Introduction

Acute type A aortic dissection (ATAAD) is a life-threatening condition requiring emergent ascending aortic replacement under cardiopulmonary bypass (CPB).1 While CPB ensures systemic perfusion and mitigates intraoperative ischemia, it also induces systemic inflammation, contributing to postoperative pulmonary dysfunction. Approximately 50% of ATAAD patients develop hypoxemia due to CPB-mediated alveolar-capillary damage and neutrophil activation,2 underscoring the need for lung-protective strategies during surgery.

In post-cardiac surgery patients, acute lung injury (ALI) and its severe progression to acute respiratory distress syndrome (ARDS) occur in ~10% of CPB cases,3,4 increasing 30-day mortality by 3- to 5-fold compared to uncomplicated recoveries.5 Current management relies primarily on supportive measures, including lung-protective ventilation, while evidence for pharmacologic interventions, such as neuromuscular blockade or glucocorticoids, remains limited.6

The hallmark of ALI/ARDS is inflammatory disruption of the alveolar-capillary barrier, leading to increased vascular permeability and pulmonary edema.7 The pathophysiology involves dysregulated inflammation, coagulopathy, and neutrophil activation.8 Neutrophil elastase (NE), a key serine protease comprising ~80% of granulocyte proteolytic activity, drives disease progression by degrading extracellular matrix, increasing vascular permeability, and promoting bronchoconstriction.9 NE activity serves as both a pathological mediator and biomarker in ALI severity.10,11 Sivelestat sodium, a selective neutrophil elastase (NE) inhibitor, has been shown in preclinical studies to inhibit NE, suppress excessive inflammatory response and exert substantial significant effects against ALI.12,13 Subsequent clinical trials in Japan demonstrated sivelestat’s therapeutic benefits, including improved pulmonary function, reduced intensive care unit (ICU) stays, and shorter mechanical ventilation duration.14,15 However, its efficacy in post-aortic dissection ARDS patients remains unclear.16 This specific clinical context is characterized by a unique and profound systemic inflammatory response associated with ischemia–reperfusion injury, coagulopathy, and malperfusion, which may influence treatment response. In addition, the mechanisms underlying the potential organ-protective effects of sivelestat, as well as the relationship between NE activity and clinical outcomes, are not fully understood.

To address these gaps, we conducted a pilot physiological trial to systematically evaluate the therapeutic potential of sivelestat in post-aortic dissection ARDS and to explore its molecular mechanisms using transcriptomic profiling (RNA sequencing).17

Materials and Methods Study Design and Population

In this investigator-initiated pilot RCT, patients were recruited from the Department of Cardiovascular Surgery ICU, Fujian Union Hospital, between June 2024 and March 2025. The study protocol was approved by the ethics review board of Fujian Union Hospital (No. 2021YF037-02). Written informed consent was obtained from the patients’ legally authorized representatives prior to enrollment. The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2300074438) before enrollment commenced.

Eligible participants were adult patients (≥18 years) undergoing aortic dissection surgery who subsequently developed ARDS (per Berlin criteria)18 accompanied by systemic inflammatory response syndrome (SIRS).19 Key exclusion criteria comprised: (1) evidence of cardiogenic pulmonary edema (pulmonary wedge pressure ≥18 mmHg or clinical signs of elevated left atrial pressure); (2) recent glucocorticoid therapy (within 4 weeks); (3) history of chemotherapy; (4) significant hepatic impairment (AST/ALT >2× upper limit of normal) or chronic liver disease; (5) advanced chronic respiratory disorders (including chronic obstructive pulmonary disease or interstitial lung disease); (6) hematologic abnormalities (leukocytosis or thrombocytopenia within preceding 12 months); (7) HIV/AIDS; (8) end-stage renal disease requiring dialysis; (9) known sivelestat sodium hypersensitivity; and (10) current enrollment in competing clinical trials. Participant flow through screening, exclusion, and randomization is shown in Figure 1.

