the feature importance ranking for in-hospital mortality in patients with SA-CAP (Figure 2). The top 10 features were entered as independent variables into a multivariate logistic regression analysis (forward: conditional), with in-hospital mortality as the dependent variable.

Figure 2 Feature importance ranking from random forest analysis. The top 10 features identified were: APTT, AST, D-dimer, SOFA, PaO₂/FiO₂, Lac, PCT, hs-TnT, PLT, acute kidney injury. APTT, activated partial thromboplastin time; ARDS, acute respiratory distress syndrome; AST, aspartate aminotransferase; CRP, C-reactive protein; FiO₂, fraction of inspired oxygen; hs-TnT, high-sensitivity troponin T; IAV, influenza A virus; Lac, lactate; PaO₂, partial pressure of arterial oxygen; PCT, procalcitonin; PLT, platelet count; SA, Staphylococcus aureus; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SOFA, sequential organ failure assessment.

The multivariate analysis revealed that PLT <100×10⁹/L (OR =9.864; 95% CI: 1.237–18.656; P=0.007) and hs-TnT >14 pg/mL (OR =11.120; 95% CI: 1.644–15.199; P=0.005) were independently associated with an increased risk of in-hospital mortality in SA-CAP patients. Additionally, the development of concurrent AKI during the course of the disease (OR =11.944; 95% CI: 1.868–26.368; P=0.006) was also correlated with a higher in-hospital mortality in patients with SA-CAP (Figure 3).

Figure 3 Risk factors for in-hospital mortality in SA-CAP patients. The ORs on the x axis were from multivariate analyses. The right side of the reference line (OR >1) indicated a higher risk of mortality, whereas the left side of the reference line (OR <1) indicated a lower risk of mortality. hs-TnT >14 pg/mL (myocardial injury, doi: 10.1186/s13054-025-05249-2), PLT <100×10⁹/L (thrombocytopenia, in accordance with the Chinese Expert Consensus on the Diagnosis and Treatment of Adult Thrombocytopenia, doi: 10.3760/cma). CI, confidence interval; hs-TnT, high-sensitivity troponin T; OR, odds ratio; PLT, platelet count; SA-CAP, Staphylococcus aureus community-acquired pneumonia.

Influence of SA drug resistance and virulence profiles on viral co-infection and clinical outcomes

WGS was performed on SA and IAV isolates from 20 representative SA-CAP cases to address two key questions: (I) whether the profiles of core SA virulence and resistance genes are associated with synergistic pathogenesis with IAV, and (II) whether specific IAV genomic features correlate with enhanced SA pathogenicity or poorer patient outcomes.

As shown in Table 4, WGS was performed on 30 SA isolates (including sputum and blood isolates from the same patient) from 20 representative SA-CAP cases. The selection ensured representation from the IAV co-infection group, the SARS-CoV-2 co-infection group, and the SA monoinfection group, with both deceased and surviving patients included in each category. The final cohort comprised six patients with SA-IAV co-infection, six with SA-SARS-CoV-2 co-infection, and eight with SA monoinfection (no respiratory virus detected).

A total of 6 IAV isolates were submitted for testing. Viral detection failed in three cases (Cases 2, 3, and 5) due to insufficient viral load. The remaining three isolates were all typed as H1N1. Specifically, the H1N1 strains from Cases 1, 4, and 6 belonged to the subclade 6B.1A.5a.2a. Mutations were detected on gene segments 6, 7, 8, and 9; however, all occurred in intergenic regions, representing merely adaptive substitutions.

Multilocus sequence typing (MLST) of the 30 SA isolates identified six distinct sequence types (STs). ST22 was the predominant type (19/30, 63.3%), followed by ST59 (3/30, 10.0%), ST398 (4/30, 13.3%), ST630 (1/30, 3.3%), and ST88 (2/30, 6.7%). One isolate (3.3%) could not be matched to a known ST. Notably, among SA strains from patients with viral co-infection, ST22 accounted for 83.3% (10/12), suggesting a potential association between this ST and susceptibility to viral co-infection. Furthermore, sputum and blood isolates from the same patient consistently shared identical MLST profiles (Table 4).

The 30 SA strains shared 16 resistance genes, including kdpD, rpoB2, LmrS, arlS, norC, mepA, and tet(38), which are associated with efflux pump systems, cell wall modification, antibiotic modification, and regulatory functions. Susceptibility testing revealed a high frequency of macrolide resistance among the tested SA isolates. Furthermore, strains Sau-23, Sau-24 (ST88), Sau-25, Sau-26, and Sau-30 (ST59) were identified as community-acquired methicillin-resistant S. aureus (CA-MRSA) (Table 5).

