Identification and validation of genes involved in pyroptosis of LPS and D-GALN induced acute liver injury

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In this study, we aimed to identify and validate important pyroptosis-related genes in ALF by bioinformatics analysis. The pyroptosis-related genes involved in the differential expression of ALF were identified using the gene expression comprehensive database (GEO) and the mRNA expression profile dataset GSE217659 provided by R software. The Gene Ontology (GO) enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the differentially expressed related genes.Further screened the module genes by WGCNA and identified four genes (Gzmb, Mefv, Gbp 2 and Casp 4), which could be used as potential diagnostic biomarkers for ALF. Subsequently, the hub gene was modeled using nomogram to assess whether the model was good. pyroptosis ALF bioinformatics analysis gene expression omnibus dataset Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Acute liver injury(ALI) is a common clinical disease, the main reasons are viruses, alcohol, drugs, chemical poisons and extrahepatic injury factors. If it is not controlled and treated in time, it will develop into liver failure. Acute liver failure(ALF) refers to a syndrome caused by a variety of factors and a rapid deterioration of liver function, resulting in serious impairment or decompensation of the synthesis, detoxification, excretion and biotransformation of the liver itself, thus showing progressive changes and coagulation dysfunction. The causes of acute liver failure vary widely by socioeconomic factors, exposure, and genetic predisposition.Paracetamol-aminaminophen (APAP) toxicity is the most common cause of acute liver failure, followed by other drug-related injuries, viral etiology, and Wilson's disease (WD). The mortality rate from acute liver failure is extremely high, usually over 90%; the most common causes of death are multiple organ failure, bleeding, infection and cerebral edema. Without diagnosis and treatment early will induce a poor prognosis. Virus, alcohol, drugs, chemical poisons, and extrahepatic damaging factors may cause risk-related molecular patterns (DAMPs) and the accumulation of systemic cytokines associated with ischemic hepatocytes or cholangiocyte injury. Stellate cells and hepatic macrophages (Kupffer cells) respond to these danger signals (alarms) by producing large amounts of inflammatory cytokines (TNF IL-6 IL-1β) and chemokines (CCL2), attracting other immune cells (e. g., neutrophils and monocytes) and providing signals to liver cells to switch from homeostasis to inflammatory gene and protein expression programs. This process involves the induction of acute phase proteins (APPs), such as serum amyloid A-1 (SAA) and other inflammatory mediators (IL-8 and CXCL1). The accumulation of neutrophils (through interaction between cell-cell adhesion molecule 1 (ICAM 1) and lymphocyte function-related antigen 1 (LFA 1)) and monocytes in the liver contributes to antimicrobial defense (formation or phagocytosis by neutrophil extracellular traps (NET)) and immune-mediated hepatocyte injury (induction of apoptosis by TNF and pyroptosis by NLRP3 bodies). Inflammasomes clustered via nod-like receptors (NLRs) are essentially considered the most important innate immune sensors and play a critical role in screening for cytosolic contamination or perturbation of [ 1 ]. In conclusion, inflammation caused by innate immune signaling mediated by DAMPs is considered as the major pathogenic mechanism in the progression of acute liver failure.So far, the role of pyroptosis in acute liver failure is not clear and should be further investigated.Exploration of the pathogenesis is beneficial to identify the potential targets of the disease.Novel immunological biomarkers not only have the potential to serve as possible predictors of the diagnosis of ALF but also have the ability to act as prospective targets of ALF.To maximize outcomes for patients with ALF, exploring the pathogenesis of the disease are crucial. The infiltration of native immune cells into liver tissue can mediate hepatocyte injury, and pyroptosis is an important mechanism of hepatocyte injury. Recent reviews have also clarified that pyroptosis plays an important role in the progression of the liver disease [ 2 ].Pyroptosis is a profoundly inflammatory mode of Regulated cell death related to the innate immune system [ 3 ].It has evolved to remove intracellular pathogens and has a distinct morphology associated with cell bursting. The canonical pathway of pyroptosis occurs when inflammasome sensors, NOD-like receptor family, pyrin domain-containing-1 and 3 (NLRP1, NLRP3), or absent in melanoma-2 (AIM2) are stimulated by pathogens, pathogen-associated molecular patterns(PAMPs), and DAMPs and recruit CASP1 to activate Gasdermin D (GSDMD), which forms a pore in the plasma membrane [ 4 ].In the non-canonical pathway, cytosolic LPS and PAMPs stimulate CASP4, 5, and 11 directly, which in turn cleave GSDMD. Then, activated GSDMD, the main conduit of pyroptosis, binds membrane phospholipids and initiates pore formation, resulting in cell death[ 5 – 7 ]. The contribution of pyroptosis to liver disease is the topic of intense research in Hepatology [ 8 , 9 ]. Unrepressed NLRP3 activation has been shown to result in shortened survival, severe liver inflammation, and hepatic stellate cell (HSC) activation, leading to collagen deposition and liver fibrosis [ 10 ]. Increasing evidence supports a correlation between pyroptosis and LPS D-GALN-induced liver injury pathogenesis. For example,The ACLF model induced by Concanavalin (ConA) and D-galactosamine (D-Gal), the expression levels of NLRP3 inflammasome, cleaved caspase-1, and IL-1β were significantly increased, and the main predominant, pyroptotic cells death were markedly observed [ 11 , 12 ]. Exposure to the inhibitor of the NLRP3 inflammasome called MCC950 can considerably alleviate pyroptosis in ACLF before D-Gal stimulation [ 13 ]. IL-1 receptor 1 (IL-1R1) can markedly enhance hepatocyte death, and increase inflammation by pyroptotic process in liver failure. ACLF is significantly decreased by D-Gal and LPS induction in the liver-specific IL-1R1-knockout mice model [ 14 ].By the treatment of rhIL-1 receptor antagonist (rhIL-Ra), strongly inhibits ConAinduced hepatitis via reducing the secretion of tumor necrosis factoralpha (TNF-α) and interleukin-17 (IL-17), and the infiltration of inflammatory cells into the liver tissues [ 15 ].Pyroptosis in LPS D-GALN-induced liver injury has not been studied broadly.Further studies are needed to determine new biomarkers for the treatment of ALF based on potential pyroptosis related genes involved in ALF . In this study, we aimed to analyze the GSE217659 data set from different perspectives. The differential expression of LPS D-GALN induced ALF-related genes was determined by bioinformatic methods using limma test,Weighted Gene Go-expression Network Analysis (WGCNA) analysis, correlation analysis,gene ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, nomogram were used for and diagnostic ALF molecular marker identification.Also,immune infiltration analysis were used to evaluated hepatic immune cell infiltration. Finally, the expression levels of hub pyroptosis-related genes were screened. MATERIALS AND METHODS Pyroptosis-Related Gene Data Set and LPS D-GALN induced ALF-related genes Sequencing Data For this study, 49 genes were selected from The Molecular Signatures Database (MSigDB) ( https://www.gsea-msigdb.org/gsea/msigdb/mouse/geneset/GOBP_PYROPTOSIS/ ). The GSE217659 mRNA expression profile dataset was downloaded from GEO ( https://www.ncbi.nlm.nih.gov/geo/ ). The GSE217659 dataset, which contains 8 model and 6 control liver tissues in mouse, is based on GPL24247 platform (Illumina Nova Seq 6000, Mus musculus).