Identification of Oxygenation Impairment–Associated Gene Networks in ARDS Through Integrated mRNA and miRNA Analysis

Identification of Oxygenation Impairment–Associated Gene Networks in ARDS Through Integrated mRNA and miRNA Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Oxygenation Impairment–Associated Gene Networks in ARDS Through Integrated mRNA and miRNA Analysis Yumi Mitsuyama, Hisatake Matsumoto, Takeshi Ebihara, Daisuke Okuzaki, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8776914/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Acute respiratory distress syndrome (ARDS) is associated with high mortality and complex pathophysiology, yet molecularly targeted therapies remain undeveloped. In particular, the microRNA (miRNA)–mRNA regulatory network underlying ARDS are poorly understood. This study aimed to elucidate the miRNA–mRNA interactions associated with the pathophysiology of ARDS. Methods mRNA-Seq and miRNA-Seq were performed in 34 patients with ARDS and healthy controls. Gene and miRNA co-expression modules were constructed using Weighted Gene Co-expression Network Analysis. miRNA–mRNA regulatory relationships were inferred through an integrated analysis of predicted and experimentally validated miRNA targets. Molecular signatures were quantified via single-sample gene set enrichment analysis, and module structure preservation was evaluated in an external pneumonia cohort. Results A key mRNA co-expression module was identified that exhibited the strongest negative correlation with the P/F ratio, along with a negatively correlated miRNA co-expression module. The miRNA module, centered on miR-361-5p and miR-186-5p, formed a regulatory network broadly controlling gene clusters involved in ubiquitin ligase activity and cellular stress response pathways. This network demonstrated a strong association with the P/F ratio and showed extremely high structural preservation in the external pneumonia cohort. Conclusion A miRNA–mRNA regulatory network linked to impaired oxygenation in patients with ARDS has been identified. The network highlights miRNAs as potential key regulators of disease progression and suggests their utility as biomarkers of disease severity and prospective therapeutic targets. ARDS microRNA mRNA transcriptome regulatory network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Acute respiratory distress syndrome (ARDS) is a life-threatening condition characterized by severe hypoxemia and diffuse lung injury and is associated with high mortality [ 1 ]. Current management is largely supportive, as no molecular targeted therapies that directly address the underlying pathophysiology have been established. The development of effective treatments has been hindered by the heterogeneity of ARDS pathophysiology, which encompasses diverse mechanisms—including inflammation, immune dysregulation, and hypercoagulability—resulting in significant variations in clinical outcomes [ 2 , 3 ]. A detailed understanding of this heterogeneity at the molecular level is essential for advancing precision medicine approaches in ARDS. Recent omics-based studies, including transcriptomics and proteomics, have delineated molecular subtypes of ARDS, such as pro-inflammatory and immunosuppressive phenotypes [ 4 ]. However, many of these studies have relied predominantly on differential expression analyses of single-layer omics data, providing limited insights into the regulatory networks that orchestrate disease pathophysiology. MicroRNAs (miRNAs) regulate gene expression post-transcriptionally by simultaneously targeting multiple mRNAs and play central roles in pathways involved in inflammation, immune responses, and cellular stress [ 5 ]. As higher-level regulatory elements that coordinately govern changes in mRNA and protein expression, mRNAs may be critical to understanding the molecular heterogeneity of ARDS [ 6 , 7 ]. Nevertheless, integrated analyses of miRNA–mRNA interactions in ARDS remain scarce, with limited validation of network structural features or reproducibility in external cohorts. Although emerging evidence indicates that miRNA and mRNA interactions contribute to immune dysregulation in ARDS, a comprehensive network-level understanding is still lacking [ 8 ]. This study aimed to elucidate the miRNA–mRNA regulatory network underlying the molecular pathophysiology of ARDS. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct co-expression networks for both miRNAs and mRNA from peripheral blood samples of patients with ARDS. Integration of these networks enabled the identification of regulatory modules and molecular signatures associated with disease severity. Methods Study design and participants This prospective, single-center observational study was conducted at The University of Osaka Hospital between July 2020 and February 2021. Patients aged ≥ 18 years who were diagnosed with ARDS according to the Berlin definition were enrolled within 24 h of diagnosis [ 9 ]. Clinical data, including age, comorbidities, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Sequential Organ Failure Assessment (SOFA) score, and clinical outcomes, were extracted from electronic medical records [ 10 , 11 ]. Patients aged < 18 years, who declined active treatment, or who refused to participate in the study were excluded. Healthy volunteers without acute or chronic inflammatory disease were recruited as controls. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Review Committee of Osaka University Hospital (approval no.: 885). Written informed consent was obtained from all participants or their legally authorized representatives. Sample collection and RNA isolation Peripheral whole blood samples were collected within 24 hours of ARDS diagnosis using PAXgene™ Blood RNA Tubes (BD Biosciences, San Jose, CA). Total RNA was extracted using a PAXgene Blood RNA Kit (BD Biosciences) following the manufacturer’s instructions. The same procedures were applied for blood collection and RNA isolation in healthy controls. Transcriptome sequencing Both mRNA sequencing (mRNA-Seq) and miRNA sequencing (miRNA-Seq) were performed for all samples. For mRNA-seq, libraries were prepared using the SMART-Seq HT kit (Takara Bio, Shiga, Japan) and sequenced using the DNBSEQ-G400RS platform. For miRNA-Seq, small RNA libraries were constructed using the NEBNext Small RNA Library Prep Set for Illumina (New England Biolabs) and sequenced on the NovaSeq 6000 platform. Detailed information regarding RNA quality control, library preparation, sequencing depth, read alignment, reference genomes, and expression quantification methods is provided in Supplementary Methods. Statistical analysis Raw read count data for mRNAs and miRNAs were filtered to remove low-expression features, normalized using the trimmed mean of M values (TMM) method, and log-transformed. Detailed descriptions of preprocessing steps and filtering thresholds are provided in Supplementary Methods. Differential expression analysis was performed to identify differentially expressed genes (DEGs) and miRNAs between patients with ARDS and healthy controls. Next, WGCNA was performed exclusively on the samples from patient with ARDS to construct co-expression modules [ 12 ]. Module eigengenes (MEs), representing the first principal components of each module, were correlated with clinical traits to identify mRNA modules with clinical relevance. Within these modules, hub genes and miRNAs were defined based on high module eigengene-based connectivity (kME), reflecting strong intramodular connectivity and central regulatory roles. Correlations between the MEs of mRNA and miRNA modules were subsequently evaluated to identify key interacting module pairs. miRNA–mRNA regulatory networks were then inferred by integrating computationally predicted miRNA targets from TargetScan with experimentally validated targets from miRTarBase, with a focus on hub miRNAs and mRNAs within the identified modules [ 13 , 14 ]. The activity of molecular signatures derived from the integrated miRNA–mRNA network was quantified using single-sample gene set enrichment analysis (ssGSEA) and correlated with clinical outcomes [ 15 ]. The RNA sequencing data analyzed in this study were generated within the same laboratory and derived from the same underlying biological samples as those previously deposited in the Gene Expression Omnibus (GEO) database under accession numbers GSE243217 and GSE243218. No additional or independent sequencing experiments were performed beyond these datasets, and the present study represents a secondary integrative analysis focusing on miRNA–mRNA network architecture and clinical correlations. The correspondence between the analyzed samples and GEO sample accession numbers is provided in Supplementary Table 1. External pneumonia cohorts (GSE182152 and GSE243219) were analyzed to evaluate the robustness of the identified modules and regulatory networks using module eigengene–based connectivity and network activity metrics. Continuous variables were expressed as medians (interquartile ranges [IQRs]), whereas categorical variables were expressed as frequencies and percentages. Comparisons of continuous variables were performed using the Pearson’s chi-square and Wilcoxon rank-sum tests, as appropriate, whereas categorical variables were analyzed using Fisher’s exact test. Statistical significance was defined as a two-tailed p value of < 0.05. All statistical analyses were performed using R (version 4.5.1; https://www.r-project.org/ ) and Python (version 3.10). This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. Results The analytical workflow is illustrated in Fig. 1 . The patient characteristics of the ARDS cohort are presented in Table 1 and Supplementary Tables 2 and 3. Viral pneumonia was the predominant underlying etiology, accounting for 73.5% of all cases. Moderate ARDS was observed in 47.1% of patients, whereas severe ARDS was identified in 14.7%. The median duration of mechanical ventilation days was 14 (IQR: 7–22 days), and the in-hospital mortality rate was 8.8%. Comparison of peripheral blood transcriptomes between patients with ARDS and healthy controls revealed widespread alterations in gene expression. Differential mRNA expression analysis identified numerous genes that were significantly up- or downregulated (Fig. 2 A), and a heatmap of the top DEGs clearly discriminated between the two groups (Fig. 2 B). Functional enrichment analysis demonstrated marked activation of pathways related to inflammatory responses, cytokine production, and host defense against infection in patients with ARDS. By contrast, pathways associated with adaptive immunity, such as T-cell activation and antigen presentation, were significantly suppressed (Fig. 2 C and 2 D and Supplementary Fig. 1). MiRNA analysis identified miRNAs that differed significantly between patients and healthy controls, although the number of differentially expressed miRNAs was smaller than that of mRNA (Fig. 2 E). These miRNAs exhibited distinct, clustered expression patterns that clearly separated patients from healthy controls (Fig. 2 E and 2 F). Collectively, these findings indicate that the early phase of ARDS is characterized by a distinct transcriptional signature, marked by enhanced inflammatory signaling, suppression of adaptive immune pathways, and concurrent dysregulation of miRNA expression. WGCNA was performed on mRNA and miRNA expression data from patients with ARDS to construct co-expression networks associated with disease pathogenesis (Fig. 3 ). For the mRNA analysis, the top 50% of genes ranked by median absolute deviation (6,130 genes) were selected as input. A soft threshold of β = 9 was selected to approximate scale-free network topology (Fig. 3 A). Using hierarchical clustering combined with dynamic tree cutting, fifteen distinct mRNA co-expression modules were identified (Fig. 3 B, Supplementary Table 4). Correlation analysis between module eigengenes and clinical parameters demonstrated that several modules were significantly correlated with disease severity indicators (Fig. 3 C). Notably, the turquoise module exhibited the strongest negative correlation with the P/F ratio (r = − 0.45, p = 0.008), indicating that this module represents a key gene cluster reflecting the progression of ARDS-related hypoxemia. Gene ontology (GO) enrichment analysis of the turquoise module revealed a significant overrepresentation of biological processes relevant to ARDS pathophysiology and systemic stress responses, including oxygen transport, erythrocyte differentiation, peroxide metabolism, and cellular stress responses (Fig. 3 D). Similarly, WGCNA of the miRNA expression data identified three miRNA co-expression modules (Fig. 3 E and 3 F and Supplementary Table 5). Neither the major ME1 nor ME2 modules showed significant correlations with key clinical indicators (Supplementary Fig. 2). By contrast, integrative correlation analysis between mRNA modules and their corresponding miRNA module eigengenes revealed the strongest negative correlation between the mRNA turquoise module and the miRNA ME2 module (r = − 0.42, p = 0.01) (Fig. 3 F). This finding suggests that miRNAs may act as upstream regulators of disease-associated mRNA co-expression networks in ARDS. Based on the negative correlation between the turquoise mRNA module and the miRNA ME2 module, a detailed analysis of their regulatory interactions was conducted (Supplementary Table 6). Differential expression patterns of genes within the turquoise module were initially assessed using a volcano plot, which confirmed that a substantial number of genes exhibited significant expression changes (Supplementary Fig. 3A). Furthermore, the topological overlap matrix heatmap of the top 50 hub genes demonstrated a robust co-expression structure with high-density connections (Supplementary Fig. 3B). The correlation analysis between the hub mRNAs (342 genes) in the turquoise module and hub miRNAs (20 genes) in the miRNA ME2 module revealed a hub–hub network in which miRNAs broadly exerted suppressive effects on turquoise module genes (Fig. 4 A and 4 B and Supplementary Table 7). These findings indicate that the ME2 moduke functions as a global repressive regulator of the turquoise gene cluster. TargetScan analysis identified 40 genes as predicted targets of ME2-associated miRNAs, confirming a strong co-expression cluster (Fig. 4 A and Supplementary Fig. 3C). Consistently, correlation heatmap analysis revealed distinct gene clusters that were synchronously repressed by miRNAs (Fig. 4 B and 4 C and Supplementary Table 8). GO analysis demonstrated significant overrepresentation of ubiquitin-related enzyme activity (Fig. 4 D), suggesting that ME2-associated miRNAs may modulate key pathways involved in protein quality control and stress responses. Furthermore, strong negative correlations were confirmed for seven experimentally validated miRNA–mRNA pairs curated from miRTarBase (Fig. 4 A and 4 E and Supplementary Table 9). Although several validated target genes were not classified as DEGs (Supplementary Fig. 3D), their central positions within the network suggested that they may function as phase-specific regulators contributing to disease progression following ARDS onset. Finally, integration of all predicted and experimentally validated miRNA–mRNA pairs interactions into a comprehensive network revealed that miR-361-5p and miR-186-5p exerted particularly concentrated regulatory influence over core gene clusters within the turquoise module (Fig. 4 F). Molecular signature scores were calculated using ssGSEA based on predicted (40 genes) and experimentally validated (7 genes) miRNA–mRNA sets. No significant differences in signature scores were observed between patients with ARDS and healthy controls for either gene set (Supplementary Fig. 4A). However, within the ARDS cohort, both signatures showed significant inverse correlations with the P/F ratio (predicted: r = − 0.491, validated: r = − 0.455, both p < 0.01) (Fig. 5 A and Supplementary Fig. 4B). These findings indicate that the identified gene signatures are more closely associated with disease progression following the onset rather than with disease initiation. Given the strong correlation with the P/F ratio, receiver operating characteristic analysis was performed using a P/F ratio of < 200 (corresponding to moderate-to-severe ARDS) as the clinical outcome. Both signatures demonstrated good discriminatory performance, with area under the curve (AUC) values of 0.730 (95% confidence interval: 0.546–0.896) for the predicted gene set and 0.705 (95% confidence interval: 0.516–0.875) for the validated gene set (Fig. 5 B and Supplementary Fig. 4C). When patients were stratified into high expression and low signature score groups based on the median signature score, both the predicted and validated signatures were associated with significantly higher white blood cell counts and CRP levels in the high signature score group (Fig. 5 C and Supplementary Table 10). CRP levels were also significantly higher in the high signature score group in the validated cohort (Supplementary Fig. 4D and Supplementary Table 10). These indicators reflect inflammation and demonstrate that the signatures correlate with the severity of inflammation. Using an external pneumonia cohort, the reproducibility and preservation of the mRNA–miRNA network identified in the ARDS cohort were evaluated (Supplementary Table 11). Co-expression analysis of the 40 predicted mRNA genes in the pneumonia cohort reproduced the original cluster structure, demonstrating strong positive intermodular correlations (Fig. 6 A). This finding confirms that the core mRNA module characteristics observed in the ARDS cohort were preserved in an independent population. In the miRNA–mRNA correlation analysis, many of the predicted miRNA–mRNA pairs identified in the ARDS cohort, involving the two miRNAs expressed in the pneumonia cohort, retained strong negative correlations (Fig. 6 B). Consistent results were also observed for the experimentally validated pairs (2 miRNAs × 7 mRNA) (Fig. 6 C). Comprehensive analysis of all possible interactions (3,740 pairs) between hub mRNAs (n = 340) and hub miRNAs (n = 11) further revealed that the extensive negative correlation structure of the network was preserved in the pneumonia cohort (Fig. 6 D). Although the overall correlation of pairwise relationships between the two cohorts was modest (r = 0.106), a significant correlation was observed, suggesting partial preservation of the regulatory network structure in the external dataset (Fig. 6 E). Furthermore, the application of ssGSEA scores derived from the ARDS cohort to the pneumonia cohort reproduced a consistent “low mRNA expression + high miRNA expression” pattern in high-score patients, whereas low-score patients exhibited the opposite expression pattern (Fig. 6 F). Finally, recalculation of the kME values for the turquoise module in the pneumonia cohort demonstrated a very strong correlation with those observed in the ARDS cohort (r = 0.932, p < 0.05), indicating that module centrality (hubness) was well preserved in the external population (Fig. 6 G). Discussion This study comprehensively analyzed the co-expression patterns of mRNA and miRNAs in the peripheral blood of ARDS patients, revealing that miRNA–mRNA regulatory networks are closely associated with disease progression at the module level. Notably, the turquoise mRNA module exhibited the strongest negative correlation with the P/F ratio and simultaneously showed the most pronounced inverse association with the miRNA ME2 module. These findings suggest that the core mRNA network associated with oxygenation impairment in ARDS is extensively and coordinately suppressed by a specific group of miRNAs, including miR-361-5p and miR-186-5p. Together, these findings support the existence of a functionally relevant miRNA–mRNA regulatory axis that contributes to the pathogenesis and progression of hypoxemic respiratory failure. Importantly, the molecular signatures identified in this study were more strongly associated with disease progression, particularly worsening hypoxia, than with ARDS onset. This observation suggests that post-transcriptional regulation by miRNAs may function primarily as modulators of disease progression rather than as initiators of the acute inflammatory response. Accordingly, miRNAs appear to act as key regulators of hypoxia-driven inflammation and tissue stress, rather than as primary drivers of the acute inflammatory response. Among the miRNAs identified, miR-361-5p and miR-186-5p have not been extensively