Dissecting the Role of NETosis-Related Biomarkers in Sepsis: An Integrated Multi-Dataset Analysis for Diagnostic and Prognostic Applications

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The formation of Neutrophil Extracellular Traps (NETs) through a process known as NETosis has been identified as a significant contributor to the development of sepsis. This study aimed to dissect the roles of NETosis-related genes, particularly Myeloperoxidase (MPO) and Proteinase 3 (PRTN3), in sepsis progression. By integrating and analyzing multiple Gene Expression Omnibus (GEO) datasets, we conducted a comprehensive gene expression profiling that revealed consistent downregulation of MPO and PRTN3, among others, in sepsis patients. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, we characterized the biological functions and pathways associated with these genes, emphasizing their relevance to immune responses in sepsis. A prediction model utilizing these biomarkers was constructed using a Random Forest classifier, which demonstrated robust predictive capability, as reflected by an AUROC of 0.77 for training and 0.68 for validation datasets. Survival analysis further underscored the prognostic value of demographic factors, particularly gender and age. The model highlighted gender-specific survival rates and revealed a significant decline in survival probability in patients over 40 years of age. These findings illuminate the diagnostic and prognostic potential of MPO and PRTN3 in sepsis, offering novel insights into the molecular dynamics of the disease and suggesting a direction for future therapeutic strategies. The study's integrated approach and novel findings advocate for personalized management of sepsis, tailoring interventions to individual patient profiles to improve outcomes. NETosis MPO PRTN3 Gene Ontology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Sepsis, a critical medical condition characterized by a dysregulated host response to infection, remains a formidable global health challenge with significant morbidity and mortality rates. Despite advancements in healthcare, sepsis continues to impose a heavy burden on healthcare systems worldwide. Its clinical manifestations can range from mild Systemic Inflammatory Response Syndrome (SIRS) to severe sepsis and septic shock, the latter being associated with profound circulatory failure and a high mortality rate.[ 1 – 3 ] At the heart of its pathogenesis is the complex interplay between the invading pathogen and the host's immune system, resulting in systemic inflammation, tissue damage, and multi-organ dysfunction.[ 4 ]Despite extensive research efforts, the precise mechanisms driving sepsis pathophysiology remain incompletely understood, hindering the development of effective therapeutic strategies.[ 5 ] Neutrophil Extracellular Traps (NETs), formed through a process known as NETosis, have emerged as key players in the pathophysiology of sepsis.[ 6 – 8 ] NETs are web-like structures composed of chromatin fibers and antimicrobial proteins released by neutrophils to ensnare and neutralize invading pathogens. This process, initiated in response to microbial pathogens or inflammatory stimuli, serves as a mechanism for trapping and killing microbes.[ 9 – 11 ] While initially considered beneficial for host defense, dysregulated NETosis in sepsis can exacerbate tissue injury, promote thrombosis, and contribute to organ dysfunction.[ 12 – 14 ] Among the plethora of genes implicated in innate immune responses and inflammation, the roles of the genes encoding Myeloperoxidase (MPO) and Proteinase 3 (PRTN3) are particularly noteworthy.[ 8 , 15 , 16 ] Expressed predominantly in neutrophils, these genes are central to the body's defenses against infections and play crucial roles in regulating inflammatory processes. MPO, encoded by the MPO gene located on chromosome 17, is a heme-containing enzyme abundantly present in the azurophilic granules of neutrophils. MPO plays a pivotal role in host defense mechanisms by producing microbicidal reactive oxygen species (ROS) and contributing to the formation of NETs.[ 17 ]The dysregulation of MPO expression has been linked to a variety of inflammatory conditions, in diseases such as atherosclerosis, MPO levels are elevated, contributing to the oxidative stress and inflammation characteristic of the disease.[ 18 ] In rheumatoid arthritis, MPO-derived oxidants are thought to contribute to joint damage and the chronic inflammatory state. Interestingly, in cancer, MPO's role is more nuanced, with studies suggesting both pro-tumorigenic and anti-tumorigenic effects depending on the tumor microenvironment and the stages of cancer development. This dual role underscores the complexity of MPO's functions in human diseases.[ 19 – 22 ] Understanding the intricate relationship between sepsis and NETosis is crucial for unraveling the pathophysiology of this complex syndrome and identifying potential therapeutic targets.[ 23 , 24 ]Further research into the molecular mechanisms governing NET formation and its modulation in sepsis holds promise for the development of novel interventions aimed at improving patient outcomes.[ 14 ]In this study, we have embarked on a comprehensive analysis, integrating multiple databases to investigate the shared variations of NETosis-related genes in sepsis. (Fig. 1 ) Our findings illuminate the potential roles of these genes in diagnosis, prognosis, and immune infiltration, underscoring their significance in the intricate landscape of sepsis pathophysiology. Importantly, through this integrated database analysis, we identified two potential biomarkers, MPO and PRTN3, which exhibited downregulated expression across diverse datasets. Moreover, we established an in vitro model of NETs-induced endothelial cell alterations to further explore the dynamics of MPO expression in the context of NETosis. Intriguingly, we noted a decrease in MPO expression following NETs induction. To investigate the potential effects of MPO in this context, we increased MPO expression in the model, which led to the induction of endothelial cell apoptosis. This observation proposes that in the progression of sepsis, heightened MPO levels may play a role in moderating the condition, possibly by influencing neutrophil behavior to alleviate the progression of the disease. These results not only advance our understanding of the molecular underpinnings of sepsis but also pave the way for developing targeted therapeutic strategies focusing on the modulation of NETosis and its associated pathways. The validated genes were considered potential biomarkers and used to construct a Random Forest classifier, a machine learning model capable of distinguishing sepsis patients from controls. This classifier was then validated with an independent dataset, GSE134347 (n = 239). Methods and materials 3.1 GEO datasets collection To investigate the gene expression alterations associated with sepsis, we meticulously selected and analyzed data from the Gene Expression Omnibus (GEO) database. Specifically, we focused on datasets GSE54514, GSE137342, and GSE95233. GSE54514 encompasses gene expression profiles from 36 normal and 127 sepsis-affected samples, GSE137342 contains data from 14 normal and 43 sepsis samples, and GSE95233 comprises 22 normal and 102 sepsis samples. This comprehensive dataset selection provided a robust foundation for identifying genes differentially expressed in sepsis compared to healthy control states. 3.2 Differential Expression Analysis To quantify the differential expression, we utilized the "limma" package within the R software environment, a powerful tool for analyzing expression data through linear models. Our criteria for identifying significant differentially expressed genes (DEGs) were stringent, requiring a |log2 fold change| > 0.5 and a P-value 0. 5 and adjusted p-value < 0.05 are consider as DEGs. For visual representation of the DEGs, we employed the "ggplot2" package to generate informative volcano plots, which visually contrast the magnitude of expression changes against the statistical significance. Additionally, the "pheatmap" package was used to create heatmaps, offering a heatmap representation of the DEGs' expression patterns across the selected datasets. 3.3 Identification of NETosis-Related Genes and Their Intersection with Sepsis-Associated DEGs To delineate the genetic underpinnings of NETosis in the context of sepsis, we embarked on a comprehensive search for NETosis-related genes using the GeneCards database ( https://www.genecards.org/ ). We identified a list of 76 genes implicated in the NETosis process. To pinpoint genes at the intersection of NETosis and sepsis, we utilized the FunRich software, a versatile tool for functional enrichment and interaction network analysis. Through the generation of Venn diagrams, we meticulously mapped out the overlap between the 76 NETosis-related genes and differentially expressed genes (DEGs) identified in our sepsis datasets. 3.4 GO and KEGG enrichment pathway analysis Using the clusterProfiler and enrichplot packages in R software (version 4.1.2), we conducted GO (Gene Ontological Analysis, GO), including biological processes (BPs), molecular functions (MFs), and cellular components (CCs), and KEGG (Kyoto Encyclopedia of Genes and Genomes pathway analysis, KEGG) enrichment pathway analysis to better understand the biological functions of DEGs and NETosis subset genes. GSEA (Gene Set Enrichment Analysis) were performed on GSE54514, GSE137342, and GSE95233 dataset separately. We selected the most enriched entries in GO, KEGG, and GSEA using an adjusted threshold of p < 0.05 for screening. 