(Figs. 1 ) Identification of DEGs With |log2 fold change (FC)| > 2 and p 2, p < 0.05 was Up, log FC < -2, p < 0.05 was Down. The heat map and volcano map of DEG were plotted using the “Pheatmap” R package and “ggplot2” R package, respectively. Subsequently, the obtained DEGs were intersected with 49 Pyroptosis-related genes to obtain differentially expressed genes related to pyroptosis . Weighted Gene Co-expression Network Analysis (WGCNA) Weighted Gene Co-expression Network Analysis (WGCNA) is performed to identify modules of highly correlated genes, summarize the interconnections between modules and associations with external sample traits, and identify candidate biomarkers or therapeutic targets. The WGCNA R package[ 17 ] was utilized throughout the following primary phases for the construction and modularization of distinct gene networks at various stages. The samples were organized into clusters to identify any potentially signifcant outliers that may have been present. Then, automated network systems were utilized to establish co-expression networks. Hierarchical clustering and dynamic tree cutting function detection were both utilized by the modules. To establish a connection between modules and phenotype features, estimates of module membership (MM) and gene signifcance (GS) were made. The modules that had the highest Pearson module membership correlation (MM) and a P absolute value of 0.05 were chosen to be the hub modules. The values of MM > 0.8 and GS > 0.2 were indicative of a highly connected module and clinical relevance, respectively.In our research, WGCNA was constructed to identify the modules with the highest relevance to preference of LPS D-GALN induced ALF models. Specifically, we preprocessed the sample data and removed the outliers. Subsequently, the correlation matrix was constructed. The optimal soft threshold was chosen to convert the correlation matrix into an adjacency matrix, and a topological overlap matrix (TOM) was created from the adjacency matrix. The TOM-based phase dissimilarity metric was utilized to categorize genes with similar expression patterns into gene modules using average linkage hierarchical clustering. The module with the strongest relevance to ALF were selected as key modules for subsequent analysis. Finally, the DEGs related to pyroptosis and key modules were intersected for further study. Immune infiltration analysis CIBERSORT[ 18 ] employs a deconvolution algorithm to estimate the composition and abundance of immune associated cells in a mixture of cells based on transcriptome data. In the present study, we first assessed the proportion of immune cell species in normal and ALF samples in GSE217659 using the CIBERSORT algorithm.A heatmap depicting the correlation of infiltrating immune cells was carried out using the “corrplot” R package. Gene ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) functional enrichment analysis In this research, the “clusterProfiler” R package[ 19 ] was implemented to conduct GO and KEGG functional enrichment analysis in R to assess gene-related biological processes (BP), molecular functions (MF), cellular components (CC), and generelated signaling pathways. Nomogram Construction and Receiver Operating Characteristic Evaluation Nomogram construction is valuable for clinical AVC diagnosis. Based on candidate genes, the “rms” R package was applied to construct the nomogram. “Points” indicates the score of candidate genes, and “Total Points” indicates the summation of all the scores of genes above. The ROC was subsequently established to evaluate the diagnostic value of candidate genes and nomogram regarding AVC diagnosis, and the calculation of area under the curve (AUC) and 95% CI was performed to quantify its value. AUC > 0.7 was considered the ideal diagnostic value.An evaluation of decision analysis (DCA) curve was performed to determine the clinical utility of the nomogram. Statistical Analysis Statistical analysis were performed using R software (version 4.3.2, http://www.R-project.org ) and GraphPad Prism version 9(GraphPad Software, La Jolla, CA). The Wilcoxon rank sum test was used to analyze the significance of the differential pyroptosis related gene expression in the GEO datasets. Statistical significance was set at p < 0.05. RESULTS Identification of differentially expressed LPS D-GALN induced liver injury related genes PCA was conducted to evaluate the repeatability of data within the group and showed that GSE217659 had a good data repeatability (Figs. 1 A,B).A total of 5511 genes, including 2365 genes with upregulated expression and 3146 genes with downregulated expression, were identified. (Fig. 1 C).These 5511 genes differentially expressed between model group and control group are displayed in the heat map and volcano map (Figs. 1 D,E). Construction of weighted gene coexpression networks analysis To further precisely excavate the central genes associated with the ALF phenotype, we constructed a gene co-expression network using the WGCNA algorithm. The sample hierarchical cluster analysis results showed good clustering among the samples, with no significant outliers (Fig. 2 A).A gene hierarchy clustering dendrogram was constructed by gene correlation, and a total of 26 similar gene modules were identified (Fig. 2 B).Subsequently, we finally identified the “purple” module, containing 419 genes (Cor = 0.99, p = 1e -11 ), as the most clinically valuable module in ALF based on the correlation of module feature values with ALF phenotypes (Fig. 2 C). The scatter plot (Fig. 2 D) shows a strong correlation between GS and MM in the “purple” module (Cor = 0.98, p < 1e -200 ). Evaluation of immune cell infiltration Based on the results of the functional analysis. We further compared immune infiltration between ALF model and control groups by using the CIBERSORT algorithm. (Fig. 3 A). Selection and identification of ALF core genes and functional enrichment analysis of differentially expressed genes in ALF To explore the regulatory role of pyroptosis related genes in the pathogenesis of ALF, we first intersected differentially expressed genes, purple module genes, and pyroptosis related genes to obtain a total of 4 signature genes (Fig. 4 A).The most significant enrichment terms for GO were cellular process, biological regulation, regulation of biological process, metabolic process (biological process), cellular anatomical entity, protein-containing complex (cellular component), binding, catalytic activity, molecular function regulator, and transcription regulator activity (molecular function) (Fig. 4 B). The KEGG enrichment analysis showed that the differentially expressed cytokine-cytokine receptor interaction and the MAPK signaling pathway played a key role in acute liver injury.