investigated in the context of ARDS, highlighting the novelty of our findings. miR-361-5p has been implicated in endothelial dysfunction, oxidative stress, and acute stress responses in cardiovascular disease and cancer, with circulating levels correlating with systemic stress and short-term prognosis in acute coronary syndromes [ 16 , 17 ]. Although direct evidence in ARDS remains limited, these characteristics are consistent with a potential role in hypoxia-induced endothelial injury during ARDS progression. Conversely, miR-186-5p has been reported to regulate inflammatory signaling, maintain epithelial barrier integrity, and modulate cellular stress responses, and experimental models of acute lung injury have demonstrated its capacity to attenuate lung injury via the Wnt5a/β-catenin pathway [ 18 ]. Therefore, the observed association between these miRNAs and impaired oxygenation in ARDS is biologically consistent with their established roles in acute stress responses and inflammation. Functional analysis of the turquoise module revealed significant enrichment of pathways associated with hypoxic stress, inflammation, and oxidative injury, including oxygen transport, erythrocyte differentiation, and peroxide metabolism. These pathways closely correspond to the key features of progressive ARDS, such as increased oxidative stress, compensatory erythropoiesis, and tissue-level hypoxia, providing a molecular basis for impaired oxygenation [ 19 ]. Additionally, enrichment of ubiquitin-related enzymatic activities within the miRNA ME2 module suggests that miRNAs exert broad suppressive effects on cellular stress adaptation pathways, including mitochondrial stress responses and protein quality control. This coordinated regulation was consistent with the cumulative cellular damage observed during ARDS progression [ 20 ]. Because the most clinically relevant modules were associated with oxygenation impairment—a pathophysiological feature common to various forms of acute lung injury—we further examined whether the oxygenation-related mRNA–miRNA module identified in patients with ARDS was also observed in an independent cohort of patients with pneumonia accompanied by hypoxemia. The preservation of module eigengene–based connectivity and intramodular hub structure in this cohort indicates that the identified regulatory architecture represents a conserved molecular response to impaired oxygenation rather than a disease-specific phenomenon. These findings suggest that the core regulatory programs driving hypoxemic respiratory failure may be shared across distinct but related clinical conditions. A notable methodological insight from this study is that many experimentally validated miRNA targets were not detected as DEGs. This finding demonstrates that differential expression analysis alone is insufficient to capture biologically central regulatory nodes and highlights the advantage of network-based approaches for identifying hub molecules that define disease-relevant regulatory structures. Consistently, strong preservation of module eigengene–based connectivity and the high correlation of kME values across cohorts indicate that the identified module constitutes a stable biological unit rather than a dataset-specific artifact. Furthermore, the ssGSEA-based molecular signature derived from the integrated miRNA–mRNA network demonstrated consistent associations with clinical indicators of disease severity, including the P/F ratio, bilirubin levels, CRP levels, and white blood cell counts, and demonstrated moderate performance in identifying severe hypoxemia (P/F < 200). These results suggest that the identified network may serve as a quantitative biomarker of ARDS progression. In summary, ARDS pathophysiology is governed by a multilayered, modular regulatory architecture centered on miRNA-mediated post-transcriptional control, which cannot be captured by single-gene analyses alone. By emphasizing oxygenation impairment as a unifying pathophysiological feature, this study provides a framework for understanding shared molecular mechanisms underlying hypoxemic respiratory failure and highlights the potential of miRNAs as biomarkers of disease progression and targets for therapeutic interventions. Limitations This study has several limitations. First, the analysis focused on peripheral blood and did not directly reflect the molecular processes occurring within the lungs. Second, although an independent pneumonia cohort of patients with hypoxemia was used to examine the oxygenation-related regulatory module identified in patients with ARDS, these analyses should be interpreted as validation of the biological relevance of impaired oxygenation rather than as disease-specific assessment of ARDS. Third, the miRNA–mRNA regulatory relationships described were inferred from co-expression patterns, and functional experiments are necessary to establish causality. Furthermore, the relatively small sample size underscores the need for validation in larger cohorts. Future studies should investigate network causality and therapeutic potential of key miRNAs experimental models, such as miRNA-targeted interventions. Abbreviations ARDS Acute respiratory distress syndrome miRNA microRNA WGCNA Weighted Gene Co-expression Network Analysis mRNA-Seq mRNA sequencing miRNA-Seq microRNA sequencing DEGs differentially expressed genes ME Module eigengene ssGSEA single-sample gene set enrichment analysis IQR interquartile range GO Gene ontology AUC Area Under the Curve Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of Osaka University Hospital (approval numbers: 885 [Osaka University Critical Care Consortium Novel Omix Project; Occonomix Project]). All procedures were conducted in accordance with local legislations and institutional requirements. Written informed consent was obtained form all participants prior to enrollment. Consent for publication Not applicable Availability of data and materials All data analyzed in this study are included in the manuscript and supplementary materials. The bulk RNA sequencing and miRNA sequencing data analyzed in this study were previously generated and deposited in the NCBI Gene Expression Omnibus (GEO). The ARDS cohort consisted of samples from GEO accession numbers GSE243217 and GSE243218. The pneumonia cohort were derived from GEO accession numbers GSE182152 and GSE243219. A subset of samples from these datasets was used for the analyses presented in this study. Competing interests The authors declare that they have no competing interests. Funding This study was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (22K09132 to Y.M. and 23K27701 to H.O.) and the Japan Agency for Medical Research and Development (grant no. 20fk0108404h0001). Authors’ contributions YM conceived the study, designed the methodology, conducted the investigation, generated the figures, acquired funding, and drafted and revised the manuscript. TE contributed to the methodological design and manuscript revision. HM conceived the study, designed the methodology, acquired funding, managed the project, and revised the manuscript. DO provided the experts with methodological advice. HO contributed to the funding acquisition and supervision. JO supervised the study. Acknowledgments The authors would like to thank the patients and their families for their participation in this study. Gratitude is also extended to the medical staff for their cooperation and support throughout the study. References Gorman EA, O’Kane CM, McAuley DF. Acute respiratory distress syndrome in adults: diagnosis, outcomes, long-term sequelae, and management. Lancet. 2022;400:1157–70. https://doi.org/10.1016/S0140-6736(22)01439-8 Zhou K, Qin Q, Lu J. Pathophysiological mechanisms of ARDS: a narrative review from molecular to organ-level perspectives. Respir Res. 2025;26:54. https://doi.org/10.1186/s12931-025-03137-5 Ma W, Tang S, Yao P, et al. 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Transl Res. 2018;198:29–39. https://doi.org/10.1016/j.trsl.2018.04.003 Table Table 1. Characteristics of the ARDS cohort Patients with ARDS n=34 Demographics Age, years, median (IQR) 73 (66–79) Sex, male, no. (%) 23 (67.6) BMI, median, (IQR) 22.4 (20.5–25.2) Coexisting disease, no. (%) Hypertension 15 (44.1) Diabetes 13 (38.2) Chronic lung disease 6 (17.6) Renal insufficiency 6 (17.6) Immunocompromise 5 (14.7) Cardiovascular compromise 3 (8.8) Malignant neoplasm 1 (2.9) Main cause of ARDS, no. (%) Virus 25 (73.5) Bacterial pneumonia 6 (17.6) Exacerbation of interstitial pneumonia 3 (8.8) Severity of disease on admission APACHE Ⅱ, median (IQR) 14 (10–17) SOFA, median (IQR) 6 (3–6) Severity of ARDS, no. (%) Mild 13 (38.2) Moderate 16 (47.1) Severe 5 (14.7) Disease course Length of mechanical ventilation, days, median (IQR) 14 (7–22) Length of stay in hospital, days, median (IQR) 22 (13–43) Hospital mortality, no. (%) 3 (8.8) ARDS: acute respiratory distress syndrome, IQR: interquartile range, BMI: body mass index, APACHE II: Acute Physiology and Chronic Health Evaluation Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Supplementaryfigure1.pdf Supplementaryfigure2.pdf Supplementaryfigure3.pdf Supplementaryfigure4.pdf Supplementarytables.