Figure 1 CONSORT flow diagram of patient enrolment, randomization, follow-up, and analysis. Consecutive screening, eligibility assessment, randomization, and allocation. Of 25 patients screened after aortic dissection repair, 15 were excluded for prespecified reasons (hemodialysis, concurrent trial enrollment, cardiogenic pulmonary edema, or recent glucocorticoid therapy). Ten patients were randomized 1:1 to sivelestat (n=5) or placebo (n=5).

Randomization, Blinding and Interventions

Eligible participants were randomized 1:1 to either the intervention or control group. An independent statistician, uninvolved in study conduct, generated the randomization sequence using a computerized random number generator (SAS v8.1). Stratified randomization based on EuroSCORE II categories (≤5 vs >5) was applied to ensure balanced allocation across surgical risk levels.20 Allocation concealment was maintained using sequentially numbered, opaque, sealed envelopes, which were opened in order of enrollment after eligibility confirmation. Participants, investigators, data collectors, and outcome assessors remained blinded to treatment allocation throughout the study.

Patients received either a continuous intravenous infusion of sivelestat sodium at a dose of 0.2 mg/kg/h for 24 hours per day over five consecutive days, or a matching placebo (0.9% sodium chloride) administered under identical conditions. All other treatments were provided at the discretion of the attending physicians, in accordance with the 2023 European Society of Cardiology (ESC) Guidelines for the Management of Thoracic Aortic Disease.21

Trial Endpoints

The primary endpoint was the arterial oxygen partial pressure to fractional inspired oxygen ratio (PaO2/FiO2) on Day 5 after randomization. Secondary clinical endpoints included daily Sequential Organ Failure Assessment (SOFA) scores, requirement for invasive mechanical ventilation, duration of mechanical ventilation, ICU length of stay. Tertiary laboratory endpoints, assessed on Days 1, 3, and 5, comprised interleukin-6 (IL-6), procalcitonin (PCT), C-reactive protein (CRP), serum ionized calcium (Ca2⁺), cardiac troponin, and plasma D-dimer levels.

Exploratory Translational Endpoints

To explore intergroup transcriptomic differences and identify potential therapeutic targets, peripheral blood samples were collected on Day 5 for RNA sequencing (RNA-seq). Total RNA was extracted, and sequencing libraries were prepared and processed using the BGI T7 platform (150 bp paired-end; Fuzhou Frontier Gene Co., Ltd).

Sample Size Estimation

Prior studies reported a Day-5 PaO2/FiO2 of 155.3 ± 42.8 mmHg in controls.22,23 We assumed a 50% relative increase with sivelestat (target mean about 233 mmHg), corresponding to a mean difference of 77.7 mmHg and an effect size (Cohen’s d) of 1.8, assuming a common SD of 42.8 mmHg. With a two-sided α = 0.05 and a two-sample t test with 1:1 allocation, a total of 10 patients (5 per group) provides power close to 75-80% (PASS v11.0; NCSS, Kaysville, UT, USA). Given the pilot nature, the final sample size also reflected feasibility considerations. The study was designed to provide preliminary estimates of variance and effect size to inform future trials.

Statistical Analysis

All analyses followed the intention-to-treat principle. Continuous variables are summarized as mean ± standard deviation (SD) for approximately normally distributed data and as median (IQR) for skewed data; normality was assessed using the Shapiro–Wilk test. Categorical variables are presented as n (%). Between-group comparisons were performed using Student’s t test or the Mann–Whitney U-test for continuous variables, and the χ2-test or Fisher’s exact test for categorical variables, as appropriate. Key continuous variables were additionally analyzed using the Mann–Whitney U-test and reported as median (IQR) to complement parametric analyses. All tests were two-sided, with p < 0.05 considered statistically significant. Analyses were conducted using SPSS 25.0 and R version 4.2.3. Given the pilot nature and small sample size (n=10), p values should be interpreted with caution, and the study is primarily intended to provide preliminary estimates of effect sizes and variability to inform future larger trials.