(Fig. 4 C). Value Assessment and Identification of the Hub Genes Most Associated with Pyroptosis in ALF The nomogram was constructed based on the four candidate hub genes (Gzmb、Mefv、Gbp2 and Casp4) (Fig. 5 A), and a ROC curve was established to assess the diagnostic specificity and sensitivity of candidate hub genes and the nomogram. In the ROC curve analysis, the sensitivity and specificity of the four hub genes for assessing the importance of Pyroptosis in ALF by analyzing their AUC values, and the AUC values for all four genes were greater than 0.98, suggesting that these genes show superior representation(Fig. 5 B). The calibration plots of the nomogram model proved an excellent fit between the predictions of the colume line graph and the actual probabilities,which provided strong evidence for the reliability of the presence of Pyroptosis in ALF (Fig. 5 C). The evaluation of decision analysis (DCA) curve showed good positive net beneft in the cohort (Fig. 5 D), indicating the good applicability of the nomogram in predicting the presence of acute liver injury. Expression of hub genes in liver tissues of GSE217659 To verify the reliability of the GSE217659 dataset (Fig. 6 A), the expression of the above-mentioned four pyroptosis-related genes was further analyzed. DISCUSSION Acute liver injury (ALI) is a globally important public health issue that, when severe, rapidly progresses to acute liver failure (ALF), seriously compromising the life safety of patients. The pathogenesis of ALI is defined by massive cell death in the liver, which triggers a cascade of immune responses. Studies have shown that the aberrant activation of the nod-like receptor protein 3 (NLRP3) inflammasome plays an important role in LPS D-GALN induced ALF and that the activation of the NLRP3 inflammasome causes various types of programmed cell death (PCD), and the most typical cell death form is pyroptosis.[ 20 ] Inflammation is a key feature of LPS D-GALN induced acute liver injury. This occurs due to the release of DAMPs such as mitochondrial DNA, high mobility group box 1 (HMGB1), and nuclear fragments following lytic cell death.[ 21 ]Pyroptosis in LPS D-GALN induced acute liver injury has not been studied broadly.Therefore, to improve the prognosis of patients suffering from acute liver injury, it is essential to search for specifc pyroptosis-related diagnostic markers and investigate the patterns of cell infiltration that are associated with ALF immune cells. This study represents the initial exploration of the potential occurrence of pyroptosis in acute liver failure (ALF) and has successfully identified four hub genes (Gzmb, Mefv, Gbp2, and Casp4) that are significantly involved in this process. These findings were further confirmed through the analysis of samples obtained from murine models of acute liver injury induced by LPS D-GALN. The expression levels of Gzmb, Mefv, Gbp2 and Casp4 were notably elevated in the model samples compared to the control samples. Gzmb, a gene encoding a member of the granzyme subfamily of proteins within the peptidase S1 family of serine proteases, was particularly highlighted in this study.The encoded preproprotein is secreted by natural killer (NK) cells and cytotoxic T lymphocytes (CTLs) and proteolytically processed to generate the active protease, which induces target cell apoptosis. This protein also processes cytokines and degrades extracellular matrix proteins, and these roles are implicated in chronic inflammation and wound healing. Mice lacking a functional copy of this gene exhibit impaired immune cell-mediated cytolysis. Mefv,predicted to enable actin binding activity; identical protein binding activity; and ubiquitin protein ligase activity.Predicted to be involved in several processes, including negative regulation of NLRP3 inflammasome complex assembly; negative regulation of cytokine production; and pyroptosome complex assembly. Predicted to act upstream of or within immune system process. Predicted to be located in nucleus. Predicted to be part of microtubule associated complex. Predicted to be active in cytosol and nucleoplasm. Is expressed in bladder; female associated reproductive structure; ureter; and urethra of female. Used to study familial Mediterranean fever. Human ortholog(s) of this gene implicated in several diseases, including Henoch-Schoenlein purpura; Sweet syndrome; asthma; familial Mediterranean fever; and hematologic cancer (multiple). Orthologous to human MEFV (MEFV innate immuity regulator, pyrin). Gbp2,Predicted to enable several functions, including Hsp90 protein binding activity; cytoskeletal protein binding activity; and guanyl ribonucleotide binding activity. Acts upstream of or within several processes, including cellular response to interferon-beta; cellular response to lipopolysaccharide; and defense response to other organism. Located in cytoplasmic vesicle and symbiont-containing vacuole membrane. Is expressed in several structures, including alimentary system; brain; cardiovascular system; genitourinary system; and sensory organ. Orthologous to several human genes including GBP2 (guanylate binding protein 2).Recent studies have demonstrated that GBPs were required for inflammasomes activation in response to infection [ 22 , 23 ]. For example, GBPs have been shown to act as a cytosolic LPS sensor and assemble a platform for caspase-4 activation at LPS-containing membranes as the first step of inflammasome signaling [ 24 , 25 ]. In addition, GBP2 has been demonstrated to be involved in the antiviral response [ 26 ], and regulation of apoptosis [ 27 , 28 ]. Casp4,this gene encodes a member of the cysteine proteases that plays important roles in apoptosis, cell migration and the inflammatory response. The encoded protein mediates production of pro-inflammatory cytokines by macrophages upon bacterial infection. Mice lacking the encoded protein are resistant to endotoxic shock induced by lipopolysaccharide. These studies about these genes have mainly focused on infection; however, the role of these genes in ALF remained unclear and needed further exploration.This study also has some limitations.The evidence is based on publicly available data, and although we performed expression validation with part of experiments, further experiments are needed to validate these four diagnostic markers before they can be applied to the clinic. CONCLUSION Our study systematically discovered four pyroptosis-related candidate hub genes (Gzmb、Mefv、Gbp2 and Casp4) and provided the nomogram for diagnosing ALF by various bioinformatics analysis. We also point out the dysregulated immune cell proportion in ALF. Our study could provide potential diagnostic candidate genes in ALF patients. Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and material: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article. Competing interests: The author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding: There was no funding available for the study. Authors’ contributions: Conception of the manuscript, manipulation and analysis of the software, analysis and interpretation of the data, and manuscript completion were completed by ZWX. ZWX Was responsible for reviewing and revising the manuscript. This article was submitted under the authorization of all the authors who also participated in the article. Acknowledgments: Thanks to GEO database contributors for sharing the data. References B.K. Davis, H. Wen, J.P. Ting, The inflammasome NLRs in immunity, inflammation, and associated diseases, Annu. Rev. Immunol. 29 (2011) 707–735. 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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-4511726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313123312,"identity":"3a6c80d7-a670-4bb6-bc03-86b1d8b55111","order_by":0,"name":"Weixin Zuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYNACHmY5Nvb2A6RpMebjOZNAkjXMifMkHAyIUys/I8dM8oeMdXqbBEMCw4+KbYS1MM5IS5Pm4UnPbZNuPMDYc+Y2ES6STj4mzcBzOLdN5kACM2MbEVrYpBPbJH/wHE5nk0gwIE4LD9AWCR6ewwnEa5GQf5ZsDfSLYRswkA8S5Rf5njOGN3/2WMvLt7cffPCjgggtQMAiwdgDYR0gSj0QMH9g+EGs2lEwCkbBKBiRAACtrDUpLXzNZgAAAABJRU5ErkJggg==","orcid":"","institution":"Shuguang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Weixin","middleName":"","lastName":"Zuo","suffix":""}],"badges":[],"createdAt":"2024-06-01 03:36:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4511726/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4511726/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58189695,"identity":"2a3ffb0f-12f3-4d77-8182-0dc7119df6e5","added_by":"auto","created_at":"2024-06-12 08:13:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart for research.GEO gene expression omnibus,CIBERSORT cell-type identifcation by estimating relative subsets of RNA transcripts, DEGs differentially expressed genes, GO gene ontology,KEGG Kyoto Encyclopedia of Genes and Genomes, ROC receiver operating characteristic curve,DCA decision curve analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4511726/v1/44a575a1f75ecc47b3b4ca27.png"},{"id":58189697,"identity":"c7e2ff87-b8fa-4c18-b61f-d8aa9277f940","added_by":"auto","created_at":"2024-06-12 08:13:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":532122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expressed aging-related genes in IPF and healthy samples. (A,B) Principal component analysis for GSE217659. (C)Histogram of the 5511 differentially expressed genes in model and control groups of liver tissues.(D)Heatmap of the 5511 differentially expressed genes in model and control groups of liver tissues.(E) Volcano of the 5511 differentially expressed genes. The red dots represent the significantly up-regulated genes and the blue suggest the significantly down-regulated genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4511726/v1/904b0dedb3bb47957a45eb5b.png"},{"id":58190375,"identity":"9f6cbb14-f796-47dd-a774-7c8022fc841b","added_by":"auto","created_at":"2024-06-12 08:21:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":666331,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The sample dendrogram and feature heat map were drawn based on the Euclidean distance using the average clustering method for hierarchical clustering of samples, with each branch representing a sample, Height in the vertical coordinate being the clustering distance, and the horizontal coordinate being the clinical grouping information. (B) Gene hierarchy treeclustering diagram.The graph indicates different genes horizontally and the uncorrelatedness between genes vertically, the lower the branch, the less uncorrelated the genes within the branch, i.e., the stronger the correlation. (C) Heatmap showing the relations between the module and ALF features.The value in the small cells of the graph represent the two-calculated correlation values cor coefficients between the eigenvalues of each trait and each module as well as the corresponding statistically significant \u003cem\u003ep\u003c/em\u003e-values. Color corresponds to the size of the correlation; the darker the red, the more positive the correlation; the darker the blue, the more negative the correlation. (D) Scatter plot between gene salience (GS) and module members (MM) in purple.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4511726/v1/65819a83a2c8725ad3cd0541.png"},{"id":58189699,"identity":"1741aa44-d00b-4964-bcb3-5c3d6a3a332b","added_by":"auto","created_at":"2024-06-12 08:13:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":985119,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis.(A) Heat map of immune cells in samples with normal and diabetic nephropathy in GSE217659.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4511726/v1/22c2f90ddf64527cfbf39ceb.png"},{"id":58189700,"identity":"4449670b-13e2-444b-bbef-ed7c6c762b42","added_by":"auto","created_at":"2024-06-12 08:13:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2036404,"visible":true,"origin":"","legend":"\u003cp\u003eScreening for pyroptosis-related signature genes in ALF and functional enrichment analysis.\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram of the intersection of DEGs, purple module genes, and pyroptosis-related genes. (B) GO functional annotation of signature genes. (C) Functional annotation of the Kegg signaling pathway of signature genes. For all enriched GO and KEGG terms, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4511726/v1/b6b590fcea15fa90a3e9b797.png"},{"id":58190376,"identity":"6beaabc2-de79-4361-b42e-3a686cdffdba","added_by":"auto","created_at":"2024-06-12 08:21:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":210357,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the importance of Pyroptosis in the pathogenesis of ALF through the hub gene.\u003c/p\u003e\n\u003cp\u003e(A) The visible nomogram for measuring the significance of pyroptosis in ALF based on hub genes. (B) The ROC curve of candidate genes (Gzmb、Mefv、Gbp2 and Casp4) and nomogram show superior representation for pyroptosis in ALF.(C) Calibration curve plot for the nomogram. The X-axis represents the predictable probability, and the Y-axis represents the actual probability. Perfect prediction corresponds to the ideal dashed line. The apparent dashed line represents the entire queue,bias-corrected solid line is bias-corrected by bootstrapping (1000 repetitions) and represents the observed performance of the nomogram.All the candidate genes possess a Nomogram construction and the diagnostic value evaluation.Validation of the importance of Pyroptosis in the pathogenesis of ALF through the hub genes.