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 04 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 03 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8776914","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592992449,"identity":"84f1acff-19c0-41af-ae79-d08b1d43002b","order_by":0,"name":"Yumi Mitsuyama","email":"","orcid":"","institution":"The University of Osaka","correspondingAuthor":false,"prefix":"","firstName":"Yumi","middleName":"","lastName":"Mitsuyama","suffix":""},{"id":592992450,"identity":"1efa90f6-c7a0-4f68-85a7-466aecefb3e6","order_by":1,"name":"Hisatake Matsumoto","email":"data:image/png;base64,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","orcid":"","institution":"The University of Osaka","correspondingAuthor":true,"prefix":"","firstName":"Hisatake","middleName":"","lastName":"Matsumoto","suffix":""},{"id":592992451,"identity":"1f38edf9-a819-4aa9-b819-5e0162b98f3b","order_by":2,"name":"Takeshi Ebihara","email":"","orcid":"","institution":"The University of Osaka","correspondingAuthor":false,"prefix":"","firstName":"Takeshi","middleName":"","lastName":"Ebihara","suffix":""},{"id":592992452,"identity":"5ea9fd72-ac90-4efd-a6ef-6b36baa20061","order_by":3,"name":"Daisuke Okuzaki","email":"","orcid":"","institution":"The University of Osaka","correspondingAuthor":false,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Okuzaki","suffix":""},{"id":592992456,"identity":"1df4b2db-4811-4d58-b4aa-30ae7bc41c9c","order_by":4,"name":"Hiroshi Ogura","email":"","orcid":"","institution":"The University of Osaka","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Ogura","suffix":""},{"id":592992459,"identity":"f2476145-99a9-4df1-98f7-0d845d774502","order_by":5,"name":"Jun Oda","email":"","orcid":"","institution":"The University of Osaka","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Oda","suffix":""}],"badges":[],"createdAt":"2026-02-03 14:10:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8776914/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8776914/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103504783,"identity":"775b0918-8bf9-4344-aa01-0e68e95dd3db","added_by":"auto","created_at":"2026-02-26 13:21:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1508322,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchematic overview of the study design, including patient enrollment, transcriptome profiling, co-expression network construction, miRNA–mRNA network integration, and analysis of the external cohort.\u003c/p\u003e\n\u003cp\u003eARDS, acute respiratory distress syndrome; WGCNA; Weighted Gene Co-expression Network Analysis, SOFA; Sequential Organ Failure Assessment; kME, Module Eigengene-based Connectivity; ssGSEA, single-sample gene set enrichment analysis\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/f12fa0d6d88cbad488139956.png"},{"id":103505030,"identity":"9e48f13c-c7a8-4ea7-a163-180566ecd8ad","added_by":"auto","created_at":"2026-02-26 13:22:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5994243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of transcriptomic analyses in patients with ARDS and healthy controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot depicting differential mRNA expression between patients with ARDS and healthy controls. A total of 12,261 genes were analyzed, and differentially expressed genes (DEGs) were defined as an adjusted p value of \u0026lt;0.05 and a |log₂ fold change (FC)| of \u0026gt;1. Under these thresholds, 3,074 DEGs were identified (magenta dots).\u003c/p\u003e\n\u003cp\u003e(B) Heatmap showing the expression patterns of the top 100 mRNA differentially expressed genes . Genes were ranked by absolute log₂FC. The expression values were normalized prior to visualization.\u003c/p\u003e\n\u003cp\u003e(C) Gene Ontology (GO) enrichment analysis for biological process (BP) terms associated with upregulated DEGs identified in patients with ARDS. The most significantly enriched biological processes are shown.\u003c/p\u003e\n\u003cp\u003e(D) GO enrichment analysis of BP terms associated with downregulated DEGs identified in patients with ARDS.\u003c/p\u003e\n\u003cp\u003e(E) Volcano plot showing differential miRNA expression between patients with ARDS and healthy controls. Of the 444 miRNAs analyzed, 26 DEGs were identified using the adjusted p value of \u0026lt;0.05 and |log₂FC| of \u0026gt;0.3 (highlighted in magenta).\u003c/p\u003e\n\u003cp\u003e(F) Heatmap illustrating the expression patterns of differentially expressed miRNAs between patients with ARDS and healthy controls\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/b4e5e365ba24cb8ca030c93c.png"},{"id":103505457,"identity":"6a2711aa-f023-435b-8c06-4fa2cdc16146","added_by":"auto","created_at":"2026-02-26 13:31:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4115002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted gene co-expression network analysis (WGCNA) of mRNA and miRNA expression in patients with ARDS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Determination of the soft-thresholding power (β) for WGCNA based on mRNA expression status. Scale-free topology model fit and mean connectivity are shown. Based on the criteria for approximate scale-free network topology, a soft-thresholding power of β = 9 was selected for subsequent analyses.\u003c/p\u003e\n\u003cp\u003e(B) Cluster dendrogram of the mRNA co-expression network generated by WGCNA. Fifteen co-expression modules were identified using dynamic tree cutting. The color bar below the dendrogram indicates the module assignment of each gene.\u003c/p\u003e\n\u003cp\u003e(C) Correlation analysis between mRNA module eigengenes and clinical parameters. The turquoise module showed the strongest negative correlation with the P/F ratio.\u003c/p\u003e\n\u003cp\u003e(D) Gene Ontology enrichment analysis of genes within the turquoise module, showing enriched biological process (BP) and molecular function (MF) terms.\u003c/p\u003e\n\u003cp\u003e(E) Determination of the soft-thresholding power for WGCNA based on miRNA expression data. As in the mRNA analysis, scale-free topology criteria were used to select the optimal soft-thresholding power.\u003c/p\u003e\n\u003cp\u003e(F) Correlation analysis between miRNA and mRNA modules, demonstrating module-level associations between the two co-expression networks.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/f04237fbe9a4aea0f1cb851f.png"},{"id":103177117,"identity":"41dccaeb-6530-441f-b5df-a8e34d136618","added_by":"auto","created_at":"2026-02-22 16:46:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3201583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of the miRNA–mRNA regulatory network in the ARDS cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Comparison of gene set sizes associated with the turquoise module, including total turquoise genes, turquoise hub genes, predicted miRNA target genes, and validated targets.\u003c/p\u003e\n\u003cp\u003e(B) Correlation heatmap between hub miRNAs and hub mRNAs. Hierarchical clustering on both axes reveals coordinated miRNA-mediated regulation of mRNA expression.\u003c/p\u003e\n\u003cp\u003e(C) Correlation heatmap of predicted miRNA–mRNA pairs, showing widespread negative correlations consistent with observations in the ARDS cohort.\u003c/p\u003e\n\u003cp\u003e(D) Gene Ontology enrichment analysis of molecular function terms for the predicted mRNA target gene set\u003c/p\u003e\n\u003cp\u003e(E) Correlation heatmap of validated miRNA–mRNA pairs (7 mRNAs × 2 miRNAs), showing strong negative correlations consistent with findings in the ARDS cohort\u003c/p\u003e\n\u003cp\u003e(F) miRNA–mRNA regulatory network centered on ME2 (miRNA module eigengene) and ME1 (mRNA module eigengene). Node size indicates gene importance (degree), whereas arrow colors denote predicted or validated regulatory relationships.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/809036bf4e527f12d179248e.png"},{"id":103505181,"identity":"e43fe7cc-06dc-4e58-a9bd-00159d3d6ba0","added_by":"auto","created_at":"2026-02-26 13:26:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1361970,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between predicted miRNA target gene signatures and clinical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Correlation between the ssGSEA scores of the predicted miRNA target gene set and the P/F ratio. Spearman’s correlation analysis revealed a significant negative association (r = −0.491, p \u0026lt; 0.05). The red line represents the regression line, whereas the pink shaded area indicates the 95% confidence interval.\u003c/p\u003e\n\u003cp\u003e(B) Receiver operating characteristic (ROC) curve evaluating the predictive performance of the predicted target gene signature for identifying patients with a P/F ratio of \u0026lt;200 (area under the curve (AUC): 0.730).\u003c/p\u003e\n\u003cp\u003e(C) Comparison of clinical parameters between high and low signature score groups stratified by ssGSEA scores\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/0c27c60dabb35d63bfa6a07c.png"},{"id":103504599,"identity":"b78357ce-4641-433c-b199-f894de4e85b1","added_by":"auto","created_at":"2026-02-26 13:20:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4694005,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReproducibility and preservation of the mRNA–miRNA regulatory network in a pneumonia cohort with hypoxemia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Co-expression heatmap of 40 predicted mRNAs in the pneumonia cohort\u003c/p\u003e\n\u003cp\u003e(B) Correlation heatmap between the two validate miRNAs and the predicted mRNAs, showing consistent negative correlations\u003c/p\u003e\n\u003cp\u003e(C) Correlation heatmap of validated miRNA–mRNA pairs (2 miRNAs × 7 mRNAs) in the pneumonia cohort, demonstrating consistent negative correlations\u003c/p\u003e\n\u003cp\u003e(D) Correlation heatmap between hub miRNAs and hub mRNAs belonging to ME2 module, demonstrating widespread negative miRNA–mRNA associations\u003c/p\u003e\n\u003cp\u003e(E) Comparison of gene–gene correlation coefficients for 342 hub mRNAs between the ARDS and pneumonia cohorts, showing a weak but significant positive correlation (r = 0.106, p =7.24e-11)\u003c/p\u003e\n\u003cp\u003e(F) Heatmap of mRNA expression based on ssGSEA scores calculated in the ARDS cohort. Samples with high ssGSEA scores consistently exhibited lower mRNA expression patterns.