3.5 Construction and Analysis of the Protein-Protein Interaction Network To unravel the intricate interactions among NETosis-related genes, we constructed a Protein-Protein Interaction (PPI) Network, leveraging data from the STRING database. Following data retrieval from STRING, we employed the MCODE (Molecular Complex Detection) plugin in Cytoscape software for the visualization and in-depth analysis of the PPI network. Genes within this highly interconnected subnetwork were classified as hub genes, signifying their potential central role in NETosis and their influence on the pathophysiology of sepsis. 3.5Construction of a Prediction Model Using NETosis-Related Genes To assess the diagnostic potential of critical NETosis gene MPO, we employed receiver operating characteristic (ROC) analysis. This statistical method, facilitated by the scikit-learn Python package, allows for the calculation of the Area Under the Curve (AUC), providing a measure of the model's diagnostic accuracy. Leveraging the capabilities of scikit-learn, we developed a Random Forest classifier based on the expression levels of MPO. 3.6 Survival Analysis Based on GSE95233 Dataset Utilizing the GSE95233 dataset, which includes data from 22 normal and 102 sepsis samples, we conducted a survival analysis to explore the prognostic implications of sepsis. This analysis, carried out with the survminer and survival packages in R, differentiates between survival statuses, coded as 0 for survivors and 1 for non-survivors. Kaplan-Meier survival plots were generated to dissect the impact of demographic factors on sepsis outcomes, specifically examining the survival rates across different gender and age groups. Individuals aged 40 and below were classified as young, while those above 40 were considered old, allowing for an evaluation of age as a determinant of survival in sepsis patients. 3.7 Cell culture and treatments Human umbilical vein endothelial cells (HUVECs) were acquired from the American Type Culture Collection (ATCC, Manassas, USA). Cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) from Gibco, supplemented with 5 mM glucose, 10% fetal bovine serum (FBS, Gibco), and penicillin/streptomycin (100 g/ml, Gibco). Cells were incubated at 37°C in a humidified atmosphere containing 5% CO 2 and replenished the culture medium every 2–3 days to maintain optimal growth conditions. Cells were divided into control group (PBS) and MPO group (sigma, M6908). Cells were treated with MPO at 0.4 µg/ml. 3.8 Quantitative RT-PCR Total RNA from cells was obtained according to the instructions of the miRNeasy Mini Kit (Biyuntian, N-103, China). Total RNA was converted to cDNA using PrimerScript RT reagent kit (Takara, Japan). qRT-PCR was performed with SYBR Premix Ex Taq reagent kit (Takara, Japan). The sequence used by the target genes were as follows: Table 1 GAPDH、Bax、caspase-3 Primer Sequences Name Primer Sequences GAPDH Forword: 5’- CAAGGTCATCCATGACAACTTTG − 3’ Reverse: 5’- GTCCACCACCCTGTTGCTGTAG − 3’ Bax Forword: 5’- TGGCAGCTGACATGTTTTCTGAC − 3’ Reverse: 5’- CACCCAACCACCCTGGTCTT − 3’ caspase-3 Forword: AGCTACGAATCTCCGACCAC Reverse: CGTTATCCCATGTGTCGAAGAA 3.9 Western blotting Cell lysates were prepared using RIPA buffer (Beyotime Biotechnology, cat# P0013B), and proteins were denatured at 100 ℃ for 5 minutes. Proteins were then equally loaded and electrophoretically separated on an SDS-PAGE, followed by transfer onto PVDF membranes (GE Health). Membranes were blocked using 5% non-fat milk for 1 hour and subsequently incubated with primary antibodies against Cleaved Caspase-3 (#9661, 1:500) and Tubulin (#2146, 1:2000) from Cell Signaling Technology, USA, overnight. After incubation with species-specific secondary antibodies (1:5000, Immunoway, USA) for 1 hour at room temperature, detection was carried out using the ChemiDoc™ MP E-Gel system (Bio-Rad, USA). Quantitative analysis of the protein bands was performed using ImageJ software. 3.10 Colony formation experiment The cells were seeded into 6-well plates at a density of 1000 cells per well, followed by incubation at 37℃ with 5% CO 2 under saturated humidity. Cells were cultured for 7 days and the medium was changed every 2 days. The cells were gently washed twice with PBS and stained with crystal purple dye (containing fixed solution, C8470, solarbio, China) for 30 min at room temperature. 3.11 Statistical analysis All analyses were conducted using the R version 4.1.2. Two-group comparison was performed using the Wilcoxon rank sum test 35 when analyzing significance between various values (expression levels, infiltration ratios, and various features), The symbols "ns", "*", " **", "***"and "****" were used to indicate the levels of statistical significance, where "ns" indicated p > 0.05, * indicated p < 0.05, ** indicated p < 0.01, *** indicated p < 0.001, and **** indicated p < 0.0001. Results 4.1 Differential Gene Expression Analysis across Multiple Sepsis Studies Our comprehensive analysis of gene expression profiles from sepsis patients yielded a significant number of differentially expressed genes (DEGs). In the GSE54514 dataset, we identified 81 DEGs, with 50 genes upregulated and 31 downregulated. Extending our analysis to the GSE137342 dataset, we found a substantial alteration in the gene expression landscape, evident from the 4025 DEGs identified—62 upregulated and 3963 downregulated. Similarly, the GSE95233 dataset revealed 3359 DEGs, comprising 1539 upregulated and 1820 downregulated genes. (Fig. 2 A-C) The visualization of these DEGs through volcano plots and heatmaps (Fig. 2 D-F) underscores the extensive transcriptional changes occurring in sepsis. Figure 2 A-C depict the DEGs from GSE54514, GSE137342, and GSE95233, respectively, showcasing the upregulated (red dots) and downregulated (blue dots) genes. Through a meticulous intersection analysis using Venn diagrams, we distilled the data to identify critical genes consistently dysregulated across all datasets. (Fig. 2 G-H) This revealed 18 DEGs commonly downregulated: UQCRQ, PRTN3, RETN, CTSG, PGLYRP1, MPO, CEACAM6, RNASE2, DEFA4, S100P, IFI27 , and RNASE3 . Furthermore, we pinpointed genes uniquely upregulated in overlapping datasets: ACTG1 in both GSE54514 and GSE137342; MUC6, ASCC2, RPLP1, TRIM58, USP10, RNF213, PTMA, TSPAN5, RPL10A in both GSE54514 and GSE95233; and a set of 36 DEGs, including JAK1, SAMD3 , and CD96 , were found upregulated in both GSE137342 and GSE95233. 4.2 Functional Enrichment Analysis of Downregulated NETosis Genes We further delved into the biological functions of genes that were consistently downregulated across multiple datasets. The GO analysis highlighted critical biological processes involved in NETosis, such as the acute inflammatory response, response to molecule of bacterial origin, neutrophil migration, positive regulation of defense response, and neutrophil chemotaxis. KEGG pathway enrichment revealed associations with diseases and processes linked to NETosis, including neutrophil extracellular trap formation and lipid metabolism-related atherosclerosis. Further dissection of the GSE54514 dataset through a chord chart provided a visual representation of the interplay between genes and their respective GO categorizations, emphasizing the biological process, cellular components, and molecular functions of these genes. (Fig. 3 A-B) KEGG pathway analysis of GSE54514highlighted two critical pathways: Neutrophil extracellular trap formation and Lipid and atherosclerosis, as shown in Figs. 3 C and D. The pathway enrichment analysis showed that Neutrophil extracellular trap formation is predominant with a higher count of genes involved, alongside significant p-values indicating the relevance of these pathways in the study context. The chord diagrams in Figs. 3 E-G depict the comprehensive gene ontology terms categorized into biological processes, cellular components, and molecular functions. These figures illustrate the complex interplay between various GSE54514-related genes and the ontology terms. For instance, Figure E focuses on the biological processes, showing a vibrant array of connections that reflect the dynamic nature of the biological mechanisms in play. Figure F emphasizes the cellular components, where genes are linked to specific cellular locations, highlighting their potential roles within the cellular architecture. Lastly, Figure G delineates the molecular functions, demonstrating how genes contribute to the functional portfolio of the cell. 4.3 Functional pathway Enrichment Analysis of Downregulated NETosis Genes The ridge plots (Figs. 4 A-C) presented the log2 fold changes of core genes, revealing both upregulated (positive values) and downregulated (negative values) genes within the enriched pathways. Gene Set Enrichment Analysis (GSEA) across the datasets of GSE54514, GSE137342, and GSE95233 demonstrated significant pathway enrichments. Notably, the GSE54514 dataset (Figs. 4 D-F) showed enrichment in pathways such as chemical carcinogenesis-reactive oxygen species, diabetic cardiomyopathy, and lysosome-related processes. Similar pathways were observed in the GSE134347 analysis, while GSE95233 showcased pathways pivotal in immune response, such as antigen processing and presentation, and autoimmune thyroid disease. 