RNA-Seq Analysis Differential expression: Differential gene expression was assessed with the DESeq2 package in R. Transcripts were considered differentially expressed if they met a Benjamini–Hochberg false discovery rate (FDR)<0.05 and an absolute log2 fold change (|log2FC|)>1. Genes with log2FC>1 were classified as up-regulated and those with log2FC<−1 as down-regulated. Volcano plot and heatmap visualization: Differentially expressed genes (DEGs) defined by DESeq2 (|log2FC|>1 and FDR<0.05) were visualized with volcano plots, highlighting significantly up-regulated genes in red and down-regulated genes in blue. Hierarchical clustering heatmaps were generated with pheatmap using variance-stabilized expression values, Euclidean distance, and complete linkage for clustering of both genes and samples. Functional enrichment: Functional enrichment of DEGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations with clusterProfiler. GO terms were summarized into biological process (BP), cellular component (CC), and molecular function (MF) categories. Differential genes were mapped to GO terms curated by the Gene Ontology Consortium (https://www.geneontology.org), and enrichment was evaluated by hypergeometric testing against a genome-wide background with Benjamini–Hochberg correction. GO terms and KEGG pathways with p < 0.05 (adjusted) were considered significantly enriched. The top 10 GO terms and top 10 KEGG pathways (or all if fewer) were ranked by ascending p values, and the number of DEGs mapped to each pathway was quantified to indicate enrichment magnitude. Protein–protein interaction (PPI) network: PPI networks were constructed using the STRING database (v11.5). For annotated species, interactions were retrieved directly from STRING. For unannotated species, putative orthologs were identified by BLASTX (E-value<1×10−5) against reference species, and networks were inferred from conserved interactions.

Additional methodological details and extended transcriptomic analyses are provided in the Supplementary Methods.

Results Participants and Baseline Characteristics

During the study period, 25 patients were screened, and 10 eligible patients were enrolled and randomly assigned in a 1:1 ratio to the sivelestat group (n=5) or the control group (n=5). All participants completed the 30-day follow-up. Baseline characteristics were generally well balanced between the two groups, including demographics, comorbidities, laboratory parameters, and illness severity (PaO2/FiO2 ratio and SOFA score), with no significant between-group differences (Table 1), except for a nominal difference in serum calcium levels (p=0.049). Continuous variables are presented as mean ± SD in the main analysis. Normality assessments of key variables are provided in Supplementary Table S2, and complementary non-parametric analyses are presented in Supplementary Table S3.

Table 1 Baseline Characteristics of Patients in the Control and Sivelestat Groups

Primary and Secondary Endpoints

By Day 5, patients treated with sivelestat demonstrated marked physiological and clinical advantages over the control group. The oxygenation index (PaO2/FiO2) was 317.2 ± 115.8 mmHg in the sivelestat group and 168.6 ± 79.4 mmHg in the control group (p=0.046), indicating significantly improved oxygenation with sivelestat therapy. The SOFA score was 5.80 ± 1.30 in the sivelestat group compared with 9.00 ± 1.87 in the control group (p=0.014), reflecting faster recovery of organ function. Regarding inflammation, CRP levels were markedly lower in the sivelestat group, reaching 71.55 ± 41.31 mg/L on Day 3 and 59.76 ± 23.06 mg/L on Day 5, compared with 152.95 ± 21.83 mg/L and 110.45 ± 19.10 mg/L in the control group, respectively (both p=0.005), suggesting more effective suppression of systemic inflammation (Table 2).

Table 2 Key Laboratory and Clinical Results Between the Control and Sivelestat Groups

In this mechanically ventilated patient population, ICU length of stay was substantially shorter with sivelestat, averaging 6.0 ± 2.5 days versus 13.0 ± 3.6 days in the control group (p=0.009), indicating accelerated overall recovery and reduced intensive care utilization. The duration of invasive mechanical ventilation was also shorter with sivelestat (4.0 ± 3.8 vs 9.3 ± 5.9 days) but was not statistically significant (p=0.135; Table 3). Other secondary and laboratory endpoints, including IL-6, PCT, troponin, D-dimer, and serum calcium dynamics, are summarized in Supplementary Table S1.