A total of 57 virulence genes were common to all 30 strains, with the major detected genes listed in Table 6. Notably, ChuU was expressed exclusively in bloodstream isolates (2 out of 10 strains). The clfB gene was highly expressed in sputum isolates (17 out of 20 strains). In SA strains isolated from deceased patients, high expression of fnbB (19 out of 19 strains) and sak (17 out of 19 strains) was observed. Additionally, the virulence genes lukF-PV and lukS-PV were detected in all isolates from patients with viral co-infection (Table 6).

Discussion

This study identified prolonged PLT <100×10⁹/L, hs-TnT >14 pg/mL, the development of AKI as independent risk factors for in-hospital mortality in SA-CAP patients. The following discussion integrates immunopathological features to explore these risk factors in depth, and further analyzes their clinical implications and limitations.

As highlighted by Băetu et al. (12), the interaction between the immune and coagulation systems is a key feature of severe inflammatory conditions, and thrombocytopenia often indicates immune-mediated platelet consumption, endothelial activation, and microthrombosis. In the context of SA-CAP, the excessive inflammatory response triggered by pathogenic bacterial invasion leads to platelet activation via pro-inflammatory cytokines such as IL-6 and tumor necrosis factor-α (TNF-α), which not only promotes platelet aggregation and consumption but also exacerbates endothelial injury (13). This process further impairs microcirculatory perfusion, contributing to multi-organ dysfunction and increasing the risk of in-hospital mortality. Additionally, thrombocytopenia may compromise the host’s immune defense by reducing platelet-mediated immune cell recruitment and pathogen clearance, creating a vicious cycle of immune dysregulation and disease progression (14).

Hs-TnT >14 pg/mL, a sensitive marker of myocardial injury, is another crucial risk factor identified in this study. Previous studies have linked elevated troponin levels to poor prognosis in infected patients (15,16), and a meta-analysis of sepsis and severe infection cohorts confirmed that hs-TnT >14 pg/mL is an independent risk factor for infection-related myocardial injury and all-cause mortality (15). In SA-CAP patients, myocardial injury is not typically caused by primary cardiac pathology but rather by the systemic immunopathological response to severe pneumonia. As demonstrated by Lnu et al. (17), elevated cardiac troponin levels in severe infectious conditions are associated with increased 28-day mortality, reflecting the impact of systemic inflammation on myocardial function. The immunopathological mechanisms underlying hs-TnT elevation in SA-CAP include microcirculatory dysfunction caused by endothelial activation and microthrombosis, direct myocardial cell injury by pro-inflammatory cytokines, and myocardial hypoxia due to hypoxemia and increased cardiac demand (18).

The immunopathological basis of sepsis-associated AKI (SA-AKI) in the context of SA-CAP has been extensively reviewed (19,20), who highlighted that SA-AKI was driven by a combination of inflammatory cell infiltration, metabolic reprogramming, and various forms of cell death including apoptosis, necroptosis, and pyroptosis in renal tubular cells. In SA-CAP, the excessive release of pro-inflammatory cytokines and damage-associated molecular patterns (DAMPs) from the infected lungs triggers a systemic inflammatory response that targets renal endothelial and tubular cells, leading to impaired renal perfusion and function. Additionally, the coagulation abnormalities associated with SA-CAP, such as microthrombosis, further exacerbate renal injury by reducing microcirculatory flow (21). Recent advances in SA-AKI research have also identified the role of immune cell subsets such as neutrophils and macrophages in mediating renal injury through the release of reactive oxygen species (ROS) and proteases (22). The development of AKI in SA-CAP patients not only indicates severe immune dysregulation but also contributes to further organ dysfunction through the accumulation of uremic toxins, which perpetuate the inflammatory response and increase the risk of mortality.

While multivariate analysis did not identify IAV co-infection as an independent risk factor for in-hospital mortality in SA-CAP patients, comparative data revealed that IAV co-infection was associated with a significantly elevated risk of in-hospital death. This suggests that viral-bacterial co-infection may worsen outcomes through complex molecular interactions, while also raising the possibility that SA strains prone to co-infecting with IAV may carry inherent genetic traits affecting prognosis.

Genetic markers associated with fatal pneumonia caused by SA and influenza virus co-infection have been reported, including PVL, mecA, and the spa type t008 (23,24). Furthermore, CA-MRSA clones, including USA300 and ST398, have been linked to increased mortality in young, otherwise healthy influenza patients (25). Despite these observations, systematic genomic analyses of pathogens in the context of SA-IAV co-infection are still scarce, significantly limiting the development of precise anti-infective strategies.

