Identifying pyroptosis-related molecular subtypes in asthma through gene expression profiles: implications for airway inflammation

Identifying pyroptosis-related molecular subtypes in asthma through gene expression profiles: implications for airway inflammation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying pyroptosis-related molecular subtypes in asthma through gene expression profiles: implications for airway inflammation Min Li, Jiayao Guo, Xianmei Li, Jialin Zheng, Weimin Yang, Zhuang Luo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9099737/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Purpose Asthma is a heterogeneous condition and emerging studies suggest a link between pyroptosis and the disease's development. However, few studies have identified pyroptosis-related asthma subtypes and thoroughly evaluated the role of pyroptosis-related genes (PRGs) in asthma. Methods We utilized the GSE137268 to conduct gene set variation analysis (GSVA) assessing pyroptosis levels in asthma. We then verified the pyroptosis level differences in murine asthma models. Next, we grouped asthma cases from the dataset using consensus clustering based on PRGs. We used Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single sample Gene Set Enrichment Analysis (ssGSEA) to analyze the biological functions and immune status of each subgroup. We constructed a protein-protein interaction (PPI) network to identify hub genes within the identified subtypes, and subsequently validated the identified subtypes using an independent dataset, GSE 45111. Results Asthma samples exhibited higher pyroptosis enrichment scores than control samples. The expression of IL-1β, IL-18 and GSDMD-N increased in asthma murine models. We identified two pyroptosis-related asthma subtypes: Cluster 1 and Cluster 2. Cluster 1 showed elevated inflammation, characterized by eosinophilic, neutrophilic, and mixed granulocytic asthma, while Cluster 2 was linked to paucigranulocytic asthma with reduced inflammation. Analyses of GO, KEGG, and ssGSEA revealed clear differences in biological functions and immune profiles between the two subtypes. The PPI network analysis identified five hub genes: IL1B, TLR2, MMP9, ICAM1 , and NLRP3 . Receiver operating characteristic (ROC) analysis further indicated discriminative power of these five genes to differentiate between Cluster 1 and Cluster 2. Notably, the clustering analysis applied to the GSE 45111 yielded results consistent with the subtypes identified in GSE 137268. Conclusion Aberrant pyroptosis is found in asthma and linked to airway inflammation. The subtypes identified through their PRGs expression profiles showed distinct clinical features, gene expression patterns, biological functions and inflammatory statuses. asthma heterogeneity pyroptosis molecular subtypes clustering analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Asthma is one of the most common respiratory disorders and poses a significant global health issue, affecting approximately 300 million people of all age. 1 It is characterized by pulmonary inflammation and increased airway sensitivity. However it is a broad term that covers various clinical manifestations related to bronchial hyperreactivity. In fact, asthma is an umbrella term for several diseases that include multiple subgroups with distinct clinical features or mechanisms, known as phenotypes. 2 Notable asthma phenotypes such as allergic asthma, obesity-related asthma, early-onset asthma, and neuropsychological asthma have been identified. 3 However, the differences among these phenotypes are mainly based on observable traits that arise from genetic and environmental factors, which represent downstream effects of these influences. 4 These observable traits do not necessarily reflect the unified molecular and cellular mechanisms underlying the disease. 5 Different asthma subtypes have unique clinical features and respond differently to treatments due to their varied underlying mechanisms. Therefore, there is an urgent need to develop improved strategies that can identify these subtypes based on their specific pathophysiological mechanisms. Pyroptosis is a newly characterized form of programmed cell death (PCD) that was identified by Cookson and Brennan in 2001. It is characterized by the formation of pores in the cell membrane caused by the pore formation of activated gasdermin D (GSDMD). 6 , 7 These pores allow extracellular material to flow into the cell, leading to cell swelling and even rupture. This rupture results in the release of inflammatory substances. As a potent pro-inflammatory cell death mechanism, pyroptosis has been increasingly linked to asthma pathogenesis and has become a significant focus in asthma research. 8 Genetic polymorphisms in several key components involved in pyroptosis—including NLRP3, caspase-1, and GSDMB, have been strongly linked to the risk of developing asthma. 9,10 Asthmatic patients show elevated levels of NLRP3 and caspase-1 in the supernatant of bronchoalveolar lavage fluid (BALF) as well as in airway epithelial cells obtained from lung biopsy specimens, compared to healthy controls. 11,12 In a comparative analysis, the NLRP3 knockout murine asthma model exhibited less severe pathological changes in airway hyperresponsiveness (AHR) and inflammation than the wild-type model. 13 , 14 , 15 Zhang et al. consistently found that pyroptosis of bronchial epithelial cells worsened airway inflammation and airway hyper-responsiveness in toluene diisocyanate (TDI)-induced asthma by activating the NLRP3 inflammasome and cleaving GSDMD, both in vitro and in vivo . 16 Additionally, several studies have documented a correlation between the extent of pyroptosis and clinical parameters such as airway inflammation, asthma symptom control, and lung function in patients with neutrophilic asthma. 17 , 18 Although the roles of pyroptosis and pyroptosis-related genes (PRGs) such as NLRP3, NLRP4 , and GSDMB in asthma pathogenesis have been preliminarily studied, comprehensive analyses of the roles of PRGs in asthma remain scarce. 8 Furthermore, few studies have systematically examined the pathophysiological changes of asthma from the perspective of pyroptosis. Therefore, this study aims to explore whether molecular subgroups of asthmatic individuals can be identified based on the expression patterns of PRGs in induced sputum samples, which are the most effective non-invasive samples for assessing airway inflammation in asthma. Specifically, we performed unsupervised consensus clustering using the PRGs expression matrix to identify the potential molecular subgroups of asthmatic patients. Subsequently, we characterized these subtypes by analyzing their clinical features, biological functions, immune status, and key regulatory factors. The objective of this research is to identify pyroptosis-related subtypes linked to endogenous mechanisms, with the aim of informing personalized asthma management strategies. Material and Methods Data sets and acquisition The gene expression data and clinical information of GSE137268 were retrieved from the NCBI Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo) . 19 The dataset was based on the GPL6104 platform (Illumina human Ref-8 v2.0 expression beadchip, Illumina, Inc., San Diego, California, USA). The gene expression matrix underwent log transformation, normalization, and baseline conversion to the median of all samples. This dataset included adults diagnosed with stable asthma. Participants were excluded if they had recent respiratory tract infections, experienced asthma exacerbations, had unstable asthma, underwent therapy changes, or were currently smokers. Gene expression profiles were compiled from induced sputum samples of 54 asthma patients and 15 healthy controls. Further details about the dataset can be freely accessed online at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137268 . Ethical approval was not required because all data were sourced from public databases.The animal experiments involved in this study were approved by the Animal Experiment Ethics Committee of Kunming Medical University (No. kmmu20230516). In this study, pyroptosis-related genes (PRGs) were identified using the GeneCards database ( https://www.genecards.org/ ). We used GeneCards Inferred Functionality Scores (GIFtS) for gene annotation and functional assessment. 20 By searching the keyword "pyroptosis", we identified 372 genes with GIFtS scores above 30, 21 which are listed in Supplementary Material Table 1 . Table 1 Gene list of 372 pyroptosis-related genes (PRGs) with a GIFtS above 30 ABL1 CALM2 DNMT3B HSP90AB1 MUC20 PTEN STAT5A USP48 ACE2 CALM3 DPEP1 HSP90B1 MYD88 PTGS2 STK4 USP8 ACSM3 CAMP DPP8 HTRA1 NAIP PTPN11 STXBP1 USP9X ACTN4 CAPN1 DPP9 HUWE1 NCR1 PTX3 STXBP2 UTS2 ADIPOQ CARD8 DRD2 ICAM1 NEDD4 PVALB STXBP3 VCAM1 ADORA1 CASP1 DUOX1 IFI16 NEK7 PYCARD SUGT1 VDR ADORA2B CASP10 E2F4 IFI27 NFE2L2 RAB5A SUZ12 VEGFA ADORA3 CASP3 EED IFIH1 NFKB1 RASGRF1 SYVN1 VIM AGER CASP4 EEF2K IFNB1 NFKBIA RBBP4 TAC1 VPS28 AHSA1 CASP5 EGFR IGF2BP3 NFS1 RBBP7 TACR1 VPS4B AIM2 CASP6 ELAVL1 IKBKE NINJ1 RBM26 TCEA3 VTN AKT1 CASP7 EPHA2 IKBKG NLRC4 RBMX TFAM WFDC12 ALK CASP8 ETS1 IKZF1 NLRP13 RELA TFAP2A XPNPEP1 ALOX15 CASP9 EZH2 IL13 NLRP2 RIPK1 TFG YAF2 AMIGO2 CCL5 FADD IL13RA2 NLRP3 RIPK3 TIFA YTHDF2 ANLN CD14 FCGRT IL17A NLRP6 ROCK1 TLR2 YWHAE ANXA1 CD274 FGF21 IL18 NLRP7 RPL27A TLR3 YWHAZ ANXA2 CD55 FGF5 IL18BP NLRP9 RPL3 TLR4 ZBP1 APAF1 CDC37 FKBP10 IL1A NLRX1 RPL7A TLR8 ZDHHC1 APIP CDK9 FLNA IL1B NOS1 RPS19 TLR9 ZEB2 APOA1 CDKN1B FLOT1 IL1RN NR1H2 RRBP1 TNF ZNF329 APOE CEBPB FMR1 IL27 NR4A1 RSL1D1 TNFRSF11B ZNF532 APOL1 CHI3L1 FNDC4 IL32 ORMDL3 S100A12 TNFSF13B ATF6 CHMP2A FNDC5 INPP5D OSM S100A4 TOMM20 ATG3 CHMP2B FOXP3 IQGAP1 P2RX7 S100A8 TP53 ATG5 CHMP4A FSTL1 IRAK3 P4HA1 S100A9 TPM3 ATG7 CHMP4B GABARAP IRF1 PAH SDHB TRAF2 ATOH8 CHMP4C GABARAPL1 IRF2 PAK2 SEC22B TRAF3 ATP6AP1 CHMP6 GABRG3 IRF3 PANX1 SERPINB1 TRAF6 AXL CHMP7 GALNS JUN PARP1 SERPINC1 TREM1 BAK1 CITED2 GATA1 KIF23 PCSK9 SERPINH1 TREM2 BAX CLEC3B GBP1 LCN2 PDCD6IP SESN2 TRIM21 BCL2 CLEC5A GBP2 LMNA PECAM1 SETD7 TRIM24 BCL6 CMA1 GBP3 LRPPRC PEPD SIRT1 TRIM25 BECN1 CNR1 GBP5 LY96 PGF SIRT3 TRIM31 BIRC2 COL2A1 GGT1 LYST PINK1 SIRT6 TRPA1 BIRC3 CPA3 GJA1 MALT1 PKN2 SLC16A4 TRPM2 BNIP3 CRKL GLMN MAP2K6 PLA2R1 SLC30A7 TSLP BNIP3L CRTAC1 GNA15 MAP3K7 PLAUR SLC4A2 TUBB6 BRCA1 CSNK1A1 GSK3B MAPK11 POLA2 SMAD2 TXNIP BRCC3 CSTB GSTO1 MAPK14 POP1 SMC4 UBE2D2 BRD4 CTSG GSTP1 MDM2 PPARG SNIP1 UBE2D3 BSG CUL4B GZMA MEFV PPIC SNRK UBR2 BST2 CYBB GZMB MELK PRDM1 SNRPN UCP1 BTK CYCS HDAC2 METTL3 PRF1 SOCS1 ULK1 BTN3A1 DDX3X HDAC6 MIB1 PRG2 SQSTM1 USF2 C10orf55 DGCR8 HKDC1 MKI67 PRIM1 SRPK1 USP14 CA1 DHX8 HP MLKL PRMT5 SSR1 USP18 CALM1 DHX9 HSF1 MMP9 PROM2 STAT2 USP25 DNMT3A DNMT1 HSP90AA1 MST1 PRTN3 STAT3 USP47 GSVA and ssGSEA Analysis Based on the data set of GSE137268, Gene Set Variation Analysis (GSVA) was conducted to assess the enrichment of pyroptosis in both normal and asthmatic samples. The gene set labeled "GOBP_PYROPTOSIS" from the Molecular Signature Database(MSigDB, https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/GOBP_PYROPTOSIS ) was used as the reference for the GSVA. To investigate differences in immune infiltration between asthmatic samples with varying levels of pyroptosis enrichment, single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to calculate enrichment scores. The annotated gene set and definitions of each immune term were derived from the study conducted by Liang et al. The above-described analyses of GSVA and ssGSEA were performed using the R package “GSVA”. 22 Experimental Animals and Modeling Twelve female C57BL/6 mice, aged 6 to 8 weeks, were obtained from the Experimental Animal Center of Kunming Medical University. The mice were randomly assigned to two groups: the phosphate buffered saline control group (Control Group, n = 6) and the OVA-induced asthma group (Asthma Group, n = 6). The mice were sensitized by intraperitoneal injections on days 1, 7, and 14. Each injection contained 0.2 mL PBS with 100 µg OVA (Solarbio, Beijing, China) and 1 mg alumina hydrate (Aladdin, Shanghai, China) dissolved in 500 µL saline. Subsequently, airway inflammation was induced by daily inhalation of 1% aerosolized OVA for 30 minutes over seven consecutive days. The control group received saline injections and inhaled a nebulized saline solution. Mice were euthanized 24 hours after the final challenge, and their lung tissues were snap-frozen in liquid nitrogen and stored at -80°C for further analysis. The experimental protocols adhered to the guidelines sanctioned by the Animal Research Ethics Committee of Kunming Medical University (NO. kmmu20230516). Bronchoalveolar Lavage Fluid (BALF) Collection and Enzyme-Linked Immunosorbent Assay (ELISA) BALF was collected on the day after the last OVA challenge. It was collected from the left lung, and the supernatant was isolated for quantification of IL-1β and IL-18 concentrations using ELISA kits (Lianke Biotech, Hangzhou, China) following the manufacturer's instructions. Immunofluorescence Analysis of GSDMD-N To evaluate the distribution of GSDMD-N, immunofluorescence analysis was performed. Lung sections were stained with a mouse monoclonal anti-GSDMD-N antibody (Abcam, catalog ab219800, Cambridge, UK), then examined under a fluorescence microscope. Images were captured at 400 × magnification, and fluorescence intensity was quantified using Image J software (NIH, Bethesda, MD, USA). Western Blotting analysis Lung tissues were homogenized in RIPA buffer containing a protease inhibitor cocktail (Promega, Beijing, China) and a phosphatase inhibitor cocktail (Proteintech, Rosemont, IL, USA). Protein concentrations were determined using a BCA protein assay kit (Beyotime, Shanghai, China). Equal amounts of protein (50 µg) were separated via SDS-PAGE on 10% and 12% acrylamide gels. The primary antibodies used were GSDMD-N (CST, Danvers, USA), cleaved caspase-1 (CST, Danvers, USA), and GAPDH (Proteintech, Rosemont, USA). They were incubated for 1 hour at 37°C, followed by an overnight incubation at 4°C. Membranes were washed three times with TBST and then incubated with the appropriate secondary antibodies for two hours at room temperature. After six washes, the immune complexes were analyzed using the ECL detection system (Millipore, Billerica, MA, USA). The band density was quantified using Image J software (NIH, Bethesda, MD, USA). Unsupervised consensus clustering We applied unsupervised consensus clustering to pyroptosis-related gene (PRG) expression data to classify samples from GSE137268 into distinct subgroups. The clustering analysis used the K-means algorithm based on Spearman distance, setting the maximum number of clusters to five. Specifically, the final number of clusters was determined by the consensus matrix and a cluster consensus score (> 0.8). To evaluate the effectiveness of the clustering, we utilized Principal Component Analysis (PCA). We executed the consensus clustering using the “ConsensusClusterPlus” package in R, performed PCA with the “stats” package, and generated the corresponding heatmap using the “pheatmap” package. Molecular subgroup-specific pyroptosis related gene allocation Differentially expressed genes (DEGs) were filtered using a cut-off criteria of |fold change (FC)|>1.5 and false discovery rate (FDR) < 0.05. The overlap between differentially expressed genes (DEGs) and pyroptosis-related genes (PRGs) is referred to as differentially expressed PRGs (PRDEGs). The “limma”and “ggplot2” R packages were used to screen and visualize DEGs.