Table 3 Clinical Outcomes of Patients in the Control and Sivelestat Groups

RNA-Seq Results

RNA-seq analysis identified 518 differentially expressed genes (DEGs) between the sivelestat and control groups, including 268 upregulated and 250 downregulated genes. The overall transcriptional landscape, illustrated by the bar chart, volcano plot, and hierarchical clustering heatmap (Figure 2A–C), revealed a clear separation between the two groups, indicating a distinct gene-expression profile following sivelestat treatment.

Figure 2 Transcriptomic profiling of differentially expressed genes (DEGs) between the sivelestat and control groups. (A) Numbers of upregulated and downregulated DEGs identified by RNA-seq analysis. (B) Volcano plot illustrating the overall distribution of DEGs based on log2 fold change and adjusted p values, with red and blue dots indicating upregulated and downregulated genes, respectively. (C) Hierarchical clustering heatmap of DEGs showing distinct expression patterns between the sivelestat and control groups, where red represents upregulation and blue represents downregulation.

GO biological process enrichment analysis demonstrated that DEGs were predominantly associated with immune and inflammatory responses, including leukocyte activation, humoral immune response, and complement activation (Figure 3A).Consistently, KEGG pathway enrichment highlighted significant involvement in cytokine–cytokine receptor interaction, complement and coagulation cascades, and neutrophil extracellular trap (NET) formation (Figure 3B).The KEGG pathway–gene interaction network further revealed that these enriched pathways were interconnected through several shared genes—particularly MPO, CTSG, ELANE, and DEFA3—suggesting their central roles in regulating immune and inflammatory signaling (Figure 3C). In the protein–protein interaction (PPI) network constructed using the STRING database, six hub genes—BPI, ELANE, CEBPE, MPO, CTSG, and DEFA3— showed the highest connectivity (Figure 3D). These hub genes are closely related to neutrophil-mediated proteolysis and antimicrobial defense, providing mechanistic insight into sivelestat’s action as a neutrophil elastase inhibitor and supporting its potential therapeutic role in modulating excessive inflammatory activation.

Figure 3 Functional enrichment and protein–protein interaction (PPI) network analysis of differentially expressed genes (DEGs) between the sivelestat and control groups. (A) GO biological process enrichment analysis showing that DEGs were predominantly involved in immune-related pathways, including leukocyte activation, humoral immune response, and complement activation. (B) KEGG pathway enrichment revealing significant enrichment in cytokine–cytokine receptor interaction, complement and coagulation cascades, and neutrophil extracellular trap formation. (C) KEGG pathway–gene interaction network illustrating the interconnection of major enriched pathways through shared genes such as MPO, CTSG, ELANE, and DEFA3, indicating their central roles in inflammatory and immune responses. (D) Protein–protein interaction (PPI) network constructed using the STRING database, highlighting six hub genes (BPI, ELANE, CEBPE, MPO, CTSG, and DEFA3) with the highest connectivity, which are primarily associated with neutrophil-mediated proteolysis and antimicrobial defense, consistent with the proposed mechanism of sivelestat.

To ensure reproducibility and comprehensive interpretation, additional analyses including the GO bubble plot and hierarchical DAG (Supplementary Figures S1 and S2), KEGG bubble plot (Supplementary Figure S3), and representative GSEA enrichment curves (Supplementary Figure S4) are provided in the Supplementary Materials.

Discussion

In this randomized physiological pilot study of aortic dissection (AD) surgery–associated ARDS, early inhibition of neutrophil elastase (NE) with sivelestat was associated with significantly improved oxygenation (as reflected by higher PaO2/FiO2), reduced organ dysfunction (lower SOFA scores), and attenuated systemic inflammation (lower CRP levels), as well as a shorter ICU stay. By Day 5, these improvements were evident in the sivelestat group compared with controls. Although the difference in the duration of invasive mechanical ventilation between groups did not reach statistical significance, a shortening trend was observed in the sivelestat group. Collectively, these findings provide preliminary clinical evidence that NE inhibition may facilitate postoperative recovery and reduce intensive care resource utilization.