4.4 Identification of MPO and PRTN3 downregulation in Sepsis progression Through Protein-Protein Interaction (PPI) analysis, we identified five key hub genes associated with NETosis: MPO , PRTN3 , CAMP , SERPINA1 , and ELANE . (Fig. 5 A) MPO and PRTN3 emerged as significantly downregulated genes (p < 0.01), underscoring their potential role as biomarkers in NETosis. This finding is aligned with the overlap analysis, which revealed that MPO and PRTN3 are the common genes downregulated across the datasets GSE137342, GSE54514, and GSE95233 when cross-referenced with NETosis-related genes from the Gene Cards database (Fig. 5 B). The violin plots further illustrate the differences in expression levels of these genes between sepsis patients and normal controls within the GSE54514 dataset (Figs. 5 C-F). MPO and PRTN3 exhibited a significant decrease in expression levels in sepsis patients, validating the differential analysis performed on the GSE54514 dataset. In contrast, CAMP and SERPINA1 did not show significant expression changes, and notably, ELANE was absent from the GSE54514 dataset. These results emphasize the importance of MPO and PRTN3 as key contributors to the NETosis pathway in the pathogenesis of sepsis. 4.5 Evaluation of NETosis Gene-Based Prediction Model and Survival Analysis The construction of a Random Forest prediction model based on two NETosis-related genes was validated using datasets GSE54514 and GSE134347. For the training set GSE54514, the model achieved an AUROC of 0.77, indicating a strong discriminatory capacity with a train-test split of 80 percent for training and 20 percent for testing. The validation on dataset GSE134347 yielded an AUROC of 0.68, suggesting a good predictive performance of the model (Fig. 6 A-B). In survival analysis, gender emerged as a significant predictor of survival in sepsis patients. The Kaplan-Meier survival curves show that there is a considerable discrepancy in survival rates between genders, with a p-value of 0.00034 (Fig. 6 C). Moreover, age proved to be another crucial factor, with patients older than 40 years showing a significantly decreased chance of survival compared to younger patients, as depicted by the survival curves in Fig. 6 D. This age-related difference in survival probability was statistically significant with a p-value of 0.026, underscoring the importance of age as a prognostic factor in sepsis. 4.6 Implications of MPO Induction on Endothelial Cell Apoptosis and Proliferation In our exploration of the role of MPO, a key protease expressed following neutrophil activation, we established an in vitro model by introducing purified MPO protein to HUVEC cultures. The results depicted in Fig. 7 illustrate the effects of MPO on endothelial cells. Treatment with MPO was associated with increased expression of apoptosis-inducing genes, as demonstrated by the upregulation of caspase-3 and BAX2 (Fig. 7 B). Furthermore, western blot analysis confirmed the elevated levels of Cleaved-Caspase3 in the presence of MPO, corroborating the gene expression data (Fig. 7 C). This suggests that MPO may contribute to apoptosis in endothelial cells, a process potentially relevant in the pathogenesis of sepsis. Additionally, colony formation capability was notably diminished in HUVECs treated with MPO (Fig. 7 D), indicating that MPO exposure may impair the proliferative capacity of endothelial cells. Together, these findings support the hypothesis that MPO plays a role in endothelial cell dysfunction during sepsis, and targeting neutrophil responses could be a viable approach to ameliorating sepsis outcomes. Discussion This study represents a multi-dimensional approach to understanding the role of NETosis in the pathophysiology of sepsis—a condition characterized by an overwhelming and life-threatening host response to infection. Our findings have significant implications for the identification of potential biomarkers and therapeutic targets, which could be pivotal in the management and prognosis of sepsis. The crux of our investigation centered on the analysis of NETosis-related genes and their expression patterns in sepsis patients. In particular, the genes MPO and PRTN3 were highlighted as key biomarkers due to their consistent downregulation across several independent datasets. These genes are known to play vital roles in the immune response, and their dysregulation might be intricately linked to the exacerbated inflammatory state observed in sepsis.[ 28 , 29 ] The decreased expression levels of MPO and PRTN3, validated by our rigorous analytical process, suggest their utility as indicators of sepsis progression and potential targets for therapeutic intervention. The Random Forest model we constructed, which was validated by the AUROC, showed considerable promise in distinguishing sepsis patients from healthy controls.[ 30 ] This model's high performance, as indicated by the AUROC of 0.77 for the training set and 0.68 for the validation set, underlines the robustness of the identified biomarkers in predicting sepsis. This finding opens up new avenues for the development of diagnostic tools that are both sensitive and specific. Furthermore, our survival analysis brought to light the significance of gender and age as determinants of sepsis outcomes. The marked difference in survival probabilities between genders, and the lowered survival rates observed in patients over 40 years of age, underscore the need for personalized approaches in the management of sepsis. These demographic factors could guide clinicians in risk stratification and in tailoring interventions to improve patient outcomes. Overall, our integrated analysis not only sheds light on the molecular underpinnings of sepsis but also propels us towards a more targeted approach in sepsis therapy. While the establishment of an in vitro NETs-induced endothelial cell model highlights the dynamic expression of MPO following NETs induction, it suggests a potential pathway through which sepsis exerts its harmful effects. [ 31 , 32 ]Additional stimulation with MPO, or the upregulation of MPO, has been observed to promote apoptosis in endothelial cells, suggesting that stimulating neutrophils could help mitigate disease progression in sepsis patients. In conclusion, the insights gleaned from this study enrich our understanding of sepsis and lay the groundwork for the development of novel diagnostics and therapeutics. Our research underscores the complexity of sepsis and the need for continued investigation into the multifaceted roles of NETosis in this intricate disease. Moving forward, we advocate for further studies to confirm these findings in larger cohorts and to explore the therapeutic potential of modulating MPO and PRTN3 expression in sepsis. Conclusions This comprehensive study underscores the pivotal roles of Myeloperoxidase (MPO) and Proteinase 3 (PRTN3) in the pathophysiology of sepsis, elucidating their potential as diagnostic and prognostic biomarkers. Through integrative analysis of multiple Gene Expression Omnibus datasets and in vitro modeling, we demonstrated a consistent downregulation of MPO and PRTN3 in sepsis patients, underscoring their significance in the disease's progression. The construction of a prediction model utilizing a Random Forest classifier showed substantial diagnostic potential, as evidenced by AUROC scores of 0.77 and 0.68 for the training and validation datasets, respectively. Our findings also reveal gender and age as critical prognostic factors influencing sepsis outcomes. The distinct survival probabilities between genders and the sharp decrease in survival rates in patients over the age of 40 call for a personalized approach to sepsis treatment, where patient-specific characteristics guide clinical decision-making.The discovery that modulating MPO levels in an in vitro NETs-induced endothelial cell model can induce apoptosis proposes a novel mechanism by which the regulation of NETosis-related genes may influence the severity of sepsis. This revelation invites further exploration into the therapeutic modulation of MPO and PRTN3, which may offer a new frontier in ameliorating the detrimental effects of sepsis. Declarations Conflicts of Interests The authors declare that they have no competing interests. Funding information Not applicable. Author Contribution Aili Fang conceived, devised the study, performed the bioinformatics analysis, found the related data set and analysis tool, and wrote the manuscript. The author contributed to the article and approved the submitted version. Data availability statement The datasets used to support the findings of this study are available from GEO ( http://www.ncbi.nlm.nih.gov/geo ) database. All data can be acquired from the corresponding authors on request. References Nie D, Chen C, Li Y, Zeng C (2022) Disulfiram, an aldehyde dehydrogenase inhibitor, works as a potent drug against sepsis and cancer via NETosis, pyroptosis, apoptosis, ferroptosis, and cuproptosis. Blood Sci 4(3):152–154 Zhu CL, Wang Y, Liu Q, Li HR, Yu CM, Li P et al (2022) Dysregulation of neutrophil death in sepsis. Front Immunol 13:963955 Ou Q, Tan L, Shao Y, Lei F, Huang W, Yang N et al (2022) Electrostatic Charge-Mediated Apoptotic Vesicle Biodistribution Attenuates Sepsis by Switching Neutrophil NETosis to Apoptosis. Small 18(20):e2200306 Delano MJ, Ward PA (2016) The immune system's role in sepsis progression, resolution, and long-term outcome. 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Lipids Health Dis 17(1):71 Wang C, Zhao Y, Jin B, Gan X, Liang B, Xiang Y et al (2021) Development and Validation of a Predictive Model for Coronary Artery Disease Using Machine Learning. Front Cardiovasc Med 8:614204 Folco EJ, Mawson TL, Vromman A, Bernardes-Souza B, Franck G, Persson O et al (2018) Neutrophil Extracellular Traps Induce Endothelial Cell Activation and Tissue Factor Production Through Interleukin-1alpha and Cathepsin G. Arterioscler Thromb Vasc Biol 38(8):1901–1912 Kolarova H, Vitecek J, Cerna A, Cernik M, Pribyl J, Skladal P et al (2021) Myeloperoxidase mediated alteration of endothelial function is dependent on its cationic charge. Free Radic Biol Med 162:14–26 Additional Declarations No competing interests reported. 