“VennDiagram”was applied to perform the intersection analysis. Biological function enrichment analysis To investigate the biological functions of cluster-specific genes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the "clusterProfiler" R package. The analyses were based on the corrected Fisher's exact test. A p -value of less than 0.05 was considered statistically significant. Visualization of the results was accomplished using the “ggplot” R package. Construction of PPI network and Hub gene identification We imported the identified pyroptosis-related differentially expressed genes (PRDEGs) into the STRING(version 11.0) database ( http://string-db.org ), which provides both experimental and predicted interaction data for PPI network analysis. 23 The active interaction sources included text mining, experiments, databases, co-expression, neighborhood, gene fusion, and co-occurrence. The minimum interaction score required was set at a medium confidence level of 0.40. To screen the hub genes of the PPI network, a series of topological analyses were conducted, including Maximal Clique Centrality (MCC), Edge Percolated Component (EPC), Degree, Closeness and Radiality. 22 The hub genes were ultimately determined by identifying the intersection of the top ten genes with the highest interaction scores as calculated by each of the five distinct algorithms. Cytoscape software (version 3.4.0) and the Cytohubba plugin were used for network visualization and node degree calculation in the PPI network. 24 Results Pyroptosis abundance in normal and asthma samples GSVA analysis of the GSE137268 gene expression data showed significantly higher pyroptosis enrichment scores in asthma samples than in healthy controls (P = 0.012) (Fig. 1 a). To further confirm the increased bronchial epithelial pyroptosis level in asthma, we established OVA-induced experimental asthma mice. After establishing the asthma mouse model, we conducted analyses including airway hyperresponsiveness and counting inflammatory cells in bronchoalveolar lavage fluid (BALF) to confirm the successful establishment of the model( Supplementary Material Figure S1 - S2 ). We then detected the expression of pyroptosis-related molecules in the airways. Immunofluorescence staining showed increased expression of GSDMD-N in the airway epithelium of the asthma group (Fig. 1 b). Moreover, increased expression of GSDMD-N was also found in the lungs of asthmatic mice by Western blotting (Fig. 1 c and 1 d). We further measured the levels of pyroptosis-related cytokines (IL-1β and IL-18) in BALF, finding that the concentration of IL-1β and IL-18 were higher in asthmatic mice compared with healthy controls (Fig. 1 e). Identification of pyroptosis-related subtypes in asthma Given the potential significance of pyroptosis in the pathogenesis of asthma, we performed cluster analysis based on the expression of pyroptosis-related genes (PRGs) across the entire cohort of asthmatic subjects. This analysis aimed to delineate and characterize pyroptosis-related patterns and subtypes. Consensus clustering analysis of 54 asthma samples from the GSE137268 dataset identified two stable molecular subgroups: Cluster 1 (19 samples) and Cluster 2 (35 cases). The consensus matrix indicated k = 2 as the optimal cluster number and illustrated a well-defined two-block structure. Gene expression patterns within each cluster exhibited high consistency (Figure. 2a). The bar plot showed cluster scores above 0.8 only for these two subgroup classifications (Figure. 2b), indicating greater classification stability than other groupings. Furthermore, principal component analysis (PCA) illustrated that patients were distributed into two distinct groups within the two subgroups, thereby confirming the robustness of the clustering results (Fig. 2 c). Clinical characteristics of the pyroptosis-related clusters To characterize the clinical features of the two molecular clusters, age, gender, severity of asthma, and airway inflammation phenotypes were investigated. The findings revealed a significantly higher proportion of severe asthma cases in Cluster 1 (P = 0.049). Compared to Cluster 2, patients in Cluster 1 were more likely to present with phenotypes indicative of a stronger inflammatory response, including eosinophilic asthma (EA), neutrophilic asthma (NA), and mixed granulocytic asthma (MGA). Conversely, patients in Cluster 2 were primarily associated with phenotypes characterized by lower inflammatory intensity, such as paucigranulocytic asthma (PGA). The comprehensive characteristics of both clusters are outlined in Table 1 . DEGs between the two identified clusters We identified differentially expressed genes (DEGs) between two pyroptosis-related clusters. In total, 427 DEGs were detected, including 304 up-regulated and 123 down-regulated genes. The selection criteria were |log2 FC| > 0.58 and a false discovery rate (FDR) < 0.01. Figure 3 a shows the volcano plot of these DEGs. Among them, 23 pyroptosis-related DEGs (PRDEGs) are presented in the heatmaps in Figs. 3 b and 3 c. Correlation analysis indicated a significant interrelationship among these 23 PRDEGs (Fig. 3 d). Functional analyses and immune-infiltrating landscape of the two identified clusters To clarify the differences in biological processes, we conducted functional analyses and examined the immune infiltration landscape of the two clusters. Specifically, we used the DEGs as input to perform GO and KEGG functional enrichment analyses. The results showed that the DEGs between the two clusters were mainly enriched in categories related to immune response regulation and signal transduction in the GO analysis. These include pattern recognition receptor activity (GO:0038187), chemokine receptor binding (GO:0048020), and cytokine activity (GO:0005125). In the KEGG pathway analysis, several signal transduction categories were notably enriched, including cytokine-cytokine receptor interaction (hsa04060), TNF signaling pathway (hsa04668), and NF-kappa B signaling pathway (hsa04064). The top ten enriched terms from the GO (biological process) and KEGG pathway enrichment analyses are illustrated in Figs. 4 a and 4 b. Additionally, we assessed the status of immune cell infiltration and immune-related pathways or functions in the two clusters using ssGSEA. Figures 4 c and 4 d display 23 infiltrating immune cells and eight immune-related pathways or functions. The infiltration scores for most immune cells, including eosinophils, mast cells, neutrophils, and activated B cells, were significantly higher in Cluster 1 than in Cluster 2 (all p < 0.05). In contrast, the immune scores for various immune processes were also observed significantly lower in Cluster 2, including antigen presentation process cell (APC) co-stimulation, chemokine receptors (CCR), inflammation-promoting pathways, and T cell co-stimulation (all p < 0.05) (Fig. 4 c and 4 d). Construction of PPI network and hub gene analysis The 23 identified pyroptosis-related differentially expressed genes (PRDEGs) were uploaded into the STRING database to construct a protein-protein interaction (PPI) network (further details are provided in the Supplemental Material). By analyzing the overlap among the top ten genes that had received the highest scores according to five distinct algorithms (MCC, EPC, Degree, Closeness, and Radiality), five genes were ultimately recognized as hub genes: IL1B, TLR2, MMP9, ICAM1 , and NLRP3 (Fig. 5 a and Fig. 5 b). To evaluate the predictive power of these hub genes concerning the identified clusters, a receiver operating characteristic (ROC) analysis was conducted. The results suggested that the five hub genes could distinguish patients in Cluster 1 from those in Cluster 2. The AUC was 0.869 for NLRP3 , 0.884 for TLR2 , 0.839 for MMP9 , 0.863 for IL1B and 0.913 for ICAM1 (Fig. 6 ). Validation of the identified clusters To validate the identified pyroptosis-related clusters, we repeated the consensus clustering analysis using the validation dataset (GSE45111). Consistent with previous findings, the results indicated that the cluster consensus scores for each subgroup exceeded 0.8 in only two cluster classifications ( Supplemental Material Figure S3 ). The consensus matrix, indicating a consensus for k = 2 ( Supplemental Material Figure S4 ), exhibited a well-defined two-block structure and a high consistency in gene expression patterns. Consequently, two distinct clusters were recognized. Overall, the clustering analysis results derived from the validation dataset (GSE45111) displayed remarkable similarity to the molecular subgroups identified in the GSE137268 dataset ( Supplemental Material Table 2 ). Table 2 Baseline characteristics of the pyroptosis related clusters in the validation dataset(GSE45111) Characteristics Cluster 1 Cluster 2 t/z /χ 2 P- Value N 21 26 - - Age, years, median (Q1,Q3) 63 (57, 68) 56 (43, 66) 2.164 0.031 Gender, n(%) 0.302 0.583 Male 8 (38.0) 12 (46.1) Female 13 (61.9) 14 (53.8) Inflammation type(%) 8.193 0.004 Paucigranulocytic asthma 1 (4.7) 17 (65.4) Non-Paucigranulocytic asthma 20 (95.2) 9 (34.6) Eosinophilic asthma 11 (52.3) 6 (23.0) Neutrophilic asthma 9 (42.8) 3 (11.5) Mixed granulocytic asthma 0 (0) 0 (0) Discussion Asthma is a prevalent inflammatory airway disease with underlying heterogeneous inflammatory mechanisms. The primary objectives for asthma management are symptom control and exacerbation prevention. However, achieving these goals is challenging due to the disease's heterogeneity. Asthma patients with different inflammatory mechanisms may have varied disease courses and responses to standard treatment. 25 For instance, Th2-driven asthma typically responds favorably to corticosteroid or biologic therapies, whereas non-Th2 asthma often shows limited responsiveness to these interventions. Managing severe asthma with a non-Th2 endotype is notably challenging because of unclear pathogenesis, limited effective medications, and absence of predictive tools. 26 , 27 Therefore, identifying asthma subtypes defined by distinct inflammatory mechanisms and conducting an in-depth examination based on the clinical and transcriptomic features of these subtypes holds considerable clinical and practical importance. This study revealed a significant occurrence of pyroptosis in asthmatic bronchial epithelial cells. It also identified two distinct subtypes of asthma related to pyroptosis, each with unique clinical features, biological functions, and immune profiles. GO and KEGG analyses showed that differentially expressed genes (DEGs) from the two clusters were mainly involved in pathways regulating immune response and signal transduction. This indicates variations in these biological processes between the clusters. Subsequent ssGSEA corroborated the findings from the biological function analyses. The data indicated that immune cells infiltration scores for eosinophils, mast cells, neutrophils, and activated B cells, were significantly higher in Cluster 1 than in Cluster 2. Furthermore, immune scores for various immune processes were notably lower in Cluster 2, including antigen presentation, antigen-presenting cell (APC) co-stimulation, chemokine receptors (CCR), inflammation-related pathways, and T cell co-stimulation. Our analysis showed that Paucigranulocytic asthma (PGA) was more prevalent in Cluster 2 and significantly correlated with the cluster's characteristics. Studies have suggested that PGA represents a “benign” asthma phenotype, marked by low-grade airway and systemic inflammation. 28 , 29 The features of this "benign" phenotype, along with its attenuated inflammatory response, may help explain the lower immune scores and decreased inflammation levels observed in Cluster 2. Variations in pyroptosis levels may account for the differences in inflammation between Cluster 1 and Cluster 2. Airway epithelial cells, recognized as the first line of immune defense against pathogens and environmental allergens, play a crucial role in modulating inflammatory and immune responses to irritants. 30 Damage to the airway epithelium contributes to asthma pathogenesis. The impaired epithelium releases cytokines and chemokines that attract inflammatory cells. 31 These recruited cells promote airway hyper-inflammation and airway remodeling. 32 However, even after eliminating the factors causing airway epithelial damage, airway inflammation in asthma patients persists as a chronic condition. This persistent inflammation may be linked to abnormal pyroptosis of airway epithelial cells, which could serve as a pivotal mechanism in this context. Studies have shown that allergen stimulation induces airway epithelial cells to undergo pyroptosis, rupturing cell membranes and releasing pro-inflammatory cytokines, thereby exacerbating airway inflammation. For example, studies indicated that mice with a ventricular septal defect exhibit elevated levels of GSDMD, and its deficiency can alleviate allergic airway inflammation and remodeling. 33 Additionally, drugs like Astragaloside IV reduce pyroptosis in ASMCs by inhibiting the HMGB1/RAGE axis, which helps alleviate asthma-induced airway remodeling. 34 Clinical observations showed that markers like N-GSDMD, IL-18, and IL-1β are significantly elevated in the bronchial epithelial tissues of asthma patients, suggesting that pyroptosis is a key component of asthma`s pathophysiology. Inhibiting the NF-κB/NLRP3 signaling pathway effectively suppresses pyroptosis, leading to improvements in asthma-related lung lesions. 