(D)DCA curve of the diagnostic efficacy verifcation.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4511726/v1/07274e3d7a08209c06eaec71.png"},{"id":58189701,"identity":"2e57150f-c89e-4a6e-8e28-ac8d72cb84bc","added_by":"auto","created_at":"2024-06-12 08:13:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":237498,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of hub genes in liver tissues of GSE217659 and experimental samples\u003c/p\u003e\n\u003cp\u003e(A)The box plot of RNA-Seq expression of 4 differentially expressed pyroptosis-related genes in model and control samples.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4511726/v1/8f5d01f8cfbe36627a29a119.png"},{"id":58191182,"identity":"3ffa5055-6d15-4b3d-8db8-230e4a98123a","added_by":"auto","created_at":"2024-06-12 08:29:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5474267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4511726/v1/606923ac-c221-4c30-a184-2791202a16d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and validation of genes involved in pyroptosis of LPS and D-GALN induced acute liver injury","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAcute liver injury(ALI) is a common clinical disease, the main reasons are viruses, alcohol, drugs, chemical poisons and extrahepatic injury factors. If it is not controlled and treated in time, it will develop into liver failure. Acute liver failure(ALF) refers to a syndrome caused by a variety of factors and a rapid deterioration of liver function, resulting in serious impairment or decompensation of the synthesis, detoxification, excretion and biotransformation of the liver itself, thus showing progressive changes and coagulation dysfunction. The causes of acute liver failure vary widely by socioeconomic factors, exposure, and genetic predisposition.Paracetamol-aminaminophen (APAP) toxicity is the most common cause of acute liver failure, followed by other drug-related injuries, viral etiology, and Wilson's disease (WD). The mortality rate from acute liver failure is extremely high, usually over 90%; the most common causes of death are multiple organ failure, bleeding, infection and cerebral edema. Without diagnosis and treatment early will induce a poor prognosis. Virus, alcohol, drugs, chemical poisons, and extrahepatic damaging factors may cause risk-related molecular patterns (DAMPs) and the accumulation of systemic cytokines associated with ischemic hepatocytes or cholangiocyte injury. Stellate cells and hepatic macrophages (Kupffer cells) respond to these danger signals (alarms) by producing large amounts of inflammatory cytokines (TNF IL-6 IL-1β) and chemokines (CCL2), attracting other immune cells (e. g., neutrophils and monocytes) and providing signals to liver cells to switch from homeostasis to inflammatory gene and protein expression programs. This process involves the induction of acute phase proteins (APPs), such as serum amyloid A-1 (SAA) and other inflammatory mediators (IL-8 and CXCL1). The accumulation of neutrophils (through interaction between cell-cell adhesion molecule 1 (ICAM 1) and lymphocyte function-related antigen 1 (LFA 1)) and monocytes in the liver contributes to antimicrobial defense (formation or phagocytosis by neutrophil extracellular traps (NET)) and immune-mediated hepatocyte injury (induction of apoptosis by TNF and pyroptosis by NLRP3 bodies). Inflammasomes clustered via nod-like receptors (NLRs) are essentially considered the most important innate immune sensors and play a critical role in screening for cytosolic contamination or perturbation of [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In conclusion, inflammation caused by innate immune signaling mediated by DAMPs is considered as the major pathogenic mechanism in the progression of acute liver failure.So far, the role of pyroptosis in acute liver failure is not clear and should be further investigated.Exploration of the pathogenesis is beneficial to identify the potential targets of the disease.Novel immunological biomarkers not only have the potential to serve as possible predictors of the diagnosis of ALF but also have the ability to act as prospective targets of ALF.To maximize outcomes for patients with ALF, exploring the pathogenesis of the disease are crucial.\u003c/p\u003e \u003cp\u003eThe infiltration of native immune cells into liver tissue can mediate hepatocyte injury, and pyroptosis is an important mechanism of hepatocyte injury. Recent reviews have also clarified that pyroptosis plays an important role in the progression of the liver disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Pyroptosis is a profoundly inflammatory mode of Regulated cell death related to the innate immune system [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].It has evolved to remove intracellular pathogens and has a distinct morphology associated with cell bursting. The canonical pathway of pyroptosis occurs when inflammasome sensors, NOD-like receptor family, pyrin domain-containing-1 and 3 (NLRP1, NLRP3), or absent in melanoma-2 (AIM2) are stimulated by pathogens, pathogen-associated molecular patterns(PAMPs), and DAMPs and recruit CASP1 to activate Gasdermin D (GSDMD), which forms a pore in the plasma membrane [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].In the non-canonical pathway, cytosolic LPS and PAMPs stimulate CASP4, 5, and 11 directly, which in turn cleave GSDMD. Then, activated GSDMD, the main conduit of pyroptosis, binds membrane phospholipids and initiates pore formation, resulting in cell death[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The contribution of pyroptosis to liver disease is the topic of intense research in Hepatology [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Unrepressed NLRP3 activation has been shown to result in shortened survival, severe liver inflammation, and hepatic stellate cell (HSC) activation, leading to collagen deposition and liver fibrosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIncreasing evidence supports a correlation between pyroptosis and LPS D-GALN-induced liver injury pathogenesis. For example,The ACLF model induced by Concanavalin (ConA) and D-galactosamine (D-Gal), the expression levels of NLRP3 inflammasome, cleaved caspase-1, and IL-1β were significantly increased, and the main predominant, pyroptotic cells death were markedly observed [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Exposure to the inhibitor of the NLRP3 inflammasome called MCC950 can considerably alleviate pyroptosis in ACLF before D-Gal stimulation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. IL-1 receptor 1 (IL-1R1) can markedly enhance hepatocyte death, and increase inflammation by pyroptotic process in liver failure. ACLF is significantly decreased by D-Gal and LPS induction in the liver-specific IL-1R1-knockout mice model [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].By the treatment of rhIL-1 receptor antagonist (rhIL-Ra), strongly inhibits ConAinduced hepatitis via reducing the secretion of tumor necrosis factoralpha (TNF-α) and interleukin-17 (IL-17), and the infiltration of inflammatory cells into the liver tissues [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].Pyroptosis in LPS D-GALN-induced liver injury has not been studied broadly.Further studies are needed to determine new biomarkers for the treatment of ALF based on potential pyroptosis related genes involved in ALF .