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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-4229642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288760543,"identity":"83fff64b-f13f-4e09-b983-27fc3f91696e","order_by":0,"name":"爱莉 方","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBAC+2bGxgcSPBLM8uwNRGoxYGc+bGAhY8Nu2HOAWC38bGkSFTZp/Aw3EojUYs7MYyBxI+ewNOPMxxtvMNTYRBPUYtnMY2A448xhY3bptGILhmNpuQ0E9RzmMUiW7DmczDg7x0yCseEwcVoO//13uL7h5hkitRgcZktskOBJY2a4wUOkFslm5sMMEjw2zIY9QL8kEOMXfv6D7T8gUXl4440PNTZE+AXZkRIJpCiHaCFVxygYBaNgFIwMAAA4nTvIVX114gAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"爱莉","middleName":"","lastName":"方","suffix":""}],"badges":[],"createdAt":"2024-04-07 04:59:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4229642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4229642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54521667,"identity":"2cd6f66f-94ee-43ad-a604-210f6f8946f7","added_by":"auto","created_at":"2024-04-11 18:17:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66832,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the analytical process for identifying NETosis-Related biomarkers in sepsis. Initially, three GEO datasets, GSE137342 (n = 57), GSE54514 (n = 163), and GSE95233 (n = 124), were subjected to differential gene expression analysis.(25-27)Concurrently, NETosis-related genes were extracted from the GeneCards database. These two data streams were integrated to identify 18 downregulated NET-related genes, which underwent further analysis.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-4229642/v1/25d6e9198e1bf3175a9116f6.png"},{"id":54521670,"identity":"097ab2aa-7282-48e9-b1bf-7ea84ef54169","added_by":"auto","created_at":"2024-04-11 18:17:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":317517,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive Analysis of Gene Expression and Identification of Differentially Expressed Genes (DEGs) in Sepsis.\u003c/p\u003e\n\u003cp\u003eA-C) Volcano plots representing the differential gene expression in sepsis across three GEO datasets. Red dots indicate upregulated genes, and blue dots represent downregulated genes, against the magnitude of log2 fold change on the x-axis and -log10 p-value on the y-axis.\u003c/p\u003e\n\u003cp\u003eA) GSE54514, B) GSE137342, C) GSE95233.\u003c/p\u003e\n\u003cp\u003eD-F) Heatmaps showcasing expression profiles of DEGs for the corresponding GEO datasets with hierarchical clustering. Colors range from blue (lower expression) to red (higher expression).\u003c/p\u003e\n\u003cp\u003eD) GSE54514, E) GSE137342, F) GSE95233.\u003c/p\u003e\n\u003cp\u003eG) Venn diagram summarizing the intersection of upregulated genes from datasets GSE54514, GSE137342, GSE95233, identifying genes consistently upregulated in sepsis.\u003c/p\u003e\n\u003cp\u003eH) Venn diagram displaying the overlap of downregulated genes across the datasets GSE54514, GSE137342, GSE95233, illustrating the common and unique DEGs in each study.\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-4229642/v1/318f941af836144ef4262c67.png"},{"id":54521674,"identity":"506a0168-0f55-46fa-8afa-4e251fa71b6e","added_by":"auto","created_at":"2024-04-11 18:17:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":360487,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Enrichment Analysis of Downregulated NETosis Genes and Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eA-B) Bar plots illustrating the Gene Ontology (GO) enrichment of biological processes, cellular components, and molecular functions for the 18 downregulated NETosis genes. Each bar represents a GO term, with its length proportional to the enrichment score, and color-coded by p-value.\u003c/p\u003e\n\u003cp\u003eC) Heatmap representing the KEGG pathway analysis of GSE54514 dataset. The color intensity indicates the significance level of pathways enriched in the dataset, with red representing the most significant pathways.\u003c/p\u003e\n\u003cp\u003eD) Dot plot showing the KEGG pathway enrichment of GSE54514 dataset. Pathways are represented along the y-axis, with the size of the dots corresponding to the gene count and the color indicating the adjusted p-value.\u003c/p\u003e\n\u003cp\u003eE) Chord diagram of the biological process ontology for the GSE54514 dataset. Colored ribbons connect genes to their associated biological processes, with the thickness of the ribbons indicating the strength of the association.\u003c/p\u003e\n\u003cp\u003eF) Chord diagram of the cellular component ontology for the GSE54514 dataset. It depicts the association of genes with cellular components, highlighting the intra-cellular distribution of gene expression.\u003c/p\u003e\n\u003cp\u003eG) Chord diagram of the molecular function ontology for the GSE54514 dataset. This diagram illustrates the molecular activities associated with the genes in the study, underlining the functional aspects they may influence.\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-4229642/v1/7cf1f61b7302773978cb3f04.png"},{"id":54522143,"identity":"a807fd14-5270-4335-95cc-b3f77eb36d6d","added_by":"auto","created_at":"2024-04-11 18:25:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":221960,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Enrichment Analysis of Downregulated NETosis Genes\u003c/p\u003e\n\u003cp\u003eA-C) Ridge plots depicting the distribution of the log2 fold changes of genes within key pathways from the KEGG analysis, with peaks representing enriched terms in the datasets analyzed.\u003c/p\u003e\n\u003cp\u003eD-F) GSEA plots for the GSE54514, GSE137342, and GSE95233 datasets, showing the enrichment scores across different gene sets related to NETosis processes and diseases.\u003c/p\u003e","description":"","filename":"Slide4.png","url":"https://assets-eu.researchsquare.com/files/rs-4229642/v1/3c6485f12c652f670f890427.png"},{"id":54521669,"identity":"1062213e-bed1-4c19-8428-d7b39ebfec45","added_by":"auto","created_at":"2024-04-11 18:17:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":103253,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of \u003cem\u003eMPO\u003c/em\u003e and \u003cem\u003ePRTN3\u003c/em\u003e downregulation in NETosis progression\u003c/p\u003e\n\u003cp\u003eA) The PPI network highlights five central hub genes:\u003cem\u003e MPO, PRTN3, CAMP, SERPINA1\u003c/em\u003e, and \u003cem\u003eELANE\u003c/em\u003e, pivotal in the NETosis process as identified from the dataset GSE54514.\u003c/p\u003e\n\u003cp\u003eB) Venn diagram demonstrating the overlap of downregulated genes from datasets GSE137342, GSE54514, and GSE95233 with NETosis-related genes from Gene Cards, indicating a significant convergence on key genes.\u003c/p\u003e\n\u003cp\u003eC-F) Violin plots displaying the expression levels of \u003cem\u003eMPO\u003c/em\u003e (D), \u003cem\u003eCAMP\u003c/em\u003e (E), and \u003cem\u003ePRTN3\u003c/em\u003e (F) and \u003cem\u003eSERPINA1(G)\u003c/em\u003ein the test group versus normal controls from the dataset GSE54514. Notably, \u003cem\u003eMPO\u003c/em\u003eand \u003cem\u003ePRTN3\u003c/em\u003e show statistically significant downregulation (p \u0026lt; 0.01), while the expression differences for \u003cem\u003eCAMP\u003c/em\u003e and \u003cem\u003eSERPINA1\u003c/em\u003e are not statistically significant (p \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"Slide5.png","url":"https://assets-eu.researchsquare.com/files/rs-4229642/v1/6129ce026ba78ea42af9eb8c.png"},{"id":54521671,"identity":"56721d54-0288-4c67-b2f4-f653757d942c","added_by":"auto","created_at":"2024-04-11 18:17:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":48889,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of NETosis Gene-Based Prediction Model and Survival Analysis by Gender and Age\u003c/p\u003e\n\u003cp\u003eA-B) ROC curves displaying the predictive performance of a Random Forest classifier constructed using two NETosis-related genes. The classifier's ability to discriminate sepsis patients from controls is quantified by the area under the ROC curve (AUROC).\u003c/p\u003e\n\u003cp\u003eA) shows the AUROC for the training dataset GSE54514, achieving an AUROC of 0.77.\u003c/p\u003e\n\u003cp\u003eB) presents the AUROC for the validation dataset GSE134347 with an AUROC of 0.68.\u003c/p\u003e\n\u003cp\u003eC-D) Kaplan-Meier survival curves stratified by gender (C) and age (D) for the dataset GSE134347.\u003c/p\u003e\n\u003cp\u003eC) indicates a significant difference in survival probability between genders with a p-value of 0.00034.\u003c/p\u003e\n\u003cp\u003eD) depicts survival probability based on age, highlighting that patients over 40 have a significantly lower survival probability compared to those aged 40 or below, with a p-value of 0.026.\u003c/p\u003e","description":"","filename":"Slide6.png","url":"https://assets-eu.researchsquare.com/files/rs-4229642/v1/cf74793ffaeedae259b33262.png"},{"id":54521673,"identity":"965da519-1e0f-40ab-9628-72f6ba215ae9","added_by":"auto","created_at":"2024-04-11 18:17:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":144159,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of Myeloperoxidase (MPO) on HUVEC Apoptosis and Colony Formation\u003c/p\u003e\n\u003cp\u003eA) Schematic representation of the in vitro MPO induction model in HUVECs. The NC group was treated with PBS, while the MPO group received purified MPO.\u003c/p\u003e\n\u003cp\u003eB) Bar graph showing the relative expression of apoptosis-related genes caspase-3 and BAX2 in HUVECs treated with MPO compared to the control.\u003c/p\u003e\n\u003cp\u003eC) Western blot analysis depicting the levels of Cleaved-Caspase3, with Tubulin as a loading control. The quantification on the right shows an increase in Cleaved-Caspase3 expression in the MPO-treated HUVECs.