35 Our findings underscore the crucial role of pyroptosis in asthma's development, involving various signaling pathways such as NLRP3 and NF-κB. Novel therapeutic strategies targeting the regulation of pyroptosis-related molecules hold promise for providing more effective treatment options for asthma patients. Our analysis of differential gene expression revealed 304 up-regulated genes and 123 down-regulated genes between the two clusters, including five hub genes classified as PRDEGs. NLRP3, a key regulator of pyroptosis, is linked to several inflammatory diseases, including asthma. 36 Activation of the NLRP3 inflammasome contributes to the release of inflammatory mediators that amplify the immune response and cause tissue injury and airway remodeling. TLR2 is another important molecule involved in the recognition of microbial components and mediates the immune response to allergens and pathogens in asthma. 37 The interplay between these two signaling pathways and their collective impact on pyroptotic cell death in airway epithelial cells may play a role in the pathogenesis of asthma. Wu et al found that TLR2 activation significantly upregulates the expression of NLRP3 and caspase-1. 38 This leads to enhanced pyroptotic cell death and the release of inflammatory mediators such as IL-1β and IL-18, which cause initiation and amplification of chronic inflammation in asthma. 39 Thus, we can infer that allergen activation of TLR2 triggers the NLRP3 inflammasome, leading to enhanced pyroptosis of airway epithelial cells. The release of IL-18 and IL-1β leads to persistent airway inflammation in asthma. The crosstalk between TLR2 and NLRP3 is likely to be a critical mechanism for abnormal pyroptosis in asthma. IL-1β is a pro-inflammatory cytokine primarily produced by activated macrophages.. It plays a significant role in the initiation and maintenance of airway inflammation in asthma. 40 IL-1β promotes inflammation and triggers pyroptotic cell death in both airway epithelial cells and immune cells. This process leads to the release of pro-inflammatory cytokines and chemokines, which worsen asthma symptoms. The activation of the NLRP3 inflammasome is crucial for processing and secreting IL-1β. This links pyroptosis to the dysregulated inflammatory response seen in asthmatic patients. 33 MMP9 is known for its role in the degradation of extracellular matrix components and is implicated in various inflammatory diseases, including asthma. 41 It regulates the infiltration and migration of inflammatory cells and influences the function of immune cells by modulating the activity of intercellular adhesion molecules and chemokine. Elevated levels of MMP9 have been observed in asthmatic patients, correlating with increased bronchial hyperreactivity and airway remodeling. Similarly, ICAM1, an adhesion molecule that facilitates the interaction between immune cells and the endothelium, has been linked to the recruitment of inflammatory cells to the airways, contributing to the sustained inflammatory response in asthma. 42 Enhanced pyroptosis upregulates MMP9 expression, which in turn increases ICAM1 expression on airway epithelial cells. 42 This process promotes the recruitment of immune cells, including neutrophils and eosinophils. The interplay between MMP9 and ICAM1 may exacerbate airway inflammation and contribute to the pathogenesis of asthma. Nonetheless, the mechanisms by which MMP9 and ICAM1 interact with pyroptotic pathways in asthma inflammation require further exploration. This study presents several limitations. First, since our data come from stable adult asthmatics, it remains unclear how these findings apply to patients with asthma exacerbations or to pediatric populations. Second, we cannot explore important clinical characteristics, such as treatment responses and exacerbation risks, due to limited data availability. Thirdly, the sample size in this research was relatively small. Therefore, future studies with larger cohorts and more comprehensive data are essential to confirm our findings and clarify the underlying mechanisms. In conclusion, we observed increased airway epithelial pyroptosis in asthma and identified two pyroptosis-related clusters exhibiting distinct clinical characteristics, gene expression profiles, biological functions, and inflammatory profiles. Within these clusters, we identified five PRDEGs. These findings not only emphasize the molecular diversity of asthma and the critical role of pyroptosis in its pathogenesis but also offer liminary evidence and potential targets for therapeutic strategies aimed at regulating pyroptosis in asthma management. Declarations Ethics approval and consent to participate GEO is a public database. Ethical approval was obtained for the patients data included in the database. Users can download data freely for research and publish relevant articles. Since our study is based on open-source data, there are no ethical issues or other conflicts of interest.The animal experiments involved in this study were approved by the Animal Experiment Ethics Committee of Kunming Medical University (No. kmmu20230516). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding The study was supported by the National Natural Science Foundation of China (No. 8236005), Scientific Research Fund Project of Yunnan Provincial Education Department (No. 2024J0200), Basic Research Key Project of Science and Technology Department of Yunnan Province (No. 202501AT070112), Yunnan Health Training Project of High Level Talents (No. H-2024004) and 535 Talent Project of First Affiliated Hospital of Kunming Medical University (No.2023535Q08). Authors` contributions WYZ and ML conceived the work. ML wrote the manuscript. JYG, XML and JLZ conducted the animal experiments. ML and WYZ participated in data analysis and interpretation. ZL and WYZ revised the manuscript and participated in discussions. WYZ supervised the study. All authors approved the submission of the manuscript. Funding The study was supported by the National Natural Science Foundation of China (No. 8236005), Scientific Research Fund Project of Yunnan Provincial Education Department (No. 2024J0200), Basic Research Key Project of Science and Technology Department of Yunnan Province (No. 202501AT070112), Yunnan Health Training Project of High Level Talents (No. H-2024004) and 535 Talent Project of First Affiliated Hospital of Kunming Medical University (No.2023535Q08). Author Contribution WYZ and ML conceived the work. ML wrote the manuscript. JYG, XML and JLZ conducted the animal experiments. ML and WYZ participated in data analysis and interpretation. ZL and WYZ revised the manuscript and participated in discussions. WYZ supervised the study. All authors approved the submission of the manuscript. Acknowledgments We acknowledge the GEO database for providing its platform and the contributors for uploading valuable datasets. Data Availability The datasets analyzed in this study were obtained from public repositories: GSE 45111dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45111) and GSE 137268 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137268). References GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858. Kuruvilla ME, Lee FE, Lee GB. Understanding asthma phenotypes, endotypes, and mechanisms of disease. Clin Rev Allergy Immunol. 2019;56(2):219–33. Han YY, Zhang X, Wang J, Wang G, Oliver BG, Zhang HP, et al. Multidimensional assessment of asthma identifies clinically relevant phenotype overlap: a cross-sectional study. J Allergy Clin Immunol Pract. 2021;9(1):349–62. Wu W, Bang S, Bleecker ER, Castro M, Denlinger L, Erzurum SC, et al. Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma. Am J Respir Crit Care Med. 2019;199:1358–67. Tyler SR, Bunyavanich S. Leveraging -omics for asthma endotyping. J Allergy Clin Immunol. 2019;144(1):13–23. Cookson BT, Brennan MA. Pro-inflammatory programmed cell death. Trends Microbiol. 2001;9:113–4. Yang J, Hu S, Bian Y, et al. Targeting cell death: pyroptosis, ferroptosis, apoptosis and necroptosis in osteoarthritis. Front Cell Dev Biol. 2021;9:789948. Liu L, Zhou L, Wang LL, Zheng PD, Zhang FQ, Mao ZY, et al. Programmed Cell Death in Asthma: Apoptosis, Autophagy, Pyroptosis, Ferroptosis, and Necroptosis. J Inflamm Res. 2023;16:2727–54. Queiroz GA, da Silva RR, Pires AO, et al. New variants in NLRP3 inflammasome genes increase risk for asthma and Blomia tropicalis-induced allergy in a Brazilian population. Cytokine X. 2020;2:100032. Feng S, Fox D, Man SM. Mechanisms of gasdermin family members in inflammasome signaling and cell death. J Mol Biol. 2018;430(18 Pt B):3068–80. Kim SR, Kim DI, Kim SH, et al. NLRP3 inflammasome activation by mitochondrial ROS in bronchial epithelial cells is required for allergic inflammation. Cell Death Dis. 2014;5:e1498. Sebag SC, Koval OM, Paschke JD, et al. Mitochondrial CaMKII inhibition in airway epithelium protects against allergic asthma. JCI Insight. 2017;2:e88297. Ritter M, Straubinger K, Schmidt S, et al. Functional relevance of NLRP3 inflammasome-mediated interleukin (IL)-1β during acute allergic airway inflammation. Clin Exp Immunol. 2014;178:212–23. Eisenbarth SC, Colegio OR, O’Connor W, Sutterwala FS, Flavell RA. Crucial role for the Nalp3 inflammasome in the immunostimulatory properties of aluminium adjuvants. Nature. 2008;453:1122–62. Ma M, Li G, Qi M, Jiang W, Zhou R. Inhibition of the Inflammasome Activity of NLRP3 Attenuates HDM-Induced Allergic Asthma. Front Immunol. 2021;12:718779. Zhuang J, Cui H, Zhuang L, et al. Bronchial epithelial pyroptosis promotes airway inflammation in a murine model of toluene diisocyanate-induced asthma. Biomed Pharmacother. 2020;125:109925. Kim RY, Pinkerton JW, Essilffe AT, et al. Role for NLRP3 Inflammasome-mediated, IL-1β-Dependent Responses in Severe, Steroid-Resistant Asthma. Am J Respir Crit Care Med. 2017;196:283–97. Lachowicz-Scroggins ME, Dunican EM, Charbit AR, et al. Extracellular DNA, neutrophil extracellular traps, and inflammasome activation in severe asthma. Am J Respir Crit Care Med. 2019;199:1076–85. Baines KJ, Fricker M, McDonald VM, Simpson JL, Wood LG, Wark PAB, et al. Sputum transcriptomics implicates increased p38 signalling activity in severe asthma. Respirology. 2020;25(7):709–18. Stelzer G et al. (2016). The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Current Protocols in Bioinformatics, 54(1), 1.30.1–1.30.33. Chang YT, Lin IH, Shieh JJ, Lan TH, Syu JN, Huang SS, Hou PH. Harnessing pyroptosis to restrain melanoma through aripiprazole. Apoptosis. 2026;31(1):19. 10.1007/s10495-025-02223-9 . Liang JY, Wang DS, Lin HC, Chen XX, Yang H, Zheng Y, et al. A novel ferroptosis-related gene signature for overall survival prediction in patients with hepatocellular carcinoma. Int J Biol Sci. 2020;16(13):2430–41. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11. Han YY, Zhang X, Wang J, Wang G, Oliver BG, Zhang HP, et al. Multidimensional assessment of asthma identifies clinically relevant phenotype overlap: a cross-sectional study. J Allergy Clin Immunol Pract. 2021;9(1):349–62. Hinks TSC, Levine SJ, Brusselle GG. Treatment options in type-2 low asthma. Eur Respir J. 2021;57(1):2000528. Theofani E, Semitekolou M, Samitas K, Mais A, Galani IE, Triantafyllia V, et al. TFEB signaling attenuates NLRP3-driven inflammatory responses in severe asthma. Allergy. 2022;77(7):2131–46. Deng K, Zhang X, Liu Y, Zhang L, Wang G, Feng M, et al. Heterogeneity of paucigranulocytic asthma: a prospective cohort study with hierarchical cluster analysis. J Allergy Clin Immunol Pract. 2021;9(6):2344–55. Demarche S, Schleich F, Henket M, Paulus V, Van Hees T, Louis R. Detailed analysis of sputum and systemic inflammation in asthma phenotypes: are paucigranulocytic asthmatics really non-inflammatory? BMC Pulm Med. 2016;16:46. Tsai YM, Chiang KH, Hung JY, Chang WA, Lin HP, Shieh JM, et al. Der f1 induces pyroptosis in human bronchial epithelia via the NLRP3 inflammasome. Int J Mol Med. 2018;41:757–64. Holgate ST, Roberts G, Arshad HS, Howarth PH, Davies DE. The role of the airway epithelium and its interaction with environmental factors in asthma pathogenesis. Proc Am Thorac Soc. 2009;6:655–9. Lee KS, Jin SM, Kim HJ, Lee YC. Matrix metalloproteinase inhibitor regulates inflammatory cell migration by reducing ICAM-1 and VCAM-1 expression in a murine model of toluene diisocyanate-induced asthma. J Allergy Clin Immunol. 2003;111:1278–84. Wu J, Wang P, Xie X, et al. Gasdermin D silencing alleviates airway inflammation and remodeling in an ovalbumin-induced asthmatic mouse model. Cell Death Dis. 2024;15(6):400. Zhang H, Zhang J, Pan H, Yang K, Hu C. Astragaloside IV promotes the pyroptosis of airway smooth muscle cells in childhood asthma by suppressing HMGB1/RAGE axis to inactivate NF-κB pathway. Autoimmunity. 2024;57(1):2387100. Hao Y, Wang W, Zhang L, Li W. Pyroptosis in asthma: inflammatory phenotypes, immune and non-immune cells, and novel treatment approaches. Front Pharmacol. 2024;15:1452845. Tenero L, Zaffanello M, Piazza M, Piacentini G. Measuring Airway Inflammation in Asthmatic Children. Front Pediatr. 2018;6:196. Marks KE, Cho K, Stickling C, Reynolds JM. Toll-like Receptor 2 in Autoimmune Inflammation. Immune Netw. 2021;21(3):e18. Wu HM, Zhao CC, Xie QM, Xu J, Fei GH. TLR2-Melatonin Feedback Loop Regulates the Activation of NLRP3 Inflammasome in Murine Allergic Airway Inflammation. Front Immunol. 2020;11:172. Im H, Ammit AJ. The NLRP3 inflammasome: role in airway inflammation. Clin Exp Allergy. 2014;44:160–72. Balkrishna A, Solleti SK, Singh H, Tomer M, Sharma N, Varshney A. Calcio-herbal formulation, Divya-Swasari-Ras, alleviates chronic inflammation and suppresses airway remodelling in mouse model of allergic asthma by modulating pro-inflammatory cytokine response. Biomed Pharmacother. 2020;126:110063. Zhang J, Xu L, Zhang J, et al. Pan-cancer analysis of the prognostic and immunological role of matrix metalloproteinase 9. Med (Baltim). 2023;102(30):e34499. Gu HF, Ma J, Gu KT, Brismar K. Association of intercellular adhesion molecule 1 (ICAM1) with diabetes and diabetic nephropathy. Front Endocrinol (Lausanne). 2013;3:179. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx GSDMDWB.doc Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 22 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9099737","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611612785,"identity":"c13fa0ab-5e71-4a73-a8e6-2b551d1e8e55","order_by":0,"name":"Min Li","email":"","orcid":"","institution":"昆明医科大学第一附属医院","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""},{"id":611612786,"identity":"e9337f36-bc64-4def-9760-cd48d526805c","order_by":1,"name":"Jiayao Guo","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiayao","middleName":"","lastName":"Guo","suffix":""},{"id":611612787,"identity":"527603bd-c588-4822-aa4e-a95512e7cfe6","order_by":2,"name":"Xianmei Li","email":"","orcid":"","institution":"德宏州人民医院","correspondingAuthor":false,"prefix":"","firstName":"Xianmei","middleName":"","lastName":"Li","suffix":""},{"id":611612788,"identity":"5d3cf6d7-7bf7-445f-9833-3e3891126880","order_by":3,"name":"Jialin Zheng","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jialin","middleName":"","lastName":"Zheng","suffix":""},{"id":611612789,"identity":"7d9377b9-98a8-4f7d-886e-86835f783991","order_by":4,"name":"Weimin Yang","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Yang","suffix":""},{"id":611612790,"identity":"ec41d86e-76e7-43c8-b740-8040c1245182","order_by":5,"name":"Zhuang Luo","email":"","orcid":"","institution":"昆明医科大学第一附属医院","correspondingAuthor":false,"prefix":"","firstName":"Zhuang","middleName":"","lastName":"Luo","suffix":""},{"id":611612791,"identity":"4f059d3f-c23c-417c-bec7-289d1fc849e1","order_by":6,"name":"Wenye Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3PsUpDMRSA4ROOnCyhdw04+AqnCNrhUl8lcCHTHdonMCXQSXBVfIn2DdRgfQWFDi2CrunWoaD3ji4mo9D824HzHRKAUukfxogODNc46IYYuR5nEOFgM7GSAMT93cQ2aQLCiU0MVU8eVHzuLqSIFHNtGE9J+q2v+RFBhpdF4mE9oXNSq6FveT0AZe1bBlENaXP50fInglYXOURfz8++dn7EQbhMwkhaDT1kEj8ybJBUO53dsG0o9Zer2/D0vj98YyVfl25/qMeVDKs/SdeJ/jVSYr0PY8ZSqVQqHXM/BwBEPppHX34AAAAASUVORK5CYII=","orcid":"","institution":"Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wenye","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2026-03-12 03:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9099737/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9099737/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105477156,"identity":"4a13a560-70d9-4644-9046-54500a7a1c38","added_by":"auto","created_at":"2026-03-26 13:06:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":313957,"visible":true,"origin":"","legend":"\u003cp\u003ePyroptosis level in asthma and control. (a) The GSVA enrichment score of pyroptosis in healthy control and asthma patients. *P\u0026lt;0.05; (b) Immunofluorescence staining for GSDMD-N (red) in airway epithelial (400×); (c,d) The expression of full length-GSDMD and GSDMD-N lung tissues detected by Western blot. (e) The concentration of IL-1β and IL-18 in BALF measured by ELISA *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001. NC, normal control; AS, Asthma.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099737/v1/1e48eb8d0c804bb0f3b8f7e0.jpg"},{"id":105477162,"identity":"3e3dfbc7-0c91-4788-921c-87d193c08289","added_by":"auto","created_at":"2026-03-26 13:06:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":233886,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus clustering of gene expression profiles of asthma based on the GSE137268. (a) The color-coded heatmap illustrates the consensus matrix (consensus k = 2), which is derived from minimal consensus scores for subgroups (\u0026gt; 0.8). Color gradients range from 0 to 1, with white representing 0 and dark blue representing 1. (b) The bar-plot depicts the consensus scores for subgroups with cluster count (k) ranging from 2 to 5. (c) PCA plot of the gene expression patterns of asthmatic patients categorized into the two clusters. PCA, principal components analysis.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099737/v1/5273de56e4616ab58c45a148.jpg"},{"id":105477155,"identity":"792c5310-a94c-47cc-ac4e-d3a68deb383b","added_by":"auto","created_at":"2026-03-26 13:06:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":655122,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the DEGs between the 2 pyroptosis related clusters. (a) Volcano plot of the DEGs. (b) Identification of the PRDEGs by Venn plot. (c) Heatmap demonstrates the expression pattern of the PRDEGs. (d) The correlation heatmap of the PRDEGs. DEGs PRDEGs, pyroptosis related DEGs. DEGs, Differentially expressed genes; PRDEGs, pyroptosis-related differentially expressed genes.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099737/v1/e11ba3bc5862595213b281a5.jpg"},{"id":105566281,"identity":"c27946d9-c9f6-4c71-94e5-1c715df798ed","added_by":"auto","created_at":"2026-03-27 12:56:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":316481,"visible":true,"origin":"","legend":"\u003cp\u003eBiological function enrichment and immune infiltration analysis. (a) Representative results of Go annotation in BP terms. (b) Representative results of KEGG pathway analysis. (c) The ssGSEA score of 23 immune cells. (d) The ssGSEA score of 8 immune related functions or pathways. p values were presented as: *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001. Go, Gene ontology; BP, biological process; KEGG, Kyoto Encyclopedia of Genes and Genomes; ssGSEA, single-sample gene set enrichment analysis.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099737/v1/cda36fcf6ed7190adc6cdfb9.jpg"},{"id":105477159,"identity":"81499bbc-7a27-45d6-b1da-912861fc5e1a","added_by":"auto","created_at":"2026-03-26 13:06:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":278722,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the hub genes. (a) The PPI network illustrates the interaction relationships among the 23 identified PRDEGs. (b) A Venn diagram depicts the overlap of the ten genes with the highest scores as calculated by the five different algorithms (MCC, EPC, Degree, Closeness, and Radiality). Five genes were ultimately identified as hub genes within the pyroptosis-related asthma clusters. PRDEGs, pyroptosis-related differentially expressed genes; MCC, Maximal Clique Centrality; EPC, Edge Percolated Component.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099737/v1/5c606f66fa678fecf6449943.jpg"},{"id":105566477,"identity":"0c22e028-acb0-4956-9349-7a4eb1607067","added_by":"auto","created_at":"2026-03-27 12:56:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":331958,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of the expression of\u003cem\u003e NLRP3, TLR2, MMP9, IL1B \u003c/em\u003eand \u003cem\u003eICAM1 \u003c/em\u003efor differentiating between the two identified pyroptosis-related asthma clusters.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9099737/v1/e4419abf3dd40e8eb2e84967.jpg"},{"id":105569445,"identity":"aba019fe-0405-4822-ae81-2a2f4a41ac27","added_by":"auto","created_at":"2026-03-27 13:12:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3316026,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9099737/v1/78bb3cba-06a9-4b58-b1d9-3037af4ceceb.pdf"},{"id":105477157,"identity":"f0a9ed0f-6522-438b-bb17-491ee66b8936","added_by":"auto","created_at":"2026-03-26 13:06:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":176351,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9099737/v1/9b76767a208762bfe63f9384.docx"},{"id":105477161,"identity":"a47e50a0-3e35-46c5-a9ff-0e60d3b30700","added_by":"auto","created_at":"2026-03-26 13:06:06","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":157696,"visible":true,"origin":"","legend":"","description":"","filename":"GSDMDWB.doc","url":"https://assets-eu.researchsquare.com/files/rs-9099737/v1/2697247ad24764d7d3e8e761.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying pyroptosis-related molecular subtypes in asthma through gene expression profiles: implications for airway inflammation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAsthma is one of the most common respiratory disorders and poses a significant global health issue, affecting approximately 300\u0026nbsp;million people of all age.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e It is characterized by pulmonary inflammation and increased airway sensitivity. However it is a broad term that covers various clinical manifestations related to bronchial hyperreactivity. In fact, asthma is an umbrella term for several diseases that include multiple subgroups with distinct clinical features or mechanisms, known as phenotypes.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Notable asthma phenotypes such as allergic asthma, obesity-related asthma, early-onset asthma, and neuropsychological asthma have been identified. \u003csup\u003e3\u003c/sup\u003eHowever, the differences among these phenotypes are mainly based on observable traits that arise from genetic and environmental factors, which represent downstream effects of these influences.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e These observable traits do not necessarily reflect the unified molecular and cellular mechanisms underlying the disease. \u003csup\u003e5\u003c/sup\u003eDifferent asthma subtypes have unique clinical features and respond differently to treatments due to their varied underlying mechanisms. Therefore, there is an urgent need to develop improved strategies that can identify these subtypes based on their specific pathophysiological mechanisms.\u003c/p\u003e \u003cp\u003ePyroptosis is a newly characterized form of programmed cell death (PCD) that was identified by \u003cem\u003eCookson\u003c/em\u003e and \u003cem\u003eBrennan\u003c/em\u003e in 2001. It is characterized by the formation of pores in the cell membrane caused by the pore formation of activated gasdermin D (GSDMD).\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e These pores allow extracellular material to flow into the cell, leading to cell swelling and even rupture. This rupture results in the release of inflammatory substances. As a potent pro-inflammatory cell death mechanism, pyroptosis has been increasingly linked to asthma pathogenesis and has become a significant focus in asthma research. \u003csup\u003e8\u003c/sup\u003e Genetic polymorphisms in several key components involved in pyroptosis\u0026mdash;including NLRP3, caspase-1, and GSDMB, have been strongly linked to the risk of developing asthma. \u003csup\u003e9,10\u003c/sup\u003eAsthmatic patients show elevated levels of NLRP3 and caspase-1 in the supernatant of bronchoalveolar lavage fluid (BALF) as well as in airway epithelial cells obtained from lung biopsy specimens, compared to healthy controls. \u003csup\u003e11,12\u003c/sup\u003eIn a comparative analysis, the NLRP3 knockout murine asthma model exhibited less severe pathological changes in airway hyperresponsiveness (AHR) and inflammation than the wild-type model.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eZhang et al.\u003c/em\u003e consistently found that pyroptosis of bronchial epithelial cells worsened airway inflammation and airway hyper-responsiveness in toluene diisocyanate (TDI)-induced asthma by activating the NLRP3 inflammasome and cleaving GSDMD, both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. \u003csup\u003e16\u003c/sup\u003eAdditionally, several studies have documented a correlation between the extent of pyroptosis and clinical parameters such as airway inflammation, asthma symptom control, and lung function in patients with neutrophilic asthma.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough the roles of pyroptosis and pyroptosis-related genes (PRGs) such as \u003cem\u003eNLRP3, NLRP4\u003c/em\u003e, and \u003cem\u003eGSDMB\u003c/em\u003e in asthma pathogenesis have been preliminarily studied, comprehensive analyses of the roles of PRGs in asthma remain scarce. \u003csup\u003e8\u003c/sup\u003eFurthermore, few studies have systematically examined the pathophysiological changes of asthma from the perspective of pyroptosis.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to explore whether molecular subgroups of asthmatic individuals can be identified based on the expression patterns of PRGs in induced sputum samples, which are the most effective non-invasive samples for assessing airway inflammation in asthma. Specifically, we performed unsupervised consensus clustering using the PRGs expression matrix to identify the potential molecular subgroups of asthmatic patients. Subsequently, we characterized these subtypes by analyzing their clinical features, biological functions, immune status, and key regulatory factors. The objective of this research is to identify pyroptosis-related subtypes linked to endogenous mechanisms, with the aim of informing personalized asthma management strategies.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sets and acquisition\u003c/h2\u003e \u003cp\u003eThe gene expression data and clinical information of GSE137268 were retrieved from the NCBI Gene Expression Omnibus (GEO) database (\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.\u003csup\u003e19\u003c/sup\u003e The dataset was based on the GPL6104 platform (Illumina human Ref-8 v2.0 expression beadchip, Illumina, Inc., San Diego, California, USA). The gene expression matrix underwent log transformation, normalization, and baseline conversion to the median of all samples. This dataset included adults diagnosed with stable asthma. Participants were excluded if they had recent respiratory tract infections, experienced asthma exacerbations, had unstable asthma, underwent therapy changes, or were currently smokers. Gene expression profiles were compiled from induced sputum samples of 54 asthma patients and 15 healthy controls. Further details about the dataset can be freely accessed online at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137268\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137268\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Ethical approval was not required because all data were sourced from public databases.The animal experiments involved in this study were approved by the Animal Experiment Ethics Committee of Kunming Medical University (No. kmmu20230516).\u003c/p\u003e \u003cp\u003eIn this study, pyroptosis-related genes (PRGs) were identified 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 used GeneCards Inferred Functionality Scores (GIFtS) for gene annotation and functional assessment.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e By searching the keyword \"pyroptosis\", we identified 372 genes with GIFtS scores above 30,\u003csup\u003e21\u003c/sup\u003e which are listed in \u003cem\u003eSupplementary Material Table\u0026nbsp;1\u003c/em\u003e.