\u003c/p\u003e\n\u003cp\u003e(G) Preservation analysis of module eigengene–based connectivity for the turquoise module between the ARDS and pneumonia cohorts, showing a strong positive correlation (r = 0.932, p = 9.98e -322)\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/11751f6b39c25e9195ba4280.png"},{"id":103509562,"identity":"05211570-76b5-4804-99cb-3fc2a92ef6a7","added_by":"auto","created_at":"2026-02-26 13:59:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22049386,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/40b0554b-3af0-4c8b-8fc3-e7706c7b6247.pdf"},{"id":103177112,"identity":"2fc8d7fe-9909-4fa6-833a-ad911e8d9b5e","added_by":"auto","created_at":"2026-02-22 16:46:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21883,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/3e25ea4a33d7860630f47b75.docx"},{"id":103505195,"identity":"cdc26735-fce5-4b48-a504-bdb2b7480afd","added_by":"auto","created_at":"2026-02-26 13:27:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":112008,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/dbcb247dda12e3721d02cbbe.pdf"},{"id":103505132,"identity":"1328a1c0-f5e8-4195-98b8-53204a83a76f","added_by":"auto","created_at":"2026-02-26 13:24:50","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":80760,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/e20b04d6c1db497baad704a4.pdf"},{"id":103505017,"identity":"0d6e1c83-c2ea-4002-9b7a-c78551cf9c06","added_by":"auto","created_at":"2026-02-26 13:22:26","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1749586,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/9ff541581a0851320467b615.pdf"},{"id":103505445,"identity":"f6ca3610-97c2-49f4-9cb5-cefa2790ed6d","added_by":"auto","created_at":"2026-02-26 13:31:08","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":267698,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/196f633cfd662699e530cd7d.pdf"},{"id":103177122,"identity":"d5ec228c-6637-430a-98b7-861a99d74852","added_by":"auto","created_at":"2026-02-22 16:46:28","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":170748,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8776914/v1/9737e7d9debca24dffc909e6.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Oxygenation Impairment–Associated Gene Networks in ARDS Through Integrated mRNA and miRNA Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute respiratory distress syndrome (ARDS) is a life-threatening condition characterized by severe hypoxemia and diffuse lung injury and is associated with high mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Current management is largely supportive, as no molecular targeted therapies that directly address the underlying pathophysiology have been established. The development of effective treatments has been hindered by the heterogeneity of ARDS pathophysiology, which encompasses diverse mechanisms\u0026mdash;including inflammation, immune dysregulation, and hypercoagulability\u0026mdash;resulting in significant variations in clinical outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A detailed understanding of this heterogeneity at the molecular level is essential for advancing precision medicine approaches in ARDS.\u003c/p\u003e \u003cp\u003eRecent omics-based studies, including transcriptomics and proteomics, have delineated molecular subtypes of ARDS, such as pro-inflammatory and immunosuppressive phenotypes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, many of these studies have relied predominantly on differential expression analyses of single-layer omics data, providing limited insights into the regulatory networks that orchestrate disease pathophysiology.\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) regulate gene expression post-transcriptionally by simultaneously targeting multiple mRNAs and play central roles in pathways involved in inflammation, immune responses, and cellular stress [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As higher-level regulatory elements that coordinately govern changes in mRNA and protein expression, mRNAs may be critical to understanding the molecular heterogeneity of ARDS [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nevertheless, integrated analyses of miRNA\u0026ndash;mRNA interactions in ARDS remain scarce, with limited validation of network structural features or reproducibility in external cohorts. Although emerging evidence indicates that miRNA and mRNA interactions contribute to immune dysregulation in ARDS, a comprehensive network-level understanding is still lacking [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to elucidate the miRNA\u0026ndash;mRNA regulatory network underlying the molecular pathophysiology of ARDS. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct co-expression networks for both miRNAs and mRNA from peripheral blood samples of patients with ARDS. Integration of these networks enabled the identification of regulatory modules and molecular signatures associated with disease severity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis prospective, single-center observational study was conducted at The University of Osaka Hospital between July 2020 and February 2021. Patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years who were diagnosed with ARDS according to the Berlin definition were enrolled within 24 h of diagnosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Clinical data, including age, comorbidities, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Sequential Organ Failure Assessment (SOFA) score, and clinical outcomes, were extracted from electronic medical records [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Patients aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years, who declined active treatment, or who refused to participate in the study were excluded. Healthy volunteers without acute or chronic inflammatory disease were recruited as controls. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Review Committee of Osaka University Hospital (approval no.: 885). Written informed consent was obtained from all participants or their legally authorized representatives.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample collection and RNA isolation\u003c/h3\u003e\n\u003cp\u003ePeripheral whole blood samples were collected within 24 hours of ARDS diagnosis using PAXgene\u0026trade; Blood RNA Tubes (BD Biosciences, San Jose, CA). Total RNA was extracted using a PAXgene Blood RNA Kit (BD Biosciences) following the manufacturer\u0026rsquo;s instructions. The same procedures were applied for blood collection and RNA isolation in healthy controls.\u003c/p\u003e\n\u003ch3\u003eTranscriptome sequencing\u003c/h3\u003e\n\u003cp\u003eBoth mRNA sequencing (mRNA-Seq) and miRNA sequencing (miRNA-Seq) were performed for all samples. For mRNA-seq, libraries were prepared using the SMART-Seq HT kit (Takara Bio, Shiga, Japan) and sequenced using the DNBSEQ-G400RS platform. For miRNA-Seq, small RNA libraries were constructed using the NEBNext Small RNA Library Prep Set for Illumina (New England Biolabs) and sequenced on the NovaSeq 6000 platform. Detailed information regarding RNA quality control, library preparation, sequencing depth, read alignment, reference genomes, and expression quantification methods is provided in Supplementary Methods.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eRaw read count data for mRNAs and miRNAs were filtered to remove low-expression features, normalized using the trimmed mean of M values (TMM) method, and log-transformed. Detailed descriptions of preprocessing steps and filtering thresholds are provided in Supplementary Methods. Differential expression analysis was performed to identify differentially expressed genes (DEGs) and miRNAs between patients with ARDS and healthy controls. Next, WGCNA was performed exclusively on the samples from patient with ARDS to construct co-expression modules [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Module eigengenes (MEs), representing the first principal components of each module, were correlated with clinical traits to identify mRNA modules with clinical relevance. Within these modules, hub genes and miRNAs were defined based on high module eigengene-based connectivity (kME), reflecting strong intramodular connectivity and central regulatory roles. Correlations between the MEs of mRNA and miRNA modules were subsequently evaluated to identify key interacting module pairs. miRNA\u0026ndash;mRNA regulatory networks were then inferred by integrating computationally predicted miRNA targets from TargetScan with experimentally validated targets from miRTarBase, with a focus on hub miRNAs and mRNAs within the identified modules [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The activity of molecular signatures derived from the integrated miRNA\u0026ndash;mRNA network was quantified using single-sample gene set enrichment analysis (ssGSEA) and correlated with clinical outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The RNA sequencing data analyzed in this study were generated within the same laboratory and derived from the same underlying biological samples as those previously deposited in the Gene Expression Omnibus (GEO) database under accession numbers GSE243217 and GSE243218.\u003c/p\u003e \u003cp\u003eNo additional or independent sequencing experiments were performed beyond these datasets, and the present study represents a secondary integrative analysis focusing on miRNA\u0026ndash;mRNA network architecture and clinical correlations. The correspondence between the analyzed samples and GEO sample accession numbers is provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eExternal pneumonia cohorts (GSE182152 and GSE243219) were analyzed to evaluate the robustness of the identified modules and regulatory networks using module eigengene\u0026ndash;based connectivity and network activity metrics.\u003c/p\u003e \u003cp\u003eContinuous variables were expressed as medians (interquartile ranges [IQRs]), whereas categorical variables were expressed as frequencies and percentages. Comparisons of continuous variables were performed using the Pearson\u0026rsquo;s chi-square and Wilcoxon rank-sum tests, as appropriate, whereas categorical variables were analyzed using Fisher\u0026rsquo;s exact test. Statistical significance was defined as a two-tailed p value of \u0026lt;\u0026thinsp;0.05. All statistical analyses were performed using R (version 4.5.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Python (version 3.10). This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe analytical workflow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The patient characteristics of the ARDS cohort are presented in Table\u0026nbsp;1 and Supplementary Tables\u0026nbsp;2 and 3. Viral pneumonia was the predominant underlying etiology, accounting for 73.5% of all cases. Moderate ARDS was observed in 47.1% of patients, whereas severe ARDS was identified in 14.7%. The median duration of mechanical ventilation days was 14 (IQR: 7\u0026ndash;22 days), and the in-hospital mortality rate was 8.8%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparison of peripheral blood transcriptomes between patients with ARDS and healthy controls revealed widespread alterations in gene expression. Differential mRNA expression analysis identified numerous genes that were significantly up- or downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and a heatmap of the top DEGs clearly discriminated between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Functional enrichment analysis demonstrated marked activation of pathways related to inflammatory responses, cytokine production, and host defense against infection in patients with ARDS. By contrast, pathways associated with adaptive immunity, such as T-cell activation and antigen presentation, were significantly suppressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and Supplementary Fig.