Identification of novel NK cell‑associated targets in severe asthma using integrated bioinformatics analysis

Identification of novel NK cell‑associated targets in severe asthma using integrated bioinformatics analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of novel NK cell‑associated targets in severe asthma using integrated bioinformatics analysis Jiayi Xiang, Shuchang Wang, Yuling Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9303964/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Although there are standard treatments, severe asthma still imposes a substantial burden on global health. Natural killer cells (NK cell) are closely related to the severity of asthma, but the precise mechanisms of their action haven't been fully elucidated. This study aims to identify NK cell‑related hub genes in severe asthma, providing a theoretical basis for understanding the immune mechanisms and finding potential therapeutic targets. Methods We integrated differential expression analysis, WGCNA, LASSO regression and other methods to identify NK cell-related genes. Multiple functional enrichment analysis and single-cell sequencing were used to explore the functional status of NK cells in severe asthma. Additionally, we constructed regulatory networks and predicted potential therapeutic compounds through the DSigDB database and molecular docking. Results We identified IL18R1 as a key NK cell‑related biomarker in severe asthma. Enrichment analyses, including GO, KEGG, and GSVA, consistently demonstrated that NK cell-related pathways were significantly activated in the airways of patients with severe asthma. Single‑cell transcriptomic analysis further confirmed that NK cells were enriched in the peripheral blood in severe asthma, accompanied by upregulated IL18R1 expression. Regulatory network analysis identified NR2F6 as an important transcription factor modulating NK cell function. The miRNA-mRNA regulatory network revealed a complex post-transcriptional regulatory mechanism. In addition, two potential therapeutic compounds, Tolnaftate and Bortezomib, were screened through drug prediction, providing new insights for targeting NK cells to intervene in severe asthma. Conclusion This study provides new theoretical insights into NK cell‑related targets and upstream regulators in severe asthma, and enhances the understanding of the underlying immune mechanisms. However, these findings warrant further experimental investigation. Severe asthma Natural killer cells IL18R1 NR2F6 Bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Asthma is a chronic inflammatory airway disease characterized by reversible airflow limitation and airway hyperresponsiveness. Despite the availability of standard and well‑established treatment regimens, there were 3.6%–10% of asthma patients worldwide develop to severe asthma ( 1 – 4 ). According to the 2025 Global Initiative for Asthma Severe Asthma Guide, severe asthma was defined as patients who failed to achieve disease control after strictly managing contributory factors and regular combined inhalation of high-dose glucocorticoids and long-acting β 2 -agonist, excluding treatment compliance and inhaler techniques, or who developed uncontrolled asthma after de-escalation of treatment ( 5 ). Severe asthma imposes substantial burdens on patients’ quality of life and on global public health. Therefore, it is of great significance to explore the pathophysiological process, immune mechanism and new biomarkers of severe asthma to improve the prognosis of these patients. NK cells are innate lymphocytes with cytotoxicity ( 6 ). Based on the expression of CD56 and CD16, human NK cells can be subdivided into two distinct functional populations. CD56 bright CD16 − NK cells, which are mainly distributed in secondary lymphoid organs and exert regulatory functions on immune responses by secreting cytokines; CD56 dim CD16 + NK cells, which have cytotoxic functions and are mainly located in peripheral blood, can mediate antibody-dependent cellular cytotoxicity and play a major role in controlling acute inflammation ( 7 ). Multiple studies have shown that compared with healthy controls, patients with severe asthma exhibit increased numbers of pro-inflammatory granulocytes and CD4⁺ T cells in bronchoalveolar lavage fluid (BALF), whereas the total number of NK cells is significantly reduced. Although the proportion of CD56 dim NK cell subsets is relatively increased in severe asthma, their actual cytotoxic capacity is markedly impaired. For instance, the ability to induce the apoptosis of eosinophils has decreased significantly. Moreover, it has been confirmed that glucocorticoids can suppress NK cell, further weakening the capacity of NK cells to eliminate activated leukocytes and pathogens in severe asthma ( 8 – 10 ). The quantity and functional status of NK cells are closely associated to the severity of asthma. The proportion of activated (CD69+) NK cells in circulation is significantly negatively correlated with the ratio of forced expiratory volume/forced vital capacity (FEV 1 /FVC) ( 11 ). However, some studies have pointed out that there was no significant difference in the total number of NK cells among healthy control, mild to moderate asthma, and severe asthma. But the ratio of key cytotoxic NK cell subset, CD56 dim/neg CD16 br , was significantly decreased in severe asthma ( 12 ). Another study utilized the CIBERSORT algorithm to assess the infiltration of immune cells in BALF and found that CD8 T cells, resting and activated memory CD4 T cells, activated NK cells, and activated mast cells were significantly enriched in severe asthma ( 13 ). In conclusion, the specific mechanism of NK cells in severe asthma has not been fully elucidated. There is still a large gap in the search for NK cell-related biomarkers and potential therapeutic targets in severe asthma. We employed integrated bioinformatics approaches to identify key genes linked to NK cells and potential therapeutic agents in severe asthma. Single-cell transcriptome analysis was used to investigate the expression characteristics of key genes under different disease states. Additionally, we constructed NK cell‑related transcription factor (TF)-mRNA and miRNA-mRNA regulatory networks in severe asthma. These findings aim to provide new theoretical insights into the immune mechanisms of severe asthma and to facilitate the identification of novel therapeutic targets. 