\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\u003eGene list of 372 pyroptosis-related genes (PRGs) with a GIFtS above 30\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABL1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCALM2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNMT3B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHSP90AB1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMUC20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTEN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTAT5A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUSP48\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCALM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDPEP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHSP90B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMYD88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTGS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTK4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUSP8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACSM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDPP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHTRA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTPN11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTXBP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUSP9X\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAPN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDPP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHUWE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNCR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTXBP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUTS2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADIPOQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCARD8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDRD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICAM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEDD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePVALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTXBP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVCAM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADORA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDUOX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIFI16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEK7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePYCARD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSUGT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADORA2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE2F4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIFI27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNFE2L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRAB5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSUZ12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVEGFA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADORA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIFIH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNFKB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRASGRF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSYVN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVIM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEEF2K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIFNB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNFKBIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRBBP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVPS28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAHSA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIGF2BP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNFS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRBBP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTACR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVPS4B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eELAVL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIKBKE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNINJ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRBM26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTCEA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVTN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEPHA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIKBKG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLRC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRBMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTFAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWFDC12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eETS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIKZF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLRP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRELA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTFAP2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eXPNPEP1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALOX15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEZH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLRP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRIPK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTFG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYAF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMIGO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFADD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL13RA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLRP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRIPK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTIFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYTHDF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFCGRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL17A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLRP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eROCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYWHAE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANXA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFGF21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLRP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRPL27A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTLR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYWHAZ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANXA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFGF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL18BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLRP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRPL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTLR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZBP1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPAF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDC37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFKBP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLRX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRPL7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTLR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZDHHC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDK9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFLNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNOS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRPS19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTLR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZEB2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDKN1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFLOT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL1RN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR1H2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRRBP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZNF329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCEBPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFMR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR4A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRSL1D1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTNFRSF11B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZNF532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHI3L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFNDC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eORMDL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTNFSF13B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHMP2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFNDC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINPP5D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS100A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTOMM20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHMP2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFOXP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIQGAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP2RX7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS100A8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHMP4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFSTL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIRAK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP4HA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS100A9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTPM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHMP4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGABARAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIRF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSDHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRAF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATOH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHMP4C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGABARAPL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIRF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEC22B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRAF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATP6AP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHMP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGABRG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIRF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePANX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSERPINB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRAF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAXL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHMP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGALNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePARP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSERPINC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTREM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCITED2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGATA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKIF23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePCSK9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSERPINH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTREM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLEC3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGBP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePDCD6IP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSESN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIM21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLEC5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGBP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePECAM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSETD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIM24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGBP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLRPPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePEPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSIRT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIM25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBECN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGBP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLY96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSIRT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRIM31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIRC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOL2A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLYST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePINK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSIRT6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRPA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIRC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGJA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMALT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePKN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSLC16A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTRPM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRKL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLMN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAP2K6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePLA2R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSLC30A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTSLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNIP3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRTAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNA15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAP3K7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePLAUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSLC4A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTUBB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSNK1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSK3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAPK11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOLA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSMAD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTXNIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRCC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSTO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAPK14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSMC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUBE2D2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSTP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMDM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPARG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNIP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUBE2D3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCUL4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGZMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMEFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNRK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUBR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBST2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGZMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMELK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePRDM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNRPN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUCP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHDAC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMETTL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePRF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSOCS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eULK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBTN3A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDDX3X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHDAC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMIB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePRG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSQSTM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUSF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC10orf55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDGCR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHKDC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMKI67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePRIM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRPK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUSP14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHX8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLKL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePRMT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSSR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUSP18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCALM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHX9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePROM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSTAT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUSP25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNMT3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSP90AA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMST1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePRTN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSTAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUSP47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\n\u003ch3\u003eGSVA