\u003c/p\u003e\n\u003cp\u003eD) Colony formation assay comparing the HUVECs treated with MPO to the control group, with quantification indicating a reduced colony formation rate in the MPO group.\u003c/p\u003e","description":"","filename":"Slide7.png","url":"https://assets-eu.researchsquare.com/files/rs-4229642/v1/10a60d4945dec69e9f2c368b.png"},{"id":54529406,"identity":"d82c0c42-cb15-409d-8406-1d75fbcf80d8","added_by":"auto","created_at":"2024-04-11 23:22:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1766694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4229642/v1/e312ff18-e096-4b58-9a0e-efd733d927c7.pdf"},{"id":54521668,"identity":"c33ee523-c3ea-4fe6-896d-9dc32143d4a5","added_by":"auto","created_at":"2024-04-11 18:17:58","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37030,"visible":true,"origin":"","legend":"","description":"","filename":"Slide8.png","url":"https://assets-eu.researchsquare.com/files/rs-4229642/v1/d5ac66d42fd5493bdf5694d5.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dissecting the Role of NETosis-Related Biomarkers in Sepsis: An Integrated Multi-Dataset Analysis for Diagnostic and Prognostic Applications","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis, a critical medical condition characterized by a dysregulated host response to infection, remains a formidable global health challenge with significant morbidity and mortality rates. Despite advancements in healthcare, sepsis continues to impose a heavy burden on healthcare systems worldwide. Its clinical manifestations can range from mild Systemic Inflammatory Response Syndrome (SIRS) to severe sepsis and septic shock, the latter being associated with profound circulatory failure and a high mortality rate.[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] At the heart of its pathogenesis is the complex interplay between the invading pathogen and the host's immune system, resulting in systemic inflammation, tissue damage, and multi-organ dysfunction.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]Despite extensive research efforts, the precise mechanisms driving sepsis pathophysiology remain incompletely understood, hindering the development of effective therapeutic strategies.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eNeutrophil Extracellular Traps (NETs), formed through a process known as NETosis, have emerged as key players in the pathophysiology of sepsis.[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] NETs are web-like structures composed of chromatin fibers and antimicrobial proteins released by neutrophils to ensnare and neutralize invading pathogens. This process, initiated in response to microbial pathogens or inflammatory stimuli, serves as a mechanism for trapping and killing microbes.[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] While initially considered beneficial for host defense, dysregulated NETosis in sepsis can exacerbate tissue injury, promote thrombosis, and contribute to organ dysfunction.[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAmong the plethora of genes implicated in innate immune responses and inflammation, the roles of the genes encoding Myeloperoxidase (MPO) and Proteinase 3 (PRTN3) are particularly noteworthy.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Expressed predominantly in neutrophils, these genes are central to the body's defenses against infections and play crucial roles in regulating inflammatory processes. MPO, encoded by the MPO gene located on chromosome 17, is a heme-containing enzyme abundantly present in the azurophilic granules of neutrophils. MPO plays a pivotal role in host defense mechanisms by producing microbicidal reactive oxygen species (ROS) and contributing to the formation of NETs.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]The dysregulation of MPO expression has been linked to a variety of inflammatory conditions, in diseases such as atherosclerosis, MPO levels are elevated, contributing to the oxidative stress and inflammation characteristic of the disease.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] In rheumatoid arthritis, MPO-derived oxidants are thought to contribute to joint damage and the chronic inflammatory state. Interestingly, in cancer, MPO's role is more nuanced, with studies suggesting both pro-tumorigenic and anti-tumorigenic effects depending on the tumor microenvironment and the stages of cancer development. This dual role underscores the complexity of MPO's functions in human diseases.[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eUnderstanding the intricate relationship between sepsis and NETosis is crucial for unraveling the pathophysiology of this complex syndrome and identifying potential therapeutic targets.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]Further research into the molecular mechanisms governing NET formation and its modulation in sepsis holds promise for the development of novel interventions aimed at improving patient outcomes.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]In this study, we have embarked on a comprehensive analysis, integrating multiple databases to investigate the shared variations of NETosis-related genes in sepsis. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) Our findings illuminate the potential roles of these genes in diagnosis, prognosis, and immune infiltration, underscoring their significance in the intricate landscape of sepsis pathophysiology. Importantly, through this integrated database analysis, we identified two potential biomarkers, MPO and PRTN3, which exhibited downregulated expression across diverse datasets. Moreover, we established an in vitro model of NETs-induced endothelial cell alterations to further explore the dynamics of MPO expression in the context of NETosis. Intriguingly, we noted a decrease in MPO expression following NETs induction. To investigate the potential effects of MPO in this context, we increased MPO expression in the model, which led to the induction of endothelial cell apoptosis. This observation proposes that in the progression of sepsis, heightened MPO levels may play a role in moderating the condition, possibly by influencing neutrophil behavior to alleviate the progression of the disease. These results not only advance our understanding of the molecular underpinnings of sepsis but also pave the way for developing targeted therapeutic strategies focusing on the modulation of NETosis and its associated pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe validated genes were considered potential biomarkers and used to construct a Random Forest classifier, a machine learning model capable of distinguishing sepsis patients from controls. This classifier was then validated with an independent dataset, GSE134347 (n\u0026thinsp;=\u0026thinsp;239).\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cem\u003eGEO datasets collection\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo investigate the gene expression alterations associated with sepsis, we meticulously selected and analyzed data from the Gene Expression Omnibus (GEO) database. Specifically, we focused on datasets GSE54514, GSE137342, and GSE95233. GSE54514 encompasses gene expression profiles from 36 normal and 127 sepsis-affected samples, GSE137342 contains data from 14 normal and 43 sepsis samples, and GSE95233 comprises 22 normal and 102 sepsis samples. This comprehensive dataset selection provided a robust foundation for identifying genes differentially expressed in sepsis compared to healthy control states.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2 \u003cem\u003eDifferential Expression Analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo quantify the differential expression, we utilized the \"limma\" package within the R software environment, a powerful tool for analyzing expression data through linear models. Our criteria for identifying significant differentially expressed genes (DEGs) were stringent, requiring a |log2 fold change| \u0026gt; 0.5 and a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Genes with | log2 fold change | \u0026gt; 0. 5 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are consider as DEGs.\u003c/p\u003e \u003cp\u003eFor visual representation of the DEGs, we employed the \"ggplot2\" package to generate informative volcano plots, which visually contrast the magnitude of expression changes against the statistical significance. Additionally, the \"pheatmap\" package was used to create heatmaps, offering a heatmap representation of the DEGs' expression patterns across the selected datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of NETosis-Related Genes and Their Intersection with Sepsis-Associated DEGs\u003c/h2\u003e \u003cp\u003eTo delineate the genetic underpinnings of NETosis in the context of sepsis, we embarked on a comprehensive search for NETosis-related genes using the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We identified a list of 76 genes implicated in the NETosis process.\u003c/p\u003e \u003cp\u003eTo pinpoint genes at the intersection of NETosis and sepsis, we utilized the FunRich software, a versatile tool for functional enrichment and interaction network analysis. Through the generation of Venn diagrams, we meticulously mapped out the overlap between the 76 NETosis-related genes and differentially expressed genes (DEGs) identified in our sepsis datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4 GO and KEGG enrichment pathway analysis\u003c/h2\u003e \u003cp\u003eUsing the clusterProfiler and enrichplot packages in R software (version 4.1.2), we conducted GO (Gene Ontological Analysis, GO), including biological processes (BPs), molecular functions (MFs), and cellular components (CCs), and KEGG (Kyoto Encyclopedia of Genes and Genomes pathway analysis, KEGG) enrichment pathway analysis to better understand the biological functions of DEGs and NETosis subset genes. GSEA (Gene Set Enrichment Analysis) were performed on GSE54514, GSE137342, and GSE95233 dataset separately. We selected the most enriched entries in GO, KEGG, and GSEA using an adjusted threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for screening.