The observed physiological benefits are mechanistically plausible in the perioperative context. During AD repair, cardiopulmonary bypass–induced ischemia–reperfusion injury triggers robust leukocyte activation characterized by neutrophil chemotaxis, endothelial adhesion, and tissue infiltration.24 Activated neutrophils release cytotoxic mediators—including reactive oxygen species, proteolytic enzymes (eg, elastase), and proinflammatory cytokines (eg, TNF-α, IL-1β, IL-6)—that collectively increase vascular permeability, damage parenchymal tissue, and amplify inflammatory cell recruitment.25 These mechanisms are well-established drivers of postoperative acute lung injury, consistent with the inflammatory profile observed in our cohort. By selectively inhibiting NE, sivelestat may mitigate protease-mediated epithelial and endothelial injury, suppress secondary inflammatory amplification, and preserve alveolar–capillary membrane integrity—mechanistically consistent with the observed improvements in oxygenation, organ function, and inflammation.

Transcriptomic analysis provided additional mechanistic support. RNA-seq identified 518 differentially expressed genes distinguishing the sivelestat and control groups, with enrichment predominantly in immune and inflammatory programs—particularly leukocyte activation, humoral immune response, and complement activation—and KEGG pathways such as cytokine–cytokine receptor interaction, complement and coagulation cascades, and neutrophil extracellular trap (NET) formation. Network integration revealed extensive crosstalk among these pathways mediated by shared genes (eg, MPO, CTSG, ELANE, DEFA3), while STRING-based protein–protein interaction analysis highlighted six hub genes—BPI, ELANE, CEBPE, MPO, CTSG, and DEFA3—with the highest connectivity. Functionally, these hub genes cluster around neutrophil granule biology, proteolysis, and antimicrobial defense, cohering with sivelestat’s pharmacological role as an NE inhibitor and suggesting that suppression of neutrophil protease–driven amplification loops constitutes a key axis of lung protection.

These data are concordant with prior work. Sivelestat has been reported to reduce neutrophil accumulation, attenuate oxidative stress, and preserve the alveolar–capillary barrier.26 Our findings of improved oxygenation are consistent with previous clinical studies,15,27,28 Prior reports have also described reductions in leukocytosis/neutrophilia and decreases in PCT with sivelestat in ARDS, in line with its anti-inflammatory pharmacology. Additionally, reductions in serum creatinine observed in parallel with respiratory improvement have been reported elsewhere, potentially reflecting better organ protection via improved oxygen delivery and mitigation of systemic hypoxia.29 To further elucidate the underlying mechanisms, transcriptomic profiling of peripheral blood mononuclear cells revealed signaling features associated with neutrophil-driven inflammation, cell-cycle regulation, ubiquitin–proteasome dynamics, and extracellular-matrix remodeling. These molecular characteristics are highly consistent with the established pathobiological landscape of ARDS, characterized by dysregulated neutrophilic inflammation, impaired ribosomal biogenesis, and aberrant RNA splicing.30 Among the identified hub genes, each exhibits clear biological plausibility. BPI encodes a lipid-binding antimicrobial protein with bactericidal and endotoxin-neutralizing activity, facilitating the clearance of Gram-negative bacteria.31ELANE encodes NE, a protease linked to ARDS progression and proposed as a biomarker.32CEBPE regulates granulocyte differentiation,33 whereas MPO contributes to oxidative stress via reactive oxygen species generation and has emerging therapeutic relevance through the MPO/μ-calpain/β-catenin signaling axis.34CTSG and DEFA3, both implicated in extracellular-matrix degradation and antimicrobial defense, are additionally involved in neutrophil-mediated tissue injury and repair.35 Together, these findings recapitulate key molecular features of ARDS and provide a testable framework for future pharmacodynamic validation of sivelestat’s transcriptomic effects. Nonetheless, the transcriptomic results presented here are hypothesis-generating rather than definitive mechanistic evidence. Further targeted functional validation, including assays of neutrophil elastase activity and NETosis-related markers, will be required in future studies.