and ssGSEA Analysis\u003c/h3\u003e\n\u003cp\u003eBased on the data set of GSE137268, Gene Set Variation Analysis (GSVA) was conducted to assess the enrichment of pyroptosis in both normal and asthmatic samples. The gene set labeled \"GOBP_PYROPTOSIS\" from the Molecular Signature Database(MSigDB,\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/human/geneset/GOBP_PYROPTOSIS\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/GOBP_PYROPTOSIS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used as the reference for the GSVA. To investigate differences in immune infiltration between asthmatic samples with varying levels of pyroptosis enrichment, single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to calculate enrichment scores.\u003c/p\u003e \u003cp\u003eThe annotated gene set and definitions of each immune term were derived from the study conducted by \u003cem\u003eLiang et al.\u003c/em\u003e The above-described analyses of GSVA and ssGSEA were performed using the R package \u0026ldquo;GSVA\u0026rdquo;.\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eExperimental Animals and Modeling\u003c/h3\u003e\n\u003cp\u003eTwelve female C57BL/6 mice, aged 6 to 8 weeks, were obtained from the Experimental Animal Center of Kunming Medical University. The mice were randomly assigned to two groups: the phosphate buffered saline control group (Control Group, n\u0026thinsp;=\u0026thinsp;6) and the OVA-induced asthma group (Asthma Group, n\u0026thinsp;=\u0026thinsp;6). The mice were sensitized by intraperitoneal injections on days 1, 7, and 14. Each injection contained 0.2 mL PBS with 100 \u0026micro;g OVA (Solarbio, Beijing, China) and 1 mg alumina hydrate (Aladdin, Shanghai, China) dissolved in 500 \u0026micro;L saline. Subsequently, airway inflammation was induced by daily inhalation of 1% aerosolized OVA for 30 minutes over seven consecutive days. The control group received saline injections and inhaled a nebulized saline solution. Mice were euthanized 24 hours after the final challenge, and their lung tissues were snap-frozen in liquid nitrogen and stored at -80\u0026deg;C for further analysis. The experimental protocols adhered to the guidelines sanctioned by the Animal Research Ethics Committee of Kunming Medical University (NO. kmmu20230516).\u003c/p\u003e\n\u003ch3\u003eBronchoalveolar Lavage Fluid (BALF) Collection and Enzyme-Linked Immunosorbent Assay (ELISA)\u003c/h3\u003e\n\u003cp\u003eBALF was collected on the day after the last OVA challenge. It was collected from the left lung, and the supernatant was isolated for quantification of IL-1β and IL-18 concentrations using ELISA kits (Lianke Biotech, Hangzhou, China) following the manufacturer's instructions.\u003c/p\u003e\n\u003ch3\u003eImmunofluorescence Analysis of GSDMD-N\u003c/h3\u003e\n\u003cp\u003eTo evaluate the distribution of GSDMD-N, immunofluorescence analysis was performed. Lung sections were stained with a mouse monoclonal anti-GSDMD-N antibody (Abcam, catalog ab219800, Cambridge, UK), then examined under a fluorescence microscope. Images were captured at 400 \u0026times; magnification, and fluorescence intensity was quantified using Image J software (NIH, Bethesda, MD, USA).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blotting analysis\u003c/h2\u003e \u003cp\u003eLung tissues were homogenized in RIPA buffer containing a protease inhibitor cocktail (Promega, Beijing, China) and a phosphatase inhibitor cocktail (Proteintech, Rosemont, IL, USA). Protein concentrations were determined using a BCA protein assay kit (Beyotime, Shanghai, China). Equal amounts of protein (50 \u0026micro;g) were separated via SDS-PAGE on 10% and 12% acrylamide gels. The primary antibodies used were GSDMD-N (CST, Danvers, USA), cleaved caspase-1 (CST, Danvers, USA), and GAPDH (Proteintech, Rosemont, USA). They were incubated for 1 hour at 37\u0026deg;C, followed by an overnight incubation at 4\u0026deg;C. Membranes were washed three times with TBST and then incubated with the appropriate secondary antibodies for two hours at room temperature. After six washes, the immune complexes were analyzed using the ECL detection system (Millipore, Billerica, MA, USA). The band density was quantified using Image J software (NIH, Bethesda, MD, USA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnsupervised consensus clustering\u003c/h3\u003e\n\u003cp\u003eWe applied unsupervised consensus clustering to pyroptosis-related gene (PRG) expression data to classify samples from GSE137268 into distinct subgroups. The clustering analysis used the K-means algorithm based on Spearman distance, setting the maximum number of clusters to five. Specifically, the final number of clusters was determined by the consensus matrix and a cluster consensus score (\u0026gt;\u0026thinsp;0.8). To evaluate the effectiveness of the clustering, we utilized Principal Component Analysis (PCA). We executed the consensus clustering using the \u0026ldquo;ConsensusClusterPlus\u0026rdquo; package in R, performed PCA with the \u0026ldquo;stats\u0026rdquo; package, and generated the corresponding heatmap using the \u0026ldquo;pheatmap\u0026rdquo; package.\u003c/p\u003e\n\u003ch3\u003eMolecular subgroup-specific pyroptosis related gene allocation\u003c/h3\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) were filtered using a cut-off criteria of |fold change (FC)|\u0026gt;1.5 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The overlap between differentially expressed genes (DEGs) and pyroptosis-related genes (PRGs) is referred to as differentially expressed PRGs (PRDEGs). The \u0026ldquo;limma\u0026rdquo;and \u0026ldquo;ggplot2\u0026rdquo; R packages were used to screen and visualize DEGs.\u0026ldquo;VennDiagram\u0026rdquo;was applied to perform the intersection analysis.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBiological function enrichment analysis\u003c/h2\u003e \u003cp\u003eTo investigate the biological functions of cluster-specific genes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the \"clusterProfiler\" R package. The analyses were based on the corrected Fisher's exact test. A \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 was considered statistically significant. Visualization of the results was accomplished using the \u0026ldquo;ggplot\u0026rdquo; R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of PPI network and Hub gene identification\u003c/h2\u003e \u003cp\u003eWe imported the identified pyroptosis-related differentially expressed genes (PRDEGs) into the STRING(version 11.0) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org\u003c/span\u003e\u003cspan address=\"http://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which provides both experimental and predicted interaction data for PPI network analysis. \u003csup\u003e23\u003c/sup\u003eThe active interaction sources included text mining, experiments, databases, co-expression, neighborhood, gene fusion, and co-occurrence. The minimum interaction score required was set at a medium confidence level of 0.40. To screen the hub genes of the PPI network, a series of topological analyses were conducted, including Maximal Clique Centrality (MCC), Edge Percolated Component (EPC), Degree, Closeness and Radiality. \u003csup\u003e22\u003c/sup\u003eThe hub genes were ultimately determined by identifying the intersection of the top ten genes with the highest interaction scores as calculated by each of the five distinct algorithms. Cytoscape software (version 3.4.0) and the Cytohubba plugin were used for network visualization and node degree calculation in the PPI network.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePyroptosis abundance in normal and asthma samples\u003c/h2\u003e \u003cp\u003eGSVA analysis of the GSE137268 gene expression data showed significantly higher pyroptosis enrichment scores in asthma samples than in healthy controls (P\u0026thinsp;=\u0026thinsp;0.012) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). To further confirm the increased bronchial epithelial pyroptosis level in asthma, we established OVA-induced experimental asthma mice. After establishing the asthma mouse model, we conducted analyses including airway hyperresponsiveness and counting inflammatory cells in bronchoalveolar lavage fluid (BALF) to confirm the successful establishment of the model(\u003cem\u003eSupplementary Material Figure\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/em\u003e). We then detected the expression of pyroptosis-related molecules in the airways. Immunofluorescence staining showed increased expression of GSDMD-N in the airway epithelium of the asthma group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Moreover, increased expression of GSDMD-N was also found in the lungs of asthmatic mice by Western blotting (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). We further measured the levels of pyroptosis-related cytokines (IL-1β and IL-18) in BALF, finding that the concentration of IL-1β and IL-18 were higher in asthmatic mice compared with healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of pyroptosis-related subtypes in asthma\u003c/h2\u003e \u003cp\u003eGiven the potential significance of pyroptosis in the pathogenesis of asthma, we performed cluster analysis based on the expression of pyroptosis-related genes (PRGs) across the entire cohort of asthmatic subjects. This analysis aimed to delineate and characterize pyroptosis-related patterns and subtypes. Consensus clustering analysis of 54 asthma samples from the GSE137268 dataset identified two stable molecular subgroups: Cluster 1 (19 samples) and Cluster 2 (35 cases). The consensus matrix indicated k\u0026thinsp;=\u0026thinsp;2 as the optimal cluster number and illustrated a well-defined two-block structure. Gene expression patterns within each cluster exhibited high consistency (Figure. 2a). The bar plot showed cluster scores above 0.8 only for these two subgroup classifications (Figure. 2b), indicating greater classification stability than other groupings. Furthermore, principal component analysis (PCA) illustrated that patients were distributed into two distinct groups within the two subgroups, thereby confirming the robustness of the clustering results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the pyroptosis-related clusters\u003c/h2\u003e \u003cp\u003eTo characterize the clinical features of the two molecular clusters, age, gender, severity of asthma, and airway inflammation phenotypes were investigated. The findings revealed a significantly higher proportion of severe asthma cases in Cluster 1 (P\u0026thinsp;=\u0026thinsp;0.049). Compared to Cluster 2, patients in Cluster 1 were more likely to present with phenotypes indicative of a stronger inflammatory response, including eosinophilic asthma (EA), neutrophilic asthma (NA), and mixed granulocytic asthma (MGA). Conversely, patients in Cluster 2 were primarily associated with phenotypes characterized by lower inflammatory intensity, such as paucigranulocytic asthma (PGA). The comprehensive characteristics of both clusters are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDEGs between the two identified clusters\u003c/h2\u003e \u003cp\u003eWe identified differentially expressed genes (DEGs) between two pyroptosis-related clusters. In total, 427 DEGs were detected, including 304 up-regulated and 123 down-regulated genes. The selection criteria were |log2 FC| \u0026gt; 0.58 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea shows the volcano plot of these DEGs. Among them, 23 pyroptosis-related DEGs (PRDEGs) are presented in the heatmaps in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec. Correlation analysis indicated a significant interrelationship among these 23 PRDEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analyses and immune-infiltrating landscape of the two identified clusters\u003c/h2\u003e \u003cp\u003eTo clarify the differences in biological processes, we conducted functional analyses and examined the immune infiltration landscape of the two clusters. Specifically, we used the DEGs as input to perform GO and KEGG functional enrichment analyses. The results showed that the DEGs between the two clusters were mainly enriched in categories related to immune response regulation and signal transduction in the GO analysis. These include pattern recognition receptor activity (GO:0038187), chemokine receptor binding (GO:0048020), and cytokine activity (GO:0005125). In the KEGG pathway analysis, several signal transduction categories were notably enriched, including cytokine-cytokine receptor interaction (hsa04060), TNF signaling pathway (hsa04668), and NF-kappa B signaling pathway (hsa04064). The top ten enriched terms from the GO (biological process) and KEGG pathway enrichment analyses are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb. Additionally, we assessed the status of immune cell infiltration and immune-related pathways or functions in the two clusters using