\u0026nbsp;1). MiRNA analysis identified miRNAs that differed significantly between patients and healthy controls, although the number of differentially expressed miRNAs was smaller than that of mRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These miRNAs exhibited distinct, clustered expression patterns that clearly separated patients from healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Collectively, these findings indicate that the early phase of ARDS is characterized by a distinct transcriptional signature, marked by enhanced inflammatory signaling, suppression of adaptive immune pathways, and concurrent dysregulation of miRNA expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWGCNA was performed on mRNA and miRNA expression data from patients with ARDS to construct co-expression networks associated with disease pathogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the mRNA analysis, the top 50% of genes ranked by median absolute deviation (6,130 genes) were selected as input. A soft threshold of β\u0026thinsp;=\u0026thinsp;9 was selected to approximate scale-free network topology (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Using hierarchical clustering combined with dynamic tree cutting, fifteen distinct mRNA co-expression modules were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Supplementary Table\u0026nbsp;4). Correlation analysis between module eigengenes and clinical parameters demonstrated that several modules were significantly correlated with disease severity indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Notably, the turquoise module exhibited the strongest negative correlation with the P/F ratio (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.45, p\u0026thinsp;=\u0026thinsp;0.008), indicating that this module represents a key gene cluster reflecting the progression of ARDS-related hypoxemia. Gene ontology (GO) enrichment analysis of the turquoise module revealed a significant overrepresentation of biological processes relevant to ARDS pathophysiology and systemic stress responses, including oxygen transport, erythrocyte differentiation, peroxide metabolism, and cellular stress responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Similarly, WGCNA of the miRNA expression data identified three miRNA co-expression modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF and Supplementary Table\u0026nbsp;5). Neither the major ME1 nor ME2 modules showed significant correlations with key clinical indicators (Supplementary Fig.\u0026nbsp;2). By contrast, integrative correlation analysis between mRNA modules and their corresponding miRNA module eigengenes revealed the strongest negative correlation between the mRNA turquoise module and the miRNA ME2 module (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.42, p\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). This finding suggests that miRNAs may act as upstream regulators of disease-associated mRNA co-expression networks in ARDS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the negative correlation between the turquoise mRNA module and the miRNA ME2 module, a detailed analysis of their regulatory interactions was conducted (Supplementary Table\u0026nbsp;6). Differential expression patterns of genes within the turquoise module were initially assessed using a volcano plot, which confirmed that a substantial number of genes exhibited significant expression changes (Supplementary Fig.\u0026nbsp;3A). Furthermore, the topological overlap matrix heatmap of the top 50 hub genes demonstrated a robust co-expression structure with high-density connections (Supplementary Fig.\u0026nbsp;3B). The correlation analysis between the hub mRNAs (342 genes) in the turquoise module and hub miRNAs (20 genes) in the miRNA ME2 module revealed a hub\u0026ndash;hub network in which miRNAs broadly exerted suppressive effects on turquoise module genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Supplementary Table\u0026nbsp;7). These findings indicate that the ME2 moduke functions as a global repressive regulator of the turquoise gene cluster. TargetScan analysis identified 40 genes as predicted targets of ME2-associated miRNAs, confirming a strong co-expression cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and Supplementary Fig.\u0026nbsp;3C). Consistently, correlation heatmap analysis revealed distinct gene clusters that were synchronously repressed by miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and Supplementary Table\u0026nbsp;8). GO analysis demonstrated significant overrepresentation of ubiquitin-related enzyme activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), suggesting that ME2-associated miRNAs may modulate key pathways involved in protein quality control and stress responses. Furthermore, strong negative correlations were confirmed for seven experimentally validated miRNA\u0026ndash;mRNA pairs curated from miRTarBase (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and Supplementary Table\u0026nbsp;9). Although several validated target genes were not classified as DEGs (Supplementary Fig.\u0026nbsp;3D), their central positions within the network suggested that they may function as phase-specific regulators contributing to disease progression following ARDS onset. Finally, integration of all predicted and experimentally validated miRNA\u0026ndash;mRNA pairs interactions into a comprehensive network revealed that miR-361-5p and miR-186-5p exerted particularly concentrated regulatory influence over core gene clusters within the turquoise module (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMolecular signature scores were calculated using ssGSEA based on predicted (40 genes) and experimentally validated (7 genes) miRNA\u0026ndash;mRNA sets. No significant differences in signature scores were observed between patients with ARDS and healthy controls for either gene set (Supplementary Fig.\u0026nbsp;4A). However, within the ARDS cohort, both signatures showed significant inverse correlations with the P/F ratio (predicted: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.491, validated: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.455, both p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and Supplementary Fig.\u0026nbsp;4B). These findings indicate that the identified gene signatures are more closely associated with disease progression following the onset rather than with disease initiation. Given the strong correlation with the P/F ratio, receiver operating characteristic analysis was performed using a P/F ratio of \u0026lt;\u0026thinsp;200 (corresponding to moderate-to-severe ARDS) as the clinical outcome. Both signatures demonstrated good discriminatory performance, with area under the curve (AUC) values of 0.730 (95% confidence interval: 0.546\u0026ndash;0.896) for the predicted gene set and 0.705 (95% confidence interval: 0.516\u0026ndash;0.875) for the validated gene set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Supplementary Fig.\u0026nbsp;4C). When patients were stratified into high expression and low signature score groups based on the median signature score, both the predicted and validated signatures were associated with significantly higher white blood cell counts and CRP levels in the high signature score group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and Supplementary Table\u0026nbsp;10). CRP levels were also significantly higher in the high signature score group in the validated cohort (Supplementary Fig.\u0026nbsp;4D and Supplementary Table\u0026nbsp;10). These indicators reflect inflammation and demonstrate that the signatures correlate with the severity of inflammation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing an external pneumonia cohort, the reproducibility and preservation of the mRNA\u0026ndash;miRNA network identified in the ARDS cohort were evaluated (Supplementary Table\u0026nbsp;11). Co-expression analysis of the 40 predicted mRNA genes in the pneumonia cohort reproduced the original cluster structure, demonstrating strong positive intermodular correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). This finding confirms that the core mRNA module characteristics observed in the ARDS cohort were preserved in an independent population. In the miRNA\u0026ndash;mRNA correlation analysis, many of the predicted miRNA\u0026ndash;mRNA pairs identified in the ARDS cohort, involving the two miRNAs expressed in the pneumonia cohort, retained strong negative correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Consistent results were also observed for the experimentally validated pairs (2 miRNAs \u0026times; 7 mRNA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Comprehensive analysis of all possible interactions (3,740 pairs) between hub mRNAs (n\u0026thinsp;=\u0026thinsp;340) and hub miRNAs (n\u0026thinsp;=\u0026thinsp;11) further revealed that the extensive negative correlation structure of the network was preserved in the pneumonia cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Although the overall correlation of pairwise relationships between the two cohorts was modest (r\u0026thinsp;=\u0026thinsp;0.106), a significant correlation was observed, suggesting partial preservation of the regulatory network structure in the external dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Furthermore, the application of ssGSEA scores derived from the ARDS cohort to the pneumonia cohort reproduced a consistent \u0026ldquo;low mRNA expression\u0026thinsp;+\u0026thinsp;high miRNA expression\u0026rdquo; pattern in high-score patients, whereas low-score patients exhibited the opposite expression pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Finally, recalculation of the kME values for the turquoise module in the pneumonia cohort demonstrated a very strong correlation with those observed in the ARDS cohort (r\u0026thinsp;=\u0026thinsp;0.932, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that module centrality (hubness) was well preserved in the external population (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study comprehensively analyzed the co-expression patterns of mRNA and miRNAs in the peripheral blood of ARDS patients, revealing that miRNA\u0026ndash;mRNA regulatory networks are closely associated with disease progression at the module level. Notably, the turquoise mRNA module exhibited the strongest negative correlation with the P/F ratio and simultaneously showed the most pronounced inverse association with the miRNA ME2 module. These findings suggest that the core mRNA network associated with oxygenation impairment in ARDS is extensively and coordinately suppressed by a specific group of miRNAs, including miR-361-5p and miR-186-5p. Together, these findings support the existence of a functionally relevant miRNA\u0026ndash;mRNA regulatory axis that contributes to the pathogenesis and progression of hypoxemic respiratory failure.