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Construction and Analysis of the Protein-Protein Interaction Network\u003c/h2\u003e \u003cp\u003eTo unravel the intricate interactions among NETosis-related genes, we constructed a Protein-Protein Interaction (PPI) Network, leveraging data from the STRING database. Following data retrieval from STRING, we employed the MCODE (Molecular Complex Detection) plugin in Cytoscape software for the visualization and in-depth analysis of the PPI network. Genes within this highly interconnected subnetwork were classified as hub genes, signifying their potential central role in NETosis and their influence on the pathophysiology of sepsis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5Construction of a Prediction Model Using NETosis-Related Genes\u003c/h2\u003e \u003cp\u003eTo assess the diagnostic potential of critical NETosis gene MPO, we employed receiver operating characteristic (ROC) analysis. This statistical method, facilitated by the scikit-learn Python package, allows for the calculation of the Area Under the Curve (AUC), providing a measure of the model's diagnostic accuracy. Leveraging the capabilities of scikit-learn, we developed a Random Forest classifier based on the expression levels of MPO.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Survival Analysis Based on GSE95233 Dataset\u003c/h2\u003e \u003cp\u003eUtilizing the GSE95233 dataset, which includes data from 22 normal and 102 sepsis samples, we conducted a survival analysis to explore the prognostic implications of sepsis. This analysis, carried out with the survminer and survival packages in R, differentiates between survival statuses, coded as 0 for survivors and 1 for non-survivors. Kaplan-Meier survival plots were generated to dissect the impact of demographic factors on sepsis outcomes, specifically examining the survival rates across different gender and age groups. Individuals aged 40 and below were classified as young, while those above 40 were considered old, allowing for an evaluation of age as a determinant of survival in sepsis patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Cell culture and treatments\u003c/h2\u003e \u003cp\u003eHuman umbilical vein endothelial cells (HUVECs) were acquired from the American Type Culture Collection (ATCC, Manassas, USA). Cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) from Gibco, supplemented with 5 mM glucose, 10% fetal bovine serum (FBS, Gibco), and penicillin/streptomycin (100 g/ml, Gibco). Cells were incubated at 37\u0026deg;C in a humidified atmosphere containing 5% CO\u003csub\u003e2\u003c/sub\u003e and replenished the culture medium every 2\u0026ndash;3 days to maintain optimal growth conditions. Cells were divided into control group (PBS) and MPO group (sigma, M6908). Cells were treated with MPO at 0.4 \u0026micro;g/ml.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Quantitative RT-PCR\u003c/h2\u003e \u003cp\u003eTotal RNA from cells was obtained according to the instructions of the miRNeasy Mini Kit (Biyuntian, N-103, China). Total RNA was converted to cDNA using PrimerScript RT reagent kit (Takara, Japan). qRT-PCR was performed with SYBR Premix Ex Taq reagent kit (Takara, Japan). The sequence used by the target genes were as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eGAPDH、Bax、caspase-3\u003c/em\u003e Primer Sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer Sequences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForword: 5\u0026rsquo;- CAAGGTCATCCATGACAACTTTG \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: 5\u0026rsquo;- GTCCACCACCCTGTTGCTGTAG \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eBax\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForword: 5\u0026rsquo;- TGGCAGCTGACATGTTTTCTGAC \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: 5\u0026rsquo;- CACCCAACCACCCTGGTCTT \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecaspase-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForword: AGCTACGAATCTCCGACCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: CGTTATCCCATGTGTCGAAGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Western blotting\u003c/h2\u003e \u003cp\u003eCell lysates were prepared using RIPA buffer (Beyotime Biotechnology, cat# P0013B), and proteins were denatured at 100 ℃ for 5 minutes. Proteins were then equally loaded and electrophoretically separated on an SDS-PAGE, followed by transfer onto PVDF membranes (GE Health). Membranes were blocked using 5% non-fat milk for 1 hour and subsequently incubated with primary antibodies against Cleaved Caspase-3 (#9661, 1:500) and Tubulin (#2146, 1:2000) from Cell Signaling Technology, USA, overnight. After incubation with species-specific secondary antibodies (1:5000, Immunoway, USA) for 1 hour at room temperature, detection was carried out using the ChemiDoc\u0026trade; MP E-Gel system (Bio-Rad, USA). Quantitative analysis of the protein bands was performed using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Colony formation experiment\u003c/h2\u003e \u003cp\u003eThe cells were seeded into 6-well plates at a density of 1000 cells per well, followed by incubation at 37℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e under saturated humidity. Cells were cultured for 7 days and the medium was changed every 2 days. The cells were gently washed twice with PBS and stained with crystal purple dye (containing fixed solution, C8470, solarbio, China) for 30 min at room temperature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.11 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using the R version 4.1.2. Two-group comparison was performed using the Wilcoxon rank sum test 35 when analyzing significance between various values (expression levels, infiltration ratios, and various features), The symbols \"ns\", \"*\", \" **\", \"***\"and \"****\" were used to indicate the levels of statistical significance, where \"ns\" indicated p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, * indicated p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** indicated p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** indicated p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and **** indicated p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Differential Gene Expression Analysis across Multiple Sepsis Studies\u003c/h2\u003e\n \u003cp\u003eOur comprehensive analysis of gene expression profiles from sepsis patients yielded a significant number of differentially expressed genes (DEGs). In the GSE54514 dataset, we identified 81 DEGs, with 50 genes upregulated and 31 downregulated. Extending our analysis to the GSE137342 dataset, we found a substantial alteration in the gene expression landscape, evident from the 4025 DEGs identified\u0026mdash;62 upregulated and 3963 downregulated. Similarly, the GSE95233 dataset revealed 3359 DEGs, comprising 1539 upregulated and 1820 downregulated genes. (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-C)\u003c/p\u003e\n \u003cp\u003eThe visualization of these DEGs through volcano plots and heatmaps (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD-F) underscores the extensive transcriptional changes occurring in sepsis. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-C depict the DEGs from GSE54514, GSE137342, and GSE95233, respectively, showcasing the upregulated (red dots) and downregulated (blue dots) genes.\u003c/p\u003e\n \u003cp\u003eThrough a meticulous intersection analysis using Venn diagrams, we distilled the data to identify critical genes consistently dysregulated across all datasets. (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG-H) This revealed 18 DEGs commonly downregulated: \u003cem\u003eUQCRQ, PRTN3, RETN, CTSG, PGLYRP1, MPO, CEACAM6, RNASE2, DEFA4, S100P, IFI27\u003c/em\u003e, and \u003cem\u003eRNASE3\u003c/em\u003e. Furthermore, we pinpointed genes uniquely upregulated in overlapping datasets: \u003cem\u003eACTG1\u003c/em\u003e in both GSE54514 and GSE137342; \u003cem\u003eMUC6, ASCC2, RPLP1, TRIM58, USP10, RNF213, PTMA, TSPAN5, RPL10A\u003c/em\u003e in both GSE54514 and GSE95233; and a set of 36 DEGs, including \u003cem\u003eJAK1, SAMD3\u003c/em\u003e, and \u003cem\u003eCD96\u003c/em\u003e, were found upregulated in both GSE137342 and GSE95233.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Functional Enrichment Analysis of Downregulated NETosis Genes\u003c/h2\u003e\n \u003cp\u003eWe further delved into the biological functions of genes that were consistently downregulated across multiple datasets. The GO analysis highlighted critical biological processes involved in NETosis, such as the acute inflammatory response, response to molecule of bacterial origin, neutrophil migration, positive regulation of defense response, and neutrophil chemotaxis. KEGG pathway enrichment revealed associations with diseases and processes linked to NETosis, including neutrophil extracellular trap formation and lipid metabolism-related atherosclerosis.