This study is subject to several limitations. The single-center design and small sample size (n=10) limit statistical precision and generalizability and increase the risk of type II error, particularly for outcomes such as duration of mechanical ventilation. A nominal baseline imbalance in serum calcium further underscores the need for confirmation in larger, more balanced cohorts. Transcriptomic analyses were conducted on peripheral blood and therefore primarily reflect systemic inflammatory responses, which may not fully capture the local alveolar microenvironment. Accordingly, these findings remain observational and require targeted functional validation—such as assessment of neutrophil elastase activity, NETosis, and complement pathways—in both in vitro and in vivo settings. Future studies incorporating bronchoalveolar lavage fluid (BALF)-based transcriptomic or proteomic profiling may provide more direct characterization of lung-specific processes. Taken together, these limitations highlight the exploratory nature of this pilot study. The findings should be interpreted with caution and are best regarded as preliminary, providing estimates of effect size and variability to inform the design of future adequately powered multicenter trials.

In summary, in AD surgery–associated ARDS, sivelestat was associated by Day 5 with improved oxygenation, reduced systemic inflammation, lower organ dysfunction, and shorter ICU stay, with convergent transcriptomics implicating suppression of neutrophil protease–centered inflammatory pathways. These pilot data support the mechanistic rationale for NE inhibition in postoperative ARDS and justify larger multicenter, biomarker-integrated trials to confirm benefit, define responsive endotypes, and evaluate hub genes (BPI, ELANE, CEBPE, MPO, CTSG, DEFA3) as pharmacodynamic markers or therapeutic targets.

Conclusion

In this pilot hypothesis-generating study of aortic dissection-associated ARDS, early sivelestat was associated with preliminary signals of improved oxygenation, reduced systemic inflammation, and shorter ICU stay. Exploratory transcriptomics identified six hub genes (BPI, ELANE, CEBPE, MPO, CTSG, DEFA3) implicating neutrophil protease-centered pathways. These findings support the need for larger, biomarker-integrated multicenter trials to confirm efficacy and define responsive subgroups.

Data Sharing Statement

The datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author upon reasonable request. Supplementary Material related to this article is available at Supplementary Materials.

Ethics

This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Ethics Review Board of Fujian Medical University Union Hospital (Approval No. 2021YF037-02). Due to the critical condition of the enrolled patients, including severe ARDS requiring invasive mechanical ventilation and intensive care support, the patients lacked the capacity to provide informed consent at the time of enrollment. Therefore, written informed consent for study participation was obtained from the patients’ legal guardians prior to enrollment. The trial was prospectively registered with the Chinese Clinical Trial Registry (ChiCTR2300074438). The study was reported in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines.

Acknowledgments

We thank all patients and their families for their participation in this study. We are grateful to the clinical teams of the Department of Cardiovascular Surgery and the Intensive Care Unit at Fujian Medical University Union Hospital for their support in patient recruitment, perioperative management, and follow-up. We also acknowledge the technical assistance provided for laboratory measurements and RNA sequencing.

Author Contributions

Hanyu Wang: Conceptualization, Methodology, Data curation, Formal analysis, Writing – original draft. Dekai Kong: Investigation, Data curation, Writing – review & editing. Lingfeng Li: Investigation, Formal analysis, Writing – review & editing. Jiaxin Zhang: Investigation, Data curation, Writing – review & editing. Zairong Lin: Investigation, Validation, Writing – review & editing. Lingli Yu: Investigation, Data curation, Writing – review & editing. Mirong Tang: Resources, Data curation, Writing – review & editing. Minxia Xie: Validation, Formal analysis, Writing – review & editing. Jing Li: Investigation, Formal analysis, Writing – review & editing. Sheng Chen: Conceptualization, Supervision, Project administration, Writing – review & editing. All authors have approved the final version of the paper, agreed to its submission, and are accountable for all aspects of the work.

Funding

No funding was received for this study.

Disclosure

The authors declare that they have no competing interests for this study.

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