\u003c/p\u003e \u003cp\u003eImportantly, the molecular signatures identified in this study were more strongly associated with disease progression, particularly worsening hypoxia, than with ARDS onset. This observation suggests that post-transcriptional regulation by miRNAs may function primarily as modulators of disease progression rather than as initiators of the acute inflammatory response. Accordingly, miRNAs appear to act as key regulators of hypoxia-driven inflammation and tissue stress, rather than as primary drivers of the acute inflammatory response.\u003c/p\u003e \u003cp\u003eAmong the miRNAs identified, miR-361-5p and miR-186-5p have not been extensively investigated in the context of ARDS, highlighting the novelty of our findings. miR-361-5p has been implicated in endothelial dysfunction, oxidative stress, and acute stress responses in cardiovascular disease and cancer, with circulating levels correlating with systemic stress and short-term prognosis in acute coronary syndromes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although direct evidence in ARDS remains limited, these characteristics are consistent with a potential role in hypoxia-induced endothelial injury during ARDS progression. Conversely, miR-186-5p has been reported to regulate inflammatory signaling, maintain epithelial barrier integrity, and modulate cellular stress responses, and experimental models of acute lung injury have demonstrated its capacity to attenuate lung injury via the Wnt5a/β-catenin pathway [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, the observed association between these miRNAs and impaired oxygenation in ARDS is biologically consistent with their established roles in acute stress responses and inflammation.\u003c/p\u003e \u003cp\u003eFunctional analysis of the turquoise module revealed significant enrichment of pathways associated with hypoxic stress, inflammation, and oxidative injury, including oxygen transport, erythrocyte differentiation, and peroxide metabolism. These pathways closely correspond to the key features of progressive ARDS, such as increased oxidative stress, compensatory erythropoiesis, and tissue-level hypoxia, providing a molecular basis for impaired oxygenation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, enrichment of ubiquitin-related enzymatic activities within the miRNA ME2 module suggests that miRNAs exert broad suppressive effects on cellular stress adaptation pathways, including mitochondrial stress responses and protein quality control. This coordinated regulation was consistent with the cumulative cellular damage observed during ARDS progression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBecause the most clinically relevant modules were associated with oxygenation impairment\u0026mdash;a pathophysiological feature common to various forms of acute lung injury\u0026mdash;we further examined whether the oxygenation-related mRNA\u0026ndash;miRNA module identified in patients with ARDS was also observed in an independent cohort of patients with pneumonia accompanied by hypoxemia. The preservation of module eigengene\u0026ndash;based connectivity and intramodular hub structure in this cohort indicates that the identified regulatory architecture represents a conserved molecular response to impaired oxygenation rather than a disease-specific phenomenon. These findings suggest that the core regulatory programs driving hypoxemic respiratory failure may be shared across distinct but related clinical conditions.\u003c/p\u003e \u003cp\u003eA notable methodological insight from this study is that many experimentally validated miRNA targets were not detected as DEGs. This finding demonstrates that differential expression analysis alone is insufficient to capture biologically central regulatory nodes and highlights the advantage of network-based approaches for identifying hub molecules that define disease-relevant regulatory structures. Consistently, strong preservation of module eigengene\u0026ndash;based connectivity and the high correlation of kME values across cohorts indicate that the identified module constitutes a stable biological unit rather than a dataset-specific artifact. Furthermore, the ssGSEA-based molecular signature derived from the integrated miRNA\u0026ndash;mRNA network demonstrated consistent associations with clinical indicators of disease severity, including the P/F ratio, bilirubin levels, CRP levels, and white blood cell counts, and demonstrated moderate performance in identifying severe hypoxemia (P/F\u0026thinsp;\u0026lt;\u0026thinsp;200). These results suggest that the identified network may serve as a quantitative biomarker of ARDS progression.\u003c/p\u003e \u003cp\u003eIn summary, ARDS pathophysiology is governed by a multilayered, modular regulatory architecture centered on miRNA-mediated post-transcriptional control, which cannot be captured by single-gene analyses alone. By emphasizing oxygenation impairment as a unifying pathophysiological feature, this study provides a framework for understanding shared molecular mechanisms underlying hypoxemic respiratory failure and highlights the potential of miRNAs as biomarkers of disease progression and targets for therapeutic interventions.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations. First, the analysis focused on peripheral blood and did not directly reflect the molecular processes occurring within the lungs. Second, although an independent pneumonia cohort of patients with hypoxemia was used to examine the oxygenation-related regulatory module identified in patients with ARDS, these analyses should be interpreted as validation of the biological relevance of impaired oxygenation rather than as disease-specific assessment of ARDS. Third, the miRNA\u0026ndash;mRNA regulatory relationships described were inferred from co-expression patterns, and functional experiments are necessary to establish causality. Furthermore, the relatively small sample size underscores the need for validation in larger cohorts. Future studies should investigate network causality and therapeutic potential of key miRNAs experimental models, such as miRNA-targeted interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARDS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Acute respiratory distress syndrome\u003c/p\u003e\n\u003cp\u003emiRNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;microRNA\u003c/p\u003e\n\u003cp\u003eWGCNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Weighted Gene Co-expression Network Analysis\u003c/p\u003e\n\u003cp\u003emRNA-Seq\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;mRNA sequencing\u003c/p\u003e\n\u003cp\u003emiRNA-Seq\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;microRNA sequencing\u003c/p\u003e\n\u003cp\u003eDEGs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;differentially expressed genes\u003c/p\u003e\n\u003cp\u003eME\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Module eigengene\u0026nbsp;\u003c/p\u003e\n\u003cp\u003essGSEA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;single-sample gene set enrichment analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;interquartile range\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene ontology\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Area Under the Curve\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Osaka University Hospital (approval numbers: 885 [Osaka University Critical Care Consortium Novel Omix Project; Occonomix Project]). All procedures were conducted in accordance with local legislations and institutional requirements. Written informed consent was obtained form all participants prior to enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed in this study are included in the manuscript and supplementary materials. The bulk RNA sequencing and miRNA sequencing data analyzed in this study were previously generated and deposited in the NCBI Gene Expression Omnibus (GEO). The ARDS cohort consisted of samples from GEO accession numbers GSE243217 and GSE243218. The pneumonia cohort were derived from GEO accession numbers GSE182152 and GSE243219. A subset of samples from these datasets was used for the analyses presented in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (22K09132 to Y.M. and 23K27701 to H.O.) and the Japan Agency for Medical Research and Development (grant no. 20fk0108404h0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYM conceived the study, designed the methodology, conducted the investigation, generated the figures, acquired funding, and drafted and revised the manuscript. TE contributed to the methodological design and manuscript revision. HM conceived the study, designed the methodology, acquired funding, managed the project, and revised the manuscript. DO provided the experts with methodological advice. HO contributed to the funding acquisition and supervision. JO supervised the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the patients and their families for their participation in this study. Gratitude is also extended to the medical staff for their cooperation and support throughout the study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGorman EA, O\u0026rsquo;Kane CM, McAuley DF. Acute respiratory distress syndrome in adults: diagnosis, outcomes, long-term sequelae, and management. Lancet. 