\u003c/p\u003e \u003cp\u003eIn this study, we aimed to analyze the GSE217659 data set from different perspectives. The differential expression of LPS D-GALN induced ALF-related genes was determined by bioinformatic methods using limma test,Weighted Gene Go-expression Network Analysis (WGCNA) analysis, correlation analysis,gene ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, nomogram were used for and diagnostic ALF molecular marker identification.Also,immune infiltration analysis were used to evaluated hepatic immune cell infiltration. Finally, the expression levels of hub pyroptosis-related genes were screened.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePyroptosis-Related Gene Data Set and LPS D-GALN induced ALF-related genes Sequencing Data\u003c/h2\u003e \u003cp\u003eFor this study, 49 genes were selected from The Molecular Signatures Database (MSigDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/mouse/geneset/GOBP_PYROPTOSIS/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/mouse/geneset/GOBP_PYROPTOSIS/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE217659 mRNA expression profile dataset was downloaded from GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE217659 dataset, which contains 8 model and 6 control liver tissues in mouse, is based on GPL24247 platform (Illumina Nova Seq 6000, Mus musculus).(Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eWith |log2 fold change (FC)| \u0026gt; 2 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as screening criteria, differentially expressed genes (DEGs) from GSE217659 were identified utilizing \u0026ldquo;Limma\u0026rdquo; R package [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], where log FC\u0026thinsp;\u0026gt;\u0026thinsp;2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was Up, log FC \u0026lt; -2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was Down. The heat map and volcano map of DEG were plotted using the \u0026ldquo;Pheatmap\u0026rdquo; R package and \u0026ldquo;ggplot2\u0026rdquo; R package, respectively.\u003c/p\u003e \u003cp\u003eSubsequently, the obtained DEGs were intersected with 49 Pyroptosis-related genes to obtain differentially expressed genes related to pyroptosis .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eWeighted Gene Co-expression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eWeighted Gene Co-expression Network Analysis (WGCNA) is performed to identify modules of highly correlated genes, summarize the interconnections between modules and associations with external sample traits, and identify candidate biomarkers or therapeutic targets. The WGCNA R package[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was utilized throughout the following primary phases for the construction and modularization of distinct gene networks at various stages. The samples were organized into clusters to identify any potentially signifcant outliers that may have been present. Then, automated network systems were utilized to establish co-expression networks. Hierarchical clustering and dynamic tree cutting function detection were both utilized by the modules. To establish a connection between modules and phenotype features, estimates of module membership (MM) and gene signifcance (GS) were made. The modules that had the highest Pearson module membership correlation (MM) and a \u003cem\u003eP\u003c/em\u003e absolute value of 0.05 were chosen to be the hub modules. The values of MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and GS\u0026thinsp;\u0026gt;\u0026thinsp;0.2 were indicative of a highly connected module and clinical relevance, respectively.In our research, WGCNA was constructed to identify the modules with the highest relevance to preference of LPS D-GALN induced ALF models. Specifically, we preprocessed the sample data and removed the outliers. Subsequently, the correlation matrix was constructed. The optimal soft threshold was chosen to convert the correlation matrix into an adjacency matrix, and a topological overlap matrix (TOM) was created from the adjacency matrix. The TOM-based phase dissimilarity metric was utilized to categorize genes with similar expression patterns into gene modules using average linkage hierarchical clustering. The module with the strongest relevance to ALF were selected as key modules for subsequent analysis.\u003c/p\u003e \u003cp\u003eFinally, the DEGs related to pyroptosis and key modules were intersected for further study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e \u003cp\u003eCIBERSORT[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] employs a deconvolution algorithm to estimate the composition and abundance of immune associated cells in a mixture of cells based on transcriptome data. In the present study, we first assessed the proportion of immune cell species in normal and ALF samples in GSE217659 using the CIBERSORT algorithm.A heatmap depicting the correlation of infiltrating immune cells was carried out using the \u0026ldquo;corrplot\u0026rdquo; R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGene ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) functional enrichment analysis\u003c/h2\u003e \u003cp\u003eIn this research, the \u0026ldquo;clusterProfiler\u0026rdquo; R package[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was implemented to conduct GO and KEGG functional enrichment analysis in R to assess gene-related biological processes (BP), molecular functions (MF), cellular components (CC), and generelated signaling pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNomogram Construction and Receiver Operating Characteristic Evaluation\u003c/h2\u003e \u003cp\u003eNomogram construction is valuable for clinical AVC diagnosis. Based on candidate genes, the \u0026ldquo;rms\u0026rdquo; R package was applied to construct the nomogram. \u0026ldquo;Points\u0026rdquo; indicates the score of candidate genes, and \u0026ldquo;Total Points\u0026rdquo; indicates the summation of all the scores of genes above. The ROC was subsequently established to evaluate the diagnostic value of candidate genes and nomogram regarding AVC diagnosis, and the calculation of area under the curve (AUC) and 95% CI was performed to quantify its value. AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 was considered the ideal diagnostic value.An evaluation of decision analysis (DCA) curve was performed to determine the clinical utility of the nomogram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis were performed using R software (version 4.3.2,\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GraphPad Prism version 9(GraphPad Software, La Jolla, CA). The Wilcoxon rank sum test was used to analyze the significance of the differential pyroptosis related gene expression in the GEO datasets. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of differentially expressed LPS D-GALN induced liver injury related genes\u003c/h2\u003e\n \u003cp\u003ePCA was conducted to evaluate the repeatability of data within the group and showed that GSE217659 had a good data repeatability (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA,B).A total of 5511 genes, including 2365 genes with upregulated expression and 3146 genes with downregulated expression, were identified. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC).These 5511 genes differentially expressed between model group and control group are displayed in the heat map and volcano map (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD,E).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruction of weighted gene coexpression networks analysis\u003c/h2\u003e\n \u003cp\u003eTo further precisely excavate the central genes associated with the ALF phenotype, we constructed a gene co-expression network using the WGCNA algorithm. The sample hierarchical cluster analysis results showed good clustering among the samples, with no significant outliers (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA).A gene hierarchy clustering dendrogram was constructed by gene correlation, and a total of 26 similar gene modules were identified (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).Subsequently, we finally identified the \u0026ldquo;purple\u0026rdquo; module, containing 419 genes (Cor\u0026thinsp;=\u0026thinsp;0.99, p\u0026thinsp;=\u0026thinsp;1e\u003csup\u003e-11\u003c/sup\u003e), as the most clinically valuable module in ALF based on the correlation of module feature values with ALF phenotypes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). The scatter plot (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD) shows a strong correlation between GS and MM in the \u0026ldquo;purple\u0026rdquo; module (Cor\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;1e\u003csup\u003e-200\u003c/sup\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eEvaluation of immune cell infiltration\u003c/h2\u003e\n \u003cp\u003eBased on the results of the functional analysis. We further compared immune infiltration between ALF model and control groups by using the CIBERSORT algorithm. (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSelection and identification of ALF core genes and functional enrichment analysis of differentially expressed genes in ALF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo explore the regulatory role of pyroptosis related genes in the pathogenesis of ALF, we first intersected differentially expressed genes, purple module genes, and pyroptosis related genes to obtain a total of 4 signature genes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA).The most significant enrichment terms for GO were cellular process, biological regulation, regulation of biological process, metabolic process (biological process), cellular anatomical entity, protein-containing complex (cellular component), binding, catalytic activity, molecular function regulator, and transcription regulator activity (molecular function) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). The KEGG enrichment analysis showed that the differentially expressed cytokine-cytokine receptor interaction and the MAPK signaling pathway played a key role in acute liver injury.(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eValue Assessment and Identification of the Hub Genes Most Associated with Pyroptosis in ALF\u003c/h2\u003e\n \u003cp\u003eThe nomogram was constructed based on the four candidate hub genes (Gzmb、Mefv、Gbp2 and Casp4) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA), and a ROC curve was established to assess the diagnostic specificity and sensitivity of candidate hub genes and the nomogram. In the ROC curve analysis, the sensitivity and specificity of the four hub genes for assessing the importance of Pyroptosis in ALF by analyzing their AUC values, and the AUC values for all four genes were greater than 0.98, suggesting that these genes show superior representation(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). The calibration plots of the nomogram model proved an excellent fit between the predictions of the colume line graph and the actual probabilities,which provided strong evidence for the reliability of the presence of Pyroptosis in ALF (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). The evaluation of decision analysis (DCA) curve showed good positive net beneft in the cohort (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD), indicating the good applicability of the nomogram in predicting the presence of acute liver injury.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eExpression of hub genes in liver tissues of GSE217659\u003c/h2\u003e\n \u003cp\u003eTo verify the reliability of the GSE217659 dataset (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA), the expression of the above-mentioned four pyroptosis-related genes was further analyzed.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAcute liver injury (ALI) is a globally important public health issue that, when severe, rapidly progresses to acute liver failure (ALF), seriously compromising the life safety of patients. The pathogenesis of ALI is defined by massive cell death in the liver, which triggers a cascade of immune responses. Studies have shown that the aberrant activation of the nod-like receptor protein 3 (NLRP3) inflammasome plays an important role in LPS D-GALN induced ALF and that the activation of the NLRP3 inflammasome causes various types of programmed cell death (PCD), and the most typical cell death form is pyroptosis.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eInflammation is a key feature of LPS D-GALN induced acute liver injury. This occurs due to the release of DAMPs such as mitochondrial DNA, high mobility group box 1 (HMGB1), and nuclear fragments following lytic cell death.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]Pyroptosis in LPS D-GALN induced acute liver injury has not been studied broadly.Therefore, to improve the prognosis of patients suffering from acute liver injury, it is essential to search for specifc pyroptosis-related diagnostic markers and investigate the patterns of cell infiltration that are associated with ALF immune cells.\u003c/p\u003e \u003cp\u003eThis study represents the initial exploration of the potential occurrence of pyroptosis in acute liver failure (ALF) and has successfully identified four hub genes (Gzmb, Mefv, Gbp2, and Casp4) that are significantly involved in this process. These findings were further confirmed through the analysis of samples obtained from murine models of acute liver injury induced by LPS D-GALN. The expression levels of Gzmb, Mefv, Gbp2 and Casp4 were notably elevated in the model samples compared to the control samples.