ssGSEA. Figures\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed display 23 infiltrating immune cells and eight immune-related pathways or functions. The infiltration scores for most immune cells, including eosinophils, mast cells, neutrophils, and activated B cells, were significantly higher in Cluster 1 than in Cluster 2 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, the immune scores for various immune processes were also observed significantly lower in Cluster 2, including antigen presentation process cell (APC) co-stimulation, chemokine receptors (CCR), inflammation-promoting pathways, and T cell co-stimulation (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of PPI network and hub gene analysis\u003c/h2\u003e \u003cp\u003eThe 23 identified pyroptosis-related differentially expressed genes (PRDEGs) were uploaded into the STRING database to construct a protein-protein interaction (PPI) network (further details are provided in the Supplemental Material). By analyzing the overlap among the top ten genes that had received the highest scores according to five distinct algorithms (MCC, EPC, Degree, Closeness, and Radiality), five genes were ultimately recognized as hub genes: \u003cem\u003eIL1B, TLR2, MMP9, ICAM1\u003c/em\u003e, and \u003cem\u003eNLRP3\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). To evaluate the predictive power of these hub genes concerning the identified clusters, a receiver operating characteristic (ROC) analysis was conducted. The results suggested that the five hub genes could distinguish patients in Cluster 1 from those in Cluster 2. The AUC was 0.869 for \u003cem\u003eNLRP3\u003c/em\u003e, 0.884 for \u003cem\u003eTLR2\u003c/em\u003e, 0.839 for \u003cem\u003eMMP9\u003c/em\u003e, 0.863 for \u003cem\u003eIL1B\u003c/em\u003e and 0.913 for \u003cem\u003eICAM1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the identified clusters\u003c/h2\u003e \u003cp\u003eTo validate the identified pyroptosis-related clusters, we repeated the consensus clustering analysis using the validation dataset (GSE45111). Consistent with previous findings, the results indicated that the cluster consensus scores for each subgroup exceeded 0.8 in only two cluster classifications (\u003cem\u003eSupplemental Material Figure S3\u003c/em\u003e). The consensus matrix, indicating a consensus for k\u0026thinsp;=\u0026thinsp;2 (\u003cem\u003eSupplemental Material Figure S4\u003c/em\u003e), exhibited a well-defined two-block structure and a high consistency in gene expression patterns. Consequently, two distinct clusters were recognized. Overall, the clustering analysis results derived from the validation dataset (GSE45111) displayed remarkable similarity to the molecular subgroups identified in the GSE137268 dataset (\u003cem\u003eSupplemental Material\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the pyroptosis related clusters in the validation dataset(GSE45111)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et/z\u003c/em\u003e/χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (57, 68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (43, 66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammation type(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaucigranulocytic asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Paucigranulocytic asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophilic asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophilic asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed granulocytic asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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"},{"header":"Discussion","content":"\u003cp\u003eAsthma is a prevalent inflammatory airway disease with underlying heterogeneous inflammatory mechanisms. The primary objectives for asthma management are symptom control and exacerbation prevention. However, achieving these goals is challenging due to the disease's heterogeneity. Asthma patients with different inflammatory mechanisms may have varied disease courses and responses to standard treatment.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e For instance, Th2-driven asthma typically responds favorably to corticosteroid or biologic therapies, whereas non-Th2 asthma often shows limited responsiveness to these interventions. Managing severe asthma with a non-Th2 endotype is notably challenging because of unclear pathogenesis, limited effective medications, and absence of predictive tools.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Therefore, identifying asthma subtypes defined by distinct inflammatory mechanisms and conducting an in-depth examination based on the clinical and transcriptomic features of these subtypes holds considerable clinical and practical importance.\u003c/p\u003e \u003cp\u003eThis study revealed a significant occurrence of pyroptosis in asthmatic bronchial epithelial cells. It also identified two distinct subtypes of asthma related to pyroptosis, each with unique clinical features, biological functions, and immune profiles. GO and KEGG analyses showed that differentially expressed genes (DEGs) from the two clusters were mainly involved in pathways regulating immune response and signal transduction. This indicates variations in these biological processes between the clusters. Subsequent ssGSEA corroborated the findings from the biological function analyses. The data indicated that immune cells infiltration scores for eosinophils, mast cells, neutrophils, and activated B cells, were significantly higher in Cluster 1 than in Cluster 2. Furthermore, immune scores for various immune processes were notably lower in Cluster 2, including antigen presentation, antigen-presenting cell (APC) co-stimulation, chemokine receptors (CCR), inflammation-related pathways, and T cell co-stimulation. Our analysis showed that Paucigranulocytic asthma (PGA) was more prevalent in Cluster 2 and significantly correlated with the cluster's characteristics. Studies have suggested that PGA represents a \u0026ldquo;benign\u0026rdquo; asthma phenotype, marked by low-grade airway and systemic inflammation.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e The features of this \"benign\" phenotype, along with its attenuated inflammatory response, may help explain the lower immune scores and decreased inflammation levels observed in Cluster 2.\u003c/p\u003e \u003cp\u003eVariations in pyroptosis levels may account for the differences in inflammation between Cluster 1 and Cluster 2. Airway epithelial cells, recognized as the first line of immune defense against pathogens and environmental allergens, play a crucial role in modulating inflammatory and immune responses to irritants.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Damage to the airway epithelium contributes to asthma pathogenesis. The impaired epithelium releases cytokines and chemokines that attract inflammatory cells. \u003csup\u003e31\u003c/sup\u003eThese recruited cells promote airway hyper-inflammation and airway remodeling.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e However, even after eliminating the factors causing airway epithelial damage, airway inflammation in asthma patients persists as a chronic condition. This persistent inflammation may be linked to abnormal pyroptosis of airway epithelial cells, which could serve as a pivotal mechanism in this context. Studies have shown that allergen stimulation induces airway epithelial cells to undergo pyroptosis, rupturing cell membranes and releasing pro-inflammatory cytokines, thereby exacerbating airway inflammation. For example, studies indicated that mice with a ventricular septal defect exhibit elevated levels of GSDMD, and its deficiency can alleviate allergic airway inflammation and remodeling. \u003csup\u003e33\u003c/sup\u003eAdditionally, drugs like Astragaloside IV reduce pyroptosis in ASMCs by inhibiting the HMGB1/RAGE axis, which helps alleviate asthma-induced airway remodeling. \u003csup\u003e34\u003c/sup\u003eClinical observations showed that markers like N-GSDMD, IL-18, and IL-1β are significantly elevated in the bronchial epithelial tissues of asthma patients, suggesting that pyroptosis is a key component of asthma`s pathophysiology. Inhibiting the NF-κB/NLRP3 signaling pathway effectively suppresses pyroptosis, leading to improvements in asthma-related lung lesions. \u003csup\u003e35\u003c/sup\u003eOur findings underscore the crucial role of pyroptosis in asthma's development, involving various signaling pathways such as NLRP3 and NF-κB. Novel therapeutic strategies targeting the regulation of pyroptosis-related molecules hold promise for providing more effective treatment options for asthma patients.\u003c/p\u003e \u003cp\u003eOur analysis of differential gene expression revealed 304 up-regulated genes and 123 down-regulated genes between the two clusters, including five hub genes classified as PRDEGs. NLRP3, a key regulator of pyroptosis, is linked to several inflammatory diseases, including asthma. \u003csup\u003e36\u003c/sup\u003eActivation of the NLRP3 inflammasome contributes to the release of inflammatory mediators that amplify the immune response and cause tissue injury and airway remodeling. TLR2 is another important molecule involved in the recognition of microbial components and mediates the immune response to allergens and pathogens in asthma. \u003csup\u003e37\u003c/sup\u003eThe interplay between these two signaling pathways and their collective impact on pyroptotic cell death in airway epithelial cells may play a role in the pathogenesis of asthma. Wu et al found that TLR2 activation significantly upregulates the expression of NLRP3 and caspase-1. \u003csup\u003e38\u003c/sup\u003eThis leads to enhanced pyroptotic cell death and the release of inflammatory mediators such as IL-1β and IL-18, which cause initiation and amplification of chronic inflammation in asthma. \u003csup\u003e39\u003c/sup\u003eThus, we can infer that allergen activation of TLR2 triggers the NLRP3 inflammasome, leading to enhanced pyroptosis of airway epithelial cells. The release of IL-18 and IL-1β leads to persistent airway inflammation in asthma. The crosstalk between TLR2 and NLRP3 is likely to be a critical mechanism for abnormal pyroptosis in asthma.\u003c/p\u003e \u003cp\u003eIL-1β is a pro-inflammatory cytokine primarily produced by activated macrophages.. It plays a significant role in the initiation and maintenance of airway inflammation in asthma. \u003csup\u003e40\u003c/sup\u003eIL-1β promotes inflammation and triggers pyroptotic cell death in both airway epithelial cells and immune cells. This process leads to the release of pro-inflammatory cytokines and chemokines, which worsen asthma symptoms. The activation of the NLRP3 inflammasome is crucial for processing and secreting IL-1β. This links pyroptosis to the dysregulated inflammatory response seen in asthmatic patients.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e MMP9 is known for its role in the degradation of extracellular matrix components and is implicated in various inflammatory diseases, including asthma. \u003csup\u003e41\u003c/sup\u003eIt regulates the infiltration and migration of inflammatory cells and influences the function of immune cells by modulating the activity of intercellular adhesion molecules and chemokine. Elevated levels of MMP9 have been observed in asthmatic patients, correlating with increased bronchial hyperreactivity and airway remodeling. Similarly, ICAM1, an adhesion molecule that facilitates the interaction between immune cells and the endothelium, has been linked to the recruitment of inflammatory cells to the airways, contributing to the sustained inflammatory response in asthma. \u003csup\u003e42\u003c/sup\u003eEnhanced pyroptosis upregulates MMP9 expression, which in turn increases ICAM1 expression on airway epithelial cells. \u003csup\u003e42\u003c/sup\u003eThis process promotes the recruitment of immune cells, including neutrophils and eosinophils. The interplay between MMP9 and ICAM1 may exacerbate airway inflammation and contribute to the pathogenesis of asthma. Nonetheless, the mechanisms by which MMP9 and ICAM1 interact with pyroptotic pathways in asthma inflammation require further exploration.\u003c/p\u003e \u003cp\u003eThis study presents several limitations. First, since our data come from stable adult asthmatics, it remains unclear how these findings apply to patients with asthma exacerbations or to pediatric populations. Second, we cannot explore important clinical characteristics, such as treatment responses and exacerbation risks, due to limited data availability. Thirdly, the sample size in this research was relatively small. Therefore, future studies with larger cohorts and more comprehensive data are essential to confirm our findings and clarify the underlying mechanisms.\u003c/p\u003e \u003cp\u003eIn conclusion, we observed increased airway epithelial pyroptosis in asthma and identified two pyroptosis-related clusters exhibiting distinct clinical characteristics, gene expression profiles, biological functions, and inflammatory profiles. Within these clusters, we identified five PRDEGs. These findings not only emphasize the molecular diversity of asthma and the critical role of pyroptosis in its pathogenesis but also offer liminary evidence and potential targets for therapeutic strategies aimed at regulating pyroptosis in asthma management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eGEO is a public database. Ethical approval was obtained for the patients data included in the database. Users can download data freely for research and publish relevant articles. Since our study is based on open-source data, there are no ethical issues or other conflicts of interest.The animal experiments involved in this study were approved by the Animal Experiment Ethics Committee of Kunming Medical University (No. kmmu20230516).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was supported by the National Natural Science Foundation of China (No. 8236005), Scientific Research Fund Project of Yunnan Provincial Education Department (No. 2024J0200), Basic Research Key Project of Science and Technology Department of Yunnan Province (No. 202501AT070112), Yunnan Health Training Project of High Level Talents (No. H-2024004) and 535 Talent Project of First Affiliated Hospital of Kunming Medical University (No.2023535Q08).