2022;400:1157\u0026ndash;70. https://doi.org/10.1016/S0140-6736(22)01439-8\u003c/li\u003e\n\u003cli\u003eZhou K, Qin Q, Lu J. Pathophysiological mechanisms of ARDS: a narrative review from molecular to organ-level perspectives. Respir Res. 2025;26:54. https://doi.org/10.1186/s12931-025-03137-5\u003c/li\u003e\n\u003cli\u003eMa W, Tang S, Yao P, et al. Advances in acute respiratory distress syndrome: focusing on heterogeneity, pathophysiology, and therapeutic strategies. Signal Transduct Target Ther. 2025;10(1):75. https://doi.org/10.1038/s41392-025-02127-9\u003c/li\u003e\n\u003cli\u003eBos LDJ, Ware LB. Acute respiratory distress syndrome: causes, pathophysiology, and phenotypes. Lancet. 2022;400:1145\u0026ndash;56. https://doi.org/10.1016/S0140-6736(22)01485-4\u003c/li\u003e\n\u003cli\u003eWang P, Lai D, Jin L, Xue Y. Roles of microRNAs in acute lung injury and acute respiratory distress syndrome: mechanisms and clinical potential. Front Immunol. 2025;16:1570128. https://doi.org/10.3389/fimmu.2025.1570128\u003c/li\u003e\n\u003cli\u003eSelbach M, Schwanh\u0026auml;usser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N. Widespread changes in protein synthesis induced by microRNAs. Nature. 2008;455:58\u0026ndash;63. https://doi.org/10.1038/nature07228\u003c/li\u003e\n\u003cli\u003eLu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, et al. MicroRNA expression profiles classify human cancers. Nature. 2005;435:834\u0026ndash;8. https://doi.org/10.1038/nature03702\u003c/li\u003e\n\u003cli\u003eMitsuyama Y, Matsumoto H, Togami Y, Oda S, Onishi S, Yoshimura J, et al. T cell dysfunction in elderly ARDS patients based on miRNA and mRNA integration analysis. Front Immunol. 2024;15:1368446. https://doi.org/10.3389/fimmu.2024.1368446\u003c/li\u003e\n\u003cli\u003eARDS Definition Task Force, Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307:2526\u0026ndash;33. https://doi.org/10.1001/jama.2012.5669\u003c/li\u003e\n\u003cli\u003eKnaus WA, Draper EA, Wagner DP, Zimmerman JE. Apache II: A severity of disease classification system. Crit Care Med. 1985;13:818\u0026ndash;29. https://doi.org/10.1097/00003246-198510000-00009\u003c/li\u003e\n\u003cli\u003eVincent JL, Moreno R, Takala J, Willatts S, De Mendon\u0026ccedil;a A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22:707\u0026ndash;10. https://doi.org/10.1007/BF01709751\u003c/li\u003e\n\u003cli\u003eZhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17. https://doi.org/10.2202/1544-6115.1128\u003c/li\u003e\n\u003cli\u003eAgarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. eLife. 2015;4:e05005. https://doi.org/10.7554/eLife.05005\u003c/li\u003e\n\u003cli\u003eHuang HY, Lin YCD, Li J, Huang KY, Shrestha S, Hong HC, et al. miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res. 2020;48:D148\u0026ndash;D54. https://doi.org/10.1093/nar/gkz896\u003c/li\u003e\n\u003cli\u003eBarbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462:108\u0026ndash;12. https://doi.org/10.1038/nature08460\u003c/li\u003e\n\u003cli\u003eZhang W, Chang G, Cao L, Ding G. Dysregulation of serum miR-361-5p serves as a biomarker to predict disease onset and short-term prognosis in acute coronary syndrome patients. BMC Cardiovasc Disord. 2021;21:74. https://doi.org/10.1186/s12872-021-01891-0\u003c/li\u003e\n\u003cli\u003eWang F, An Y, Hao H. MicroRNA-361-5p acts as a biomarker for carotid artery stenosis and promotes vascular smooth muscle cell proliferation and migration. BMC Med Genomics. 2023;16:134. https://doi.org/10.1186/s12920-023-01563-2\u003c/li\u003e\n\u003cli\u003eLi M, Yang J, Wu Y, Ma X. miR-186-5p improves alveolar epithelial barrier function by targeting the wnt5a/\u0026beta;-catenin signaling pathway in sepsis-acute lung injury. Int Immunopharmacol. 2024;131:111864. https://doi.org/10.1016/j.intimp.2024.111864\u003c/li\u003e\n\u003cli\u003eWang F, Ge R, Cai Y, Zhao M, Fang Z, Li J, et al. Oxidative stress in ARDS: mechanisms and therapeutic potential. Front Pharmacol. 2025;16:1603287. https://doi.org/10.3389/fphar.2025.1603287\u003c/li\u003e\n\u003cli\u003eMagnani ND, Dada LA, Sznajder JI. Ubiquitin-proteasome signaling in lung injury. Transl Res. 2018;198:29\u0026ndash;39. https://doi.org/10.1016/j.trsl.2018.04.003\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 456px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of the ARDS cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003ePatients with ARDS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003en=34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eAge, years, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(66\u0026ndash;79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eSex, male, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eBMI, median, (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(20.5\u0026ndash;25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eCoexisting disease, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eChronic lung disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eRenal insufficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eImmunocompromise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eCardiovascular compromise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eMalignant neoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eMain cause of ARDS, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eVirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(73.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eBacterial pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eExacerbation of interstitial pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eSeverity of disease on admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eAPACHE Ⅱ, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(10\u0026ndash;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eSOFA, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(3\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eSeverity of ARDS, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eDisease course\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eLength of mechanical ventilation, days, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(7\u0026ndash;22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eLength of stay in hospital, days, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(13\u0026ndash;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 371px;\"\u003e\n \u003cp\u003eHospital mortality, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 569px;\"\u003e\n \u003cp\u003eARDS: acute respiratory distress syndrome, IQR: interquartile range, BMI: body mass index, APACHE II: Acute Physiology and Chronic Health Evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ARDS, microRNA, mRNA, transcriptome, regulatory network","lastPublishedDoi":"10.21203/rs.3.rs-8776914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8776914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute respiratory distress syndrome (ARDS) is associated with high mortality and complex pathophysiology, yet molecularly targeted therapies remain undeveloped. In particular, the microRNA (miRNA)\u0026ndash;mRNA regulatory network underlying ARDS are poorly understood. This study aimed to elucidate the miRNA\u0026ndash;mRNA interactions associated with the pathophysiology of ARDS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003emRNA-Seq and miRNA-Seq were performed in 34 patients with ARDS and healthy controls. Gene and miRNA co-expression modules were constructed using Weighted Gene Co-expression Network Analysis. miRNA\u0026ndash;mRNA regulatory relationships were inferred through an integrated analysis of predicted and experimentally validated miRNA targets. Molecular signatures were quantified via single-sample gene set enrichment analysis, and module structure preservation was evaluated in an external pneumonia cohort.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA key mRNA co-expression module was identified that exhibited the strongest negative correlation with the P/F ratio, along with a negatively correlated miRNA co-expression module. The miRNA module, centered on miR-361-5p and miR-186-5p, formed a regulatory network broadly controlling gene clusters involved in ubiquitin ligase activity and cellular stress response pathways. This network demonstrated a strong association with the P/F ratio and showed extremely high structural preservation in the external pneumonia cohort.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA miRNA\u0026ndash;mRNA regulatory network linked to impaired oxygenation in patients with ARDS has been identified. The network highlights miRNAs as potential key regulators of disease progression and suggests their utility as biomarkers of disease severity and prospective therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Identification of Oxygenation Impairment–Associated Gene Networks in ARDS Through Integrated mRNA and miRNA Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 16:46:23","doi":"10.21203/rs.3.rs-8776914/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-18T09:42:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T16:26:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143531177028325787807460077544951501518","date":"2026-02-23T11:44:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T20:41:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-04T18:44:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T14:34:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2026-02-03T13:30:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"901d2f4e-acbf-471d-9093-5a69047d3190","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T15:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 16:46:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8776914","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8776914","identity":"rs-8776914","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}