This study found that all genetic mutations in IAV were located in intergenic regions and manifested only as non-coding modifications. Consequently, the specific synergistic mechanisms of IAV in SA co-infection, such as virulence gene regulation or differences in immune evasion pathways, remain unelucidated. Based on this, we propose the hypothesis that in patients with SA-IAV co-infection, clinical outcomes may depend more on the pathogenic characteristics of SA than on IAV genetic variation. Therefore, this study focused on the core genomic features of SA strains—namely the distribution of virulence and resistance genes—and investigated their potential association with disease progression and treatment outcomes in pneumonia patients.

Epidemiological data indicate that ST188, ST7, ST22, ST5, and ST398 were the predominant MSSA lineages in China from 2011 to 2020, with ST22 also prevalent in northwestern regions and capable of evolving into MRSA via SCCmec acquisition (26). In contrast, the ST88 CA-MRSA lineage remains largely restricted to Africa and Asia (27). Consistent with previous epidemiological data in China that ST22 is the predominant MSSA lineage, our study further found that ST22 accounted for 83.3% of SA strains in viral co-infection cases, suggesting a novel association between ST22 and viral co-infection susceptibility that may not be reported previously.

Notably, in our cohort, the chuU gene, previously reported mainly in E. coli and potentially involved in heme uptake (28), was exclusively detected in bloodstream isolates and only in ST22 strains, though its low detection rate (6.7%) necessitates cautious of false positives due to primer nonspecificity or sample contamination. Conversely, clfB was highly expressed in sputum isolates, yet its role in bloodstream invasion remains unclear.

A key finding was the prevalent detection of PVL toxin genes (lukF-PV-lukS-PV) in viral co-infection cases. PVL exacerbates lung injury by lysing neutrophils, leading to uncontrolled protease release and necrotizing pneumonia, as documented in severe influenza and SA co-infections. This aligns with the observed high mortality in SA-IAV co-infected patients. Furthermore, virulence genes such as sak (immune evasion), fnbB (adhesion/invasion), cap8, and esxC were highly expressed in non-survivors, suggesting their collective role in enhancing bacterial dissemination and immune evasion.

In traditional clinical practice, bacterial culture and susceptibility testing have primarily focused on monitoring resistance to guide antibiotic selection. In this study, the clinical mortality rate among CA-MRSA patients was 100%. However, the critical role of bacterial virulence in determining disease outcomes cannot be overlooked. Patients infected with community-acquired methicillin-susceptible Staphylococcus aureus (CA-MSSA), while not exhibiting multidrug resistance, progressed to severe pneumonia and ultimately died due to high expression of virulence genes (e.g., sak, fnbB, cap8). This indicates that beyond antibiotic resistance, the expression level of bacterial virulence factors may be an independent determinant of infection severity and prognosis.

Limitations

This study has several limitations. First, the modest sample size and retrospective design may introduce bias, limit the control of confounding factors. Second, the WGS analysis was based on a limited number of isolates (n=30), which may not fully capture the local genomic epidemiology of SA.

Future multicenter prospective studies with larger cohorts are warranted to validate our findings. Additionally, clinical features combined with multi-omics approaches in subsequent research will be crucial to elucidate the mechanistic underpinnings of viral-bacterial co-infection and to inform targeted therapeutic strategies.

Conclusions

This study reported that PLT <100×10⁹/L, hs-TnT >14 pg/mL, and AKI were independent risk factors for in-hospital mortality in SA-CAP patients, reflecting the profound immunopathological disturbances that drive poor outcomes in these patients. The IAV strains identified here carried only adaptive mutations, indicating that clinical deterioration and poor prognosis in CAP are primarily attributable to SA rather than to IAV. Increased expression of these specific SA virulence genes (such as lukF-PV-lukS-PV, sak, fnbB, and cap8) is directly associated with worse patient outcomes, and SA exhibits heightened virulence in the context of IAV co-infection.

Integrating virulence gene profiling with conventional resistance testing could enable earlier identification of high-risk patients and support more precise anti-infective strategies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2791/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2791/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2791/prf

Funding: This work was supported by the National Natural Science Foundation of China (grant No. 82570078) and the Province Natural Science Foundation of Jiangsu (grant No. BK20241806).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2791/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All protocols in this study have been approved by the ethics committee of The Fourth Affiliated Hospital of Soochow University (No. 241294). Informed consent was obtained from all patients for being included in the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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