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAuthors` contributions\u003c/strong\u003e \u003cp\u003eWYZ and ML conceived the work. ML wrote the manuscript. JYG, XML and JLZ conducted the animal experiments. ML and WYZ participated in data analysis and interpretation. ZL and WYZ revised the manuscript and participated in discussions. WYZ supervised the study. All authors approved the submission of the manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was supported by the National Natural Science Foundation of China (No. 8236005), Scientific Research Fund Project of Yunnan Provincial Education Department (No. 2024J0200), Basic Research Key Project of Science and Technology Department of Yunnan Province (No. 202501AT070112), Yunnan Health Training Project of High Level Talents (No. H-2024004) and 535 Talent Project of First Affiliated Hospital of Kunming Medical University (No.2023535Q08).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWYZ and ML conceived the work. ML wrote the manuscript. JYG, XML and JLZ conducted the animal experiments. ML and WYZ participated in data analysis and interpretation. ZL and WYZ revised the manuscript and participated in discussions. WYZ supervised the study. All authors approved the submission of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe acknowledge the GEO database for providing its platform and the contributors for uploading valuable datasets.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed in this study were obtained from public repositories: GSE 45111dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45111) and GSE 137268 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137268).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789\u0026ndash;858.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuruvilla ME, Lee FE, Lee GB. Understanding asthma phenotypes, endotypes, and mechanisms of disease. Clin Rev Allergy Immunol. 2019;56(2):219\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan YY, Zhang X, Wang J, Wang G, Oliver BG, Zhang HP, et al. Multidimensional assessment of asthma identifies clinically relevant phenotype overlap: a cross-sectional study. J Allergy Clin Immunol Pract. 2021;9(1):349\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu W, Bang S, Bleecker ER, Castro M, Denlinger L, Erzurum SC, et al. Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma. Am J Respir Crit Care Med. 2019;199:1358\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyler SR, Bunyavanich S. Leveraging -omics for asthma endotyping. J Allergy Clin Immunol. 2019;144(1):13\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCookson BT, Brennan MA. Pro-inflammatory programmed cell death. Trends Microbiol. 2001;9:113\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Hu S, Bian Y, et al. Targeting cell death: pyroptosis, ferroptosis, apoptosis and necroptosis in osteoarthritis. Front Cell Dev Biol. 2021;9:789948.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, Zhou L, Wang LL, Zheng PD, Zhang FQ, Mao ZY, et al. Programmed Cell Death in Asthma: Apoptosis, Autophagy, Pyroptosis, Ferroptosis, and Necroptosis. J Inflamm Res. 2023;16:2727\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQueiroz GA, da Silva RR, Pires AO, et al. New variants in NLRP3 inflammasome genes increase risk for asthma and Blomia tropicalis-induced allergy in a Brazilian population. Cytokine X. 2020;2:100032.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng S, Fox D, Man SM. Mechanisms of gasdermin family members in inflammasome signaling and cell death. J Mol Biol. 2018;430(18 Pt B):3068\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SR, Kim DI, Kim SH, et al. NLRP3 inflammasome activation by mitochondrial ROS in bronchial epithelial cells is required for allergic inflammation. Cell Death Dis. 2014;5:e1498.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSebag SC, Koval OM, Paschke JD, et al. Mitochondrial CaMKII inhibition in airway epithelium protects against allergic asthma. JCI Insight. 2017;2:e88297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitter M, Straubinger K, Schmidt S, et al. Functional relevance of NLRP3 inflammasome-mediated interleukin (IL)-1β during acute allergic airway inflammation. Clin Exp Immunol. 2014;178:212\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisenbarth SC, Colegio OR, O\u0026rsquo;Connor W, Sutterwala FS, Flavell RA. Crucial role for the Nalp3 inflammasome in the immunostimulatory properties of aluminium adjuvants. Nature. 2008;453:1122\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa M, Li G, Qi M, Jiang W, Zhou R. Inhibition of the Inflammasome Activity of NLRP3 Attenuates HDM-Induced Allergic Asthma. Front Immunol. 2021;12:718779.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuang J, Cui H, Zhuang L, et al. Bronchial epithelial pyroptosis promotes airway inflammation in a murine model of toluene diisocyanate-induced asthma. Biomed Pharmacother. 2020;125:109925.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim RY, Pinkerton JW, Essilffe AT, et al. Role for NLRP3 Inflammasome-mediated, IL-1β-Dependent Responses in Severe, Steroid-Resistant Asthma. Am J Respir Crit Care Med. 2017;196:283\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLachowicz-Scroggins ME, Dunican EM, Charbit AR, et al. Extracellular DNA, neutrophil extracellular traps, and inflammasome activation in severe asthma. Am J Respir Crit Care Med. 2019;199:1076\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaines KJ, Fricker M, McDonald VM, Simpson JL, Wood LG, Wark PAB, et al. Sputum transcriptomics implicates increased p38 signalling activity in severe asthma. Respirology. 2020;25(7):709\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStelzer G et al. (2016). The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Current Protocols in Bioinformatics, 54(1), 1.30.1\u0026ndash;1.30.33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang YT, Lin IH, Shieh JJ, Lan TH, Syu JN, Huang SS, Hou PH. Harnessing pyroptosis to restrain melanoma through aripiprazole. Apoptosis. 2026;31(1):19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10495-025-02223-9\u003c/span\u003e\u003cspan address=\"10.1007/s10495-025-02223-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang JY, Wang DS, Lin HC, Chen XX, Yang H, Zheng Y, et al. A novel ferroptosis-related gene signature for overall survival prediction in patients with hepatocellular carcinoma. Int J Biol Sci. 2020;16(13):2430\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan YY, Zhang X, Wang J, Wang G, Oliver BG, Zhang HP, et al. Multidimensional assessment of asthma identifies clinically relevant phenotype overlap: a cross-sectional study. J Allergy Clin Immunol Pract. 2021;9(1):349\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinks TSC, Levine SJ, Brusselle GG. Treatment options in type-2 low asthma. Eur Respir J. 2021;57(1):2000528.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTheofani E, Semitekolou M, Samitas K, Mais A, Galani IE, Triantafyllia V, et al. TFEB signaling attenuates NLRP3-driven inflammatory responses in severe asthma. Allergy. 2022;77(7):2131\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng K, Zhang X, Liu Y, Zhang L, Wang G, Feng M, et al. Heterogeneity of paucigranulocytic asthma: a prospective cohort study with hierarchical cluster analysis. J Allergy Clin Immunol Pract. 2021;9(6):2344\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemarche S, Schleich F, Henket M, Paulus V, Van Hees T, Louis R. Detailed analysis of sputum and systemic inflammation in asthma phenotypes: are paucigranulocytic asthmatics really non-inflammatory? BMC Pulm Med. 2016;16:46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai YM, Chiang KH, Hung JY, Chang WA, Lin HP, Shieh JM, et al. Der f1 induces pyroptosis in human bronchial epithelia via the NLRP3 inflammasome. Int J Mol Med. 2018;41:757\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolgate ST, Roberts G, Arshad HS, Howarth PH, Davies DE. The role of the airway epithelium and its interaction with environmental factors in asthma pathogenesis. Proc Am Thorac Soc. 2009;6:655\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee KS, Jin SM, Kim HJ, Lee YC. Matrix metalloproteinase inhibitor regulates inflammatory cell migration by reducing ICAM-1 and VCAM-1 expression in a murine model of toluene diisocyanate-induced asthma. J Allergy Clin Immunol. 2003;111:1278\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Wang P, Xie X, et al. Gasdermin D silencing alleviates airway inflammation and remodeling in an ovalbumin-induced asthmatic mouse model. Cell Death Dis. 2024;15(6):400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Zhang J, Pan H, Yang K, Hu C. Astragaloside IV promotes the pyroptosis of airway smooth muscle cells in childhood asthma by suppressing HMGB1/RAGE axis to inactivate NF-κB pathway. Autoimmunity. 2024;57(1):2387100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao Y, Wang W, Zhang L, Li W. Pyroptosis in asthma: inflammatory phenotypes, immune and non-immune cells, and novel treatment approaches. Front Pharmacol. 2024;15:1452845.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTenero L, Zaffanello M, Piazza M, Piacentini G. Measuring Airway Inflammation in Asthmatic Children. Front Pediatr. 2018;6:196.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarks KE, Cho K, Stickling C, Reynolds JM. Toll-like Receptor 2 in Autoimmune Inflammation. Immune Netw. 2021;21(3):e18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu HM, Zhao CC, Xie QM, Xu J, Fei GH. TLR2-Melatonin Feedback Loop Regulates the Activation of NLRP3 Inflammasome in Murine Allergic Airway Inflammation. Front Immunol. 2020;11:172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIm H, Ammit AJ. The NLRP3 inflammasome: role in airway inflammation. Clin Exp Allergy. 2014;44:160\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalkrishna A, Solleti SK, Singh H, Tomer M, Sharma N, Varshney A. Calcio-herbal formulation, Divya-Swasari-Ras, alleviates chronic inflammation and suppresses airway remodelling in mouse model of allergic asthma by modulating pro-inflammatory cytokine response. Biomed Pharmacother. 2020;126:110063.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Xu L, Zhang J, et al. Pan-cancer analysis of the prognostic and immunological role of matrix metalloproteinase 9. Med (Baltim). 2023;102(30):e34499.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu HF, Ma J, Gu KT, Brismar K. Association of intercellular adhesion molecule 1 (ICAM1) with diabetes and diabetic nephropathy. Front Endocrinol (Lausanne). 2013;3:179.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"asthma, heterogeneity, pyroptosis, molecular subtypes, clustering analysis","lastPublishedDoi":"10.21203/rs.3.rs-9099737/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9099737/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eAsthma is a heterogeneous condition and emerging studies suggest a link between pyroptosis and the disease's development. However, few studies have identified pyroptosis-related asthma subtypes and thoroughly evaluated the role of pyroptosis-related genes (PRGs) in asthma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe utilized the GSE137268 to conduct gene set variation analysis (GSVA) assessing pyroptosis levels in asthma. We then verified the pyroptosis level differences in murine asthma models. Next, we grouped asthma cases from the dataset using consensus clustering based on PRGs. We used Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single sample Gene Set Enrichment Analysis (ssGSEA) to analyze the biological functions and immune status of each subgroup. We constructed a protein-protein interaction (PPI) network to identify hub genes within the identified subtypes, and subsequently validated the identified subtypes using an independent dataset, GSE 45111.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAsthma samples exhibited higher pyroptosis enrichment scores than control samples. The expression of IL-1β, IL-18 and GSDMD-N increased in asthma murine models. We identified two pyroptosis-related asthma subtypes: Cluster 1 and Cluster 2. Cluster 1 showed elevated inflammation, characterized by eosinophilic, neutrophilic, and mixed granulocytic asthma, while Cluster 2 was linked to paucigranulocytic asthma with reduced inflammation. Analyses of GO, KEGG, and ssGSEA revealed clear differences in biological functions and immune profiles between the two subtypes. The PPI network analysis identified five hub genes: \u003cem\u003eIL1B, TLR2, MMP9, ICAM1\u003c/em\u003e, and \u003cem\u003eNLRP3\u003c/em\u003e. Receiver operating characteristic (ROC) analysis further indicated discriminative power of these five genes to differentiate between Cluster 1 and Cluster 2. Notably, the clustering analysis applied to the GSE 45111 yielded results consistent with the subtypes identified in GSE 137268.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAberrant pyroptosis is found in asthma and linked to airway inflammation. The subtypes identified through their PRGs expression profiles showed distinct clinical features, gene expression patterns, biological functions and inflammatory statuses.\u003c/p\u003e","manuscriptTitle":"Identifying pyroptosis-related molecular subtypes in asthma through gene expression profiles: implications for airway inflammation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 13:06:00","doi":"10.21203/rs.3.rs-9099737/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-05T12:52:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T09:40:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284743784300142097945737672632010870312","date":"2026-04-01T03:50:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T18:24:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14898502224366284151008207277245924777","date":"2026-03-26T07:03:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234420533163747184438560934158230018063","date":"2026-03-24T16:35:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311069411337590346638066865650876272224","date":"2026-03-24T12:58:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50739070735978630052899619912553188411","date":"2026-03-24T12:23:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-24T10:35:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T23:28:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T03:30:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2026-03-12T03:37:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64f19e30-6e73-4f15-9dc8-7b9a410f89a7","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-05T13:08:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 13:06:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9099737","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9099737","identity":"rs-9099737","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}