\u003c/p\u003e \u003cp\u003eGzmb, a gene encoding a member of the granzyme subfamily of proteins within the peptidase S1 family of serine proteases, was particularly highlighted in this study.The encoded preproprotein is secreted by natural killer (NK) cells and cytotoxic T lymphocytes (CTLs) and proteolytically processed to generate the active protease, which induces target cell apoptosis. This protein also processes cytokines and degrades extracellular matrix proteins, and these roles are implicated in chronic inflammation and wound healing. Mice lacking a functional copy of this gene exhibit impaired immune cell-mediated cytolysis.\u003c/p\u003e \u003cp\u003eMefv,predicted to enable actin binding activity; identical protein binding activity; and ubiquitin protein ligase activity.Predicted to be involved in several processes, including negative regulation of NLRP3 inflammasome complex assembly; negative regulation of cytokine production; and pyroptosome complex assembly. Predicted to act upstream of or within immune system process. Predicted to be located in nucleus. Predicted to be part of microtubule associated complex. Predicted to be active in cytosol and nucleoplasm. Is expressed in bladder; female associated reproductive structure; ureter; and urethra of female. Used to study familial Mediterranean fever. Human ortholog(s) of this gene implicated in several diseases, including Henoch-Schoenlein purpura; Sweet syndrome; asthma; familial Mediterranean fever; and hematologic cancer (multiple). Orthologous to human MEFV (MEFV innate immuity regulator, pyrin).\u003c/p\u003e \u003cp\u003eGbp2,Predicted to enable several functions, including Hsp90 protein binding activity; cytoskeletal protein binding activity; and guanyl ribonucleotide binding activity. Acts upstream of or within several processes, including cellular response to interferon-beta; cellular response to lipopolysaccharide; and defense response to other organism. Located in cytoplasmic vesicle and symbiont-containing vacuole membrane. Is expressed in several structures, including alimentary system; brain; cardiovascular system; genitourinary system; and sensory organ. Orthologous to several human genes including GBP2 (guanylate binding protein 2).Recent studies have demonstrated that GBPs were required for inflammasomes activation in response to infection [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For example, GBPs have been shown to act as a cytosolic LPS sensor and assemble a platform for caspase-4 activation at LPS-containing membranes as the first step of inflammasome signaling [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, GBP2 has been demonstrated to be involved in the antiviral response [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and regulation of apoptosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCasp4,this gene encodes a member of the cysteine proteases that plays important roles in apoptosis, cell migration and the inflammatory response. The encoded protein mediates production of pro-inflammatory cytokines by macrophages upon bacterial infection. Mice lacking the encoded protein are resistant to endotoxic shock induced by lipopolysaccharide.\u003c/p\u003e \u003cp\u003eThese studies about these genes have mainly focused on infection; however, the role of these genes in ALF remained unclear and needed further exploration.This study also has some limitations.The evidence is based on publicly available data, and although we performed expression validation with part of experiments, further experiments are needed to validate these four diagnostic markers before they can be applied to the clinic.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study systematically discovered four pyroptosis-related candidate hub genes (Gzmb、Mefv、Gbp2 and Casp4) and provided the nomogram for diagnosing ALF by various bioinformatics analysis. We also point out the dysregulated immune cell proportion in ALF. Our study could provide potential diagnostic candidate genes in ALF patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThere was no funding available for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eConception of the manuscript, manipulation and analysis of the software, analysis and interpretation of the data, and manuscript completion were completed by ZWX. ZWX Was responsible for reviewing and revising the manuscript. This article was submitted under the authorization of all the authors who also participated in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThanks to GEO database contributors for sharing the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eB.K. Davis, H. Wen, J.P. Ting, The inflammasome NLRs in immunity, inflammation, and associated diseases, Annu. Rev. Immunol. 29 (2011) 707\u0026ndash;735.\u003c/li\u003e\n \u003cli\u003eAl Mamun A, Wu Y, Jia C, Munir F, Sathy KJ, Sarker T, Monalisa I, Zhou K, Xiao J. Role of pyroptosis in liver diseases. Int Immunopharmacol. 2020 Jul;84:106489. doi: 10.1016/j.intimp.2020.106489. Epub 2020 Apr 15. PMID: 32304992.\u003c/li\u003e\n \u003cli\u003eOrning, P.; Weng, D.; Starheim, K.; Ratner, D.; Best, Z.; Lee, B.; Brooks, A.; Xia, S.; Wu, H.; Kelliher, M.A.;et al. 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Res. 42 (5) (2017) 1515\u0026ndash;1523. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"pyroptosis, ALF, bioinformatics analysis, gene expression omnibus dataset","lastPublishedDoi":"10.21203/rs.3.rs-4511726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4511726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePyroptosis plays an important role in the development of acute liver failure (ALF). In this study, we aimed to identify and validate important pyroptosis-related genes in ALF by bioinformatics analysis. The pyroptosis-related genes involved in the differential expression of ALF were identified using the gene expression comprehensive database (GEO) and the mRNA expression profile dataset GSE217659 provided by R software. The Gene Ontology (GO) enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the differentially expressed related genes.Further screened the module genes by WGCNA and identified four genes (Gzmb, Mefv, Gbp 2 and Casp 4), which could be used as potential diagnostic biomarkers for ALF. Subsequently, the hub gene was modeled using nomogram to assess whether the model was good.\u003c/p\u003e","manuscriptTitle":"Identification and validation of genes involved in pyroptosis of LPS and D-GALN induced acute liver injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 08:13:09","doi":"10.21203/rs.3.rs-4511726/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":"05a86f2b-a460-4598-b2b5-c57d900dcb90","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-08T05:43:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 08:13:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4511726","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4511726","identity":"rs-4511726","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}