\u003c/p\u003e\n \u003cp\u003eFurther dissection of the GSE54514 dataset through a chord chart provided a visual representation of the interplay between genes and their respective GO categorizations, emphasizing the biological process, cellular components, and molecular functions of these genes. (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-B) KEGG pathway analysis of GSE54514highlighted two critical pathways: Neutrophil extracellular trap formation and Lipid and atherosclerosis, as shown in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC and D. The pathway enrichment analysis showed that Neutrophil extracellular trap formation is predominant with a higher count of genes involved, alongside significant p-values indicating the relevance of these pathways in the study context.\u003c/p\u003e\n \u003cp\u003eThe chord diagrams in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE-G depict the comprehensive gene ontology terms categorized into biological processes, cellular components, and molecular functions. These figures illustrate the complex interplay between various GSE54514-related genes and the ontology terms. For instance, Figure E focuses on the biological processes, showing a vibrant array of connections that reflect the dynamic nature of the biological mechanisms in play. Figure F emphasizes the cellular components, where genes are linked to specific cellular locations, highlighting their potential roles within the cellular architecture. Lastly, Figure G delineates the molecular functions, demonstrating how genes contribute to the functional portfolio of the cell.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Functional pathway Enrichment Analysis of Downregulated NETosis Genes\u003c/h2\u003e\n \u003cp\u003eThe ridge plots (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-C) presented the log2 fold changes of core genes, revealing both upregulated (positive values) and downregulated (negative values) genes within the enriched pathways.\u003c/p\u003e\n \u003cp\u003eGene Set Enrichment Analysis (GSEA) across the datasets of GSE54514, GSE137342, and GSE95233 demonstrated significant pathway enrichments. Notably, the GSE54514 dataset (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD-F) showed enrichment in pathways such as chemical carcinogenesis-reactive oxygen species, diabetic cardiomyopathy, and lysosome-related processes. Similar pathways were observed in the GSE134347 analysis, while GSE95233 showcased pathways pivotal in immune response, such as antigen processing and presentation, and autoimmune thyroid disease.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Identification of MPO and PRTN3 downregulation in Sepsis progression\u003c/h2\u003e\n \u003cp\u003eThrough Protein-Protein Interaction (PPI) analysis, we identified five key hub genes associated with NETosis: \u003cem\u003eMPO\u003c/em\u003e, \u003cem\u003ePRTN3\u003c/em\u003e, \u003cem\u003eCAMP\u003c/em\u003e, \u003cem\u003eSERPINA1\u003c/em\u003e, and \u003cem\u003eELANE\u003c/em\u003e. (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA) \u003cem\u003eMPO\u003c/em\u003e and \u003cem\u003ePRTN3\u003c/em\u003e emerged as significantly downregulated genes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), underscoring their potential role as biomarkers in NETosis. This finding is aligned with the overlap analysis, which revealed that \u003cem\u003eMPO\u003c/em\u003e and \u003cem\u003ePRTN3\u003c/em\u003e are the common genes downregulated across the datasets GSE137342, GSE54514, and GSE95233 when cross-referenced with NETosis-related genes from the Gene Cards database (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eThe violin plots further illustrate the differences in expression levels of these genes between sepsis patients and normal controls within the GSE54514 dataset (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC-F). \u003cem\u003eMPO\u003c/em\u003e and \u003cem\u003ePRTN3\u003c/em\u003e exhibited a significant decrease in expression levels in sepsis patients, validating the differential analysis performed on the GSE54514 dataset. In contrast, CAMP and SERPINA1 did not show significant expression changes, and notably, \u003cem\u003eELANE\u003c/em\u003e was absent from the GSE54514 dataset. These results emphasize the importance of \u003cem\u003eMPO\u003c/em\u003e and \u003cem\u003ePRTN3\u003c/em\u003e as key contributors to the NETosis pathway in the pathogenesis of sepsis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Evaluation of NETosis Gene-Based Prediction Model and Survival Analysis\u003c/h2\u003e\n \u003cp\u003eThe construction of a Random Forest prediction model based on two NETosis-related genes was validated using datasets GSE54514 and GSE134347. For the training set GSE54514, the model achieved an AUROC of 0.77, indicating a strong discriminatory capacity with a train-test split of 80 percent for training and 20 percent for testing. The validation on dataset GSE134347 yielded an AUROC of 0.68, suggesting a good predictive performance of the model (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA-B).\u003c/p\u003e\n \u003cp\u003eIn survival analysis, gender emerged as a significant predictor of survival in sepsis patients. The Kaplan-Meier survival curves show that there is a considerable discrepancy in survival rates between genders, with a p-value of 0.00034 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). Moreover, age proved to be another crucial factor, with patients older than 40 years showing a significantly decreased chance of survival compared to younger patients, as depicted by the survival curves in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD. This age-related difference in survival probability was statistically significant with a p-value of 0.026, underscoring the importance of age as a prognostic factor in sepsis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e4.6 Implications of MPO Induction on Endothelial Cell Apoptosis and Proliferation\u003c/h2\u003e\n \u003cp\u003eIn our exploration of the role of MPO, a key protease expressed following neutrophil activation, we established an in vitro model by introducing purified MPO protein to HUVEC cultures. The results depicted in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrate the effects of MPO on endothelial cells. Treatment with MPO was associated with increased expression of apoptosis-inducing genes, as demonstrated by the upregulation of caspase-3 and BAX2 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). Furthermore, western blot analysis confirmed the elevated levels of Cleaved-Caspase3 in the presence of MPO, corroborating the gene expression data (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC). This suggests that MPO may contribute to apoptosis in endothelial cells, a process potentially relevant in the pathogenesis of sepsis. Additionally, colony formation capability was notably diminished in HUVECs treated with MPO (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD), indicating that MPO exposure may impair the proliferative capacity of endothelial cells. Together, these findings support the hypothesis that MPO plays a role in endothelial cell dysfunction during sepsis, and targeting neutrophil responses could be a viable approach to ameliorating sepsis outcomes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents a multi-dimensional approach to understanding the role of NETosis in the pathophysiology of sepsis\u0026mdash;a condition characterized by an overwhelming and life-threatening host response to infection. Our findings have significant implications for the identification of potential biomarkers and therapeutic targets, which could be pivotal in the management and prognosis of sepsis.\u003c/p\u003e \u003cp\u003eThe crux of our investigation centered on the analysis of NETosis-related genes and their expression patterns in sepsis patients. In particular, the genes MPO and PRTN3 were highlighted as key biomarkers due to their consistent downregulation across several independent datasets. These genes are known to play vital roles in the immune response, and their dysregulation might be intricately linked to the exacerbated inflammatory state observed in sepsis.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] The decreased expression levels of MPO and PRTN3, validated by our rigorous analytical process, suggest their utility as indicators of sepsis progression and potential targets for therapeutic intervention.\u003c/p\u003e \u003cp\u003eThe Random Forest model we constructed, which was validated by the AUROC, showed considerable promise in distinguishing sepsis patients from healthy controls.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] This model's high performance, as indicated by the AUROC of 0.77 for the training set and 0.68 for the validation set, underlines the robustness of the identified biomarkers in predicting sepsis. This finding opens up new avenues for the development of diagnostic tools that are both sensitive and specific.\u003c/p\u003e \u003cp\u003eFurthermore, our survival analysis brought to light the significance of gender and age as determinants of sepsis outcomes. The marked difference in survival probabilities between genders, and the lowered survival rates observed in patients over 40 years of age, underscore the need for personalized approaches in the management of sepsis. These demographic factors could guide clinicians in risk stratification and in tailoring interventions to improve patient outcomes.\u003c/p\u003e \u003cp\u003eOverall, our integrated analysis not only sheds light on the molecular underpinnings of sepsis but also propels us towards a more targeted approach in sepsis therapy. While the establishment of an in vitro NETs-induced endothelial cell model highlights the dynamic expression of MPO following NETs induction, it suggests a potential pathway through which sepsis exerts its harmful effects. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]Additional stimulation with MPO, or the upregulation of MPO, has been observed to promote apoptosis in endothelial cells, suggesting that stimulating neutrophils could help mitigate disease progression in sepsis patients.\u003c/p\u003e \u003cp\u003eIn conclusion, the insights gleaned from this study enrich our understanding of sepsis and lay the groundwork for the development of novel diagnostics and therapeutics. Our research underscores the complexity of sepsis and the need for continued investigation into the multifaceted roles of NETosis in this intricate disease. Moving forward, we advocate for further studies to confirm these findings in larger cohorts and to explore the therapeutic potential of modulating MPO and PRTN3 expression in sepsis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis comprehensive study underscores the pivotal roles of Myeloperoxidase (MPO) and Proteinase 3 (PRTN3) in the pathophysiology of sepsis, elucidating their potential as diagnostic and prognostic biomarkers. Through integrative analysis of multiple Gene Expression Omnibus datasets and in vitro modeling, we demonstrated a consistent downregulation of MPO and PRTN3 in sepsis patients, underscoring their significance in the disease's progression.\u003c/p\u003e \u003cp\u003eThe construction of a prediction model utilizing a Random Forest classifier showed substantial diagnostic potential, as evidenced by AUROC scores of 0.77 and 0.68 for the training and validation datasets, respectively. Our findings also reveal gender and age as critical prognostic factors influencing sepsis outcomes. The distinct survival probabilities between genders and the sharp decrease in survival rates in patients over the age of 40 call for a personalized approach to sepsis treatment, where patient-specific characteristics guide clinical decision-making.The discovery that modulating MPO levels in an in vitro NETs-induced endothelial cell model can induce apoptosis proposes a novel mechanism by which the regulation of NETosis-related genes may influence the severity of sepsis. This revelation invites further exploration into the therapeutic modulation of MPO and PRTN3, which may offer a new frontier in ameliorating the detrimental effects of sepsis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003ch2\u003eFunding information\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAili Fang conceived, devised the study, performed the bioinformatics analysis, found the related data set and analysis tool, and wrote the manuscript. The author contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe datasets used to support the findings of this study are available from GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. All data can be acquired from the corresponding authors on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNie D, Chen C, Li Y, Zeng C (2022) Disulfiram, an aldehyde dehydrogenase inhibitor, works as a potent drug against sepsis and cancer via NETosis, pyroptosis, apoptosis, ferroptosis, and cuproptosis. 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Br J Anaesth 128(2):283\u0026ndash;293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang J, Hong W, Wan M, Zheng L (2020) Molecular mechanisms and therapeutic target of NETosis in diseases. MedComm 2022;3(3):e162\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun S, Duan Z, Wang X, Chu C, Yang C, Chen F et al (2021) Neutrophil extracellular traps impair intestinal barrier functions in sepsis by regulating TLR9-mediated endoplasmic reticulum stress pathway. Cell Death Dis 12(6):606\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Jiang H, Ding C, Zhang L, Ding N, Li G et al (2023) STING activation in platelets aggravates septic thrombosis by enhancing platelet activation and granule secretion. Immunity 56(5):1013\u0026ndash;26e6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYipp BG, Petri B, Salina D, Jenne CN, Scott BN, Zbytnuik LD et al (2012) Infection-induced NETosis is a dynamic process involving neutrophil multitasking in vivo. Nat Med 18(9):1386\u0026ndash;1393\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao JY, Zhang JH, Cheng W, Chen JW, Cui N (2021) Effects of Neutrophil Extracellular Traps in Patients With Septic Coagulopathy and Their Interaction With Autophagy. Front Immunol 12:757041\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen DP, Aiello CP, McCoy D, Stamey T, Yang J, Hogan SL et al (2023) PRTN3 variant correlates with increased autoantigen levels and relapse risk in PR3-ANCA versus MPO-ANCA disease. JCI Insight. ;8(4)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S et al (2016) The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinf 54(1 30):1\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKargapolova Y, Geissen S, Zheng R, Baldus S, Winkels H, Adam M (2021) The Enzymatic and Non-Enzymatic Function of Myeloperoxidase (MPO) in Inflammatory Communication. Antioxid (Basel). ;10(4)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLockhart JS, Sumagin R (2022) Non-Canonical Functions of Myeloperoxidase in Immune Regulation, Tissue Inflammation and Cancer. Int J Mol Sci. ;23(20)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies MJ, Hawkins CL (2020) The Role of Myeloperoxidase in Biomolecule Modification, Chronic Inflammation, and Disease. Antioxid Redox Signal 32(13):957\u0026ndash;981\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrangie C, Daher J (2022) Role of myeloperoxidase in inflammation and atherosclerosis (Review). Biomed Rep 16(6):53\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin W, Chen H, Chen X, Guo C (2024) The Roles of Neutrophil-Derived Myeloperoxidase (MPO) in Diseases: The New Progress. Antioxid (Basel). ;13(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Wu W, Du Y, Yin H, Chen Q, Yu W et al (2023) The evolution and heterogeneity of neutrophils in cancers: origins, subsets, functions, orchestrations and clinical applications. Mol Cancer 22(1):148\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenning NL, Aziz M, Gurien SD, Wang P (2019) DAMPs and NETs in Sepsis. 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Lipids Health Dis 17(1):71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Zhao Y, Jin B, Gan X, Liang B, Xiang Y et al (2021) Development and Validation of a Predictive Model for Coronary Artery Disease Using Machine Learning. Front Cardiovasc Med 8:614204\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFolco EJ, Mawson TL, Vromman A, Bernardes-Souza B, Franck G, Persson O et al (2018) Neutrophil Extracellular Traps Induce Endothelial Cell Activation and Tissue Factor Production Through Interleukin-1alpha and Cathepsin G. Arterioscler Thromb Vasc Biol 38(8):1901\u0026ndash;1912\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolarova H, Vitecek J, Cerna A, Cernik M, Pribyl J, Skladal P et al (2021) Myeloperoxidase mediated alteration of endothelial function is dependent on its cationic charge. Free Radic Biol Med 162:14\u0026ndash;26\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NETosis, MPO, PRTN3, Gene Ontology","lastPublishedDoi":"10.21203/rs.3.rs-4229642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4229642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"1.Abstract:\nSepsis, a systemic and life-threatening response to infection, presents complex challenges in clinical management and prognosis due to its intricate pathophysiology. The formation of Neutrophil Extracellular Traps (NETs) through a process known as NETosis has been identified as a significant contributor to the development of sepsis. This study aimed to dissect the roles of NETosis-related genes, particularly Myeloperoxidase (MPO) and Proteinase 3 (PRTN3), in sepsis progression. By integrating and analyzing multiple Gene Expression Omnibus (GEO) datasets, we conducted a comprehensive gene expression profiling that revealed consistent downregulation of MPO and PRTN3, among others, in sepsis patients. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, we characterized the biological functions and pathways associated with these genes, emphasizing their relevance to immune responses in sepsis. A prediction model utilizing these biomarkers was constructed using a Random Forest classifier, which demonstrated robust predictive capability, as reflected by an AUROC of 0.77 for training and 0.68 for validation datasets. Survival analysis further underscored the prognostic value of demographic factors, particularly gender and age. The model highlighted gender-specific survival rates and revealed a significant decline in survival probability in patients over 40 years of age. These findings illuminate the diagnostic and prognostic potential of MPO and PRTN3 in sepsis, offering novel insights into the molecular dynamics of the disease and suggesting a direction for future therapeutic strategies. The study's integrated approach and novel findings advocate for personalized management of sepsis, tailoring interventions to individual patient profiles to improve outcomes.","manuscriptTitle":"Dissecting the Role of NETosis-Related Biomarkers in Sepsis: An Integrated Multi-Dataset Analysis for Diagnostic and Prognostic Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-11 18:17:53","doi":"10.21203/rs.3.rs-4229642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76300b9a-3e0c-4a01-af0f-ee0b85be166c","owner":[],"postedDate":"April 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-01T14:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-11 18:17:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4229642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4229642","identity":"rs-4229642","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}