2. Materials and Methods 2.1 Data Acquisition In this study, we obtained GSE137268, GSE76262, GSE158752, GSE43696, GSE67940, and GSE172495 datasets from the Gene Expression Omnibus (GEO) of NCBI. Among them, GSE137268 and GSE76262 were used as the training set, comprising induced sputum samples from 36 healthy controls and 105 patients with severe asthma. GSE158752, GSE43696, and GSE67940 served as the validation set, consisting of bronchial epithelial cell samples. GSE172495 was used for single-cell transcriptome analysis of peripheral blood mononuclear cells (PBMCs). Detailed sample information for each dataset is provided in Supplementary Table 1. We also obtained 329 NK cell-associated genes from previous studies (Supplementary Table 2). 2.2 Data Processing and the Identification of NK cell-Related DEGs GSE137268 was normalized using the normalizeBetweenArrays function in the limma package. GSE76262 was normalized using the rma function in the oligo package. The two datasets were then merged and the batch effect was corrected using the ComBat function in the sva package. Principal component analysis (PCA) was performed utilizing the FactoMineR package, and boxplots of the combined data sets were drawn to visualize sample clustering after batch correction and confirm the effectiveness of batch effect removal. The Gene Set Variation Analysis (GSVA) was used to compare the enrichment scores of the NK cell-related gene sets between the control and severe asthma groups with 329 NK cell-associated genes as a reference. Differentially expressed genes (DEGs) between severe asthma and healthy control were identified utilizing the limma package. The criteria for DEGs were as follows: |log2(Fold Change) | > 0.5 and adjusted P-value < 0.05. The ggplot2 package was used to generate the volcano plots and the pheatmap package was used to generate the heatmaps to visualize the distribution of DEGs. To identify NK cell-related differentially expressed genes (NKRDEGs), we intersected DEGs and 329 NK cell-associated genes. Venn diagram was created using the ggvenn package to illustrate the overlap, and heatmap was used to visualize the expression patterns of 24 NKRDEGs. 2.3 Functional Enrichment Analysis We ranked all genes according to the degree of differential expression. We downloaded the gene set “c5.go.bp.v2026.1.Hs.symbols.gmt” from the Molecular Signatures Database (MSigDB). The Gene Set Enrichment Analysis (GSEA) was performed using the clusterProfiler package to evaluate the distribution of gene sets in predefined gene lists and for visualization. In our analysis, we specified a sample permutation number of 1000 and set the threshold for significance at an adjusted P value of less than 0.05. With clusterProfiler package, we converted NKRDEGs in Gene Symbol format into Entrez gene IDs, and then performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The results were visualized by ggplot2 package and pathview package. 2.4 Weighted Gene Co-expression Network Analysis (WGCNA) We performed WGCNA analysis on the expression matrix to identify gene modules associated with severe asthma. Only genes with the top 5000 median absolute deviation (MAD) were retained. Then, we conducted hierarchical clustering of the samples to exclude outliers. The soft thresholding power (β) was selected based on a fit index > 0.85. Subsequently, we computed the adjacency matrix and transformed it into the Topological Overlap Matrix (TOM) to enhance the robustness of the network. The dynamic tree cutting was applied to cluster genes into modules, and highly similar modules were merged. Eight modules related to severe asthma phenotypes were identified, and the association between gene modular and phenotypes trait was visualized by heatmap and scatter plots. The final WGCNA genes were derived from the magenta module, which exhibited the most significant positive correlation, and the blue module, which exhibited the most significant negative. The overlapping genes were determined by the intersection of the NKRDEGs and WGCNA-derived genes. 2.5 Identification of Significant Genes and Validation Spearman correlation analysis was performed on the 12 overlapping genes with corrplot package. And we uploaded them to the Metascape ( https://metascape.org/ ) to acquire enrichment analysis results. The 12 overlapping genes were submitted to the STRING database ( https://cn.string-db.org/ ) to construct a protein-protein interaction (PPI) network, with a minimum required interaction score of 0.4. The PPI network, which comprised 6 nodes and 8 edges, was visualized using Cytoscape software (version v3.10.4). Least Absolute Shrinkage and Selection Operator (LASSO) regression is a widely used method for variable selection in high-dimensional data. The overlapping genes were subjected to LASSO regression analysis using the glmnet package, with the penalty parameter α set to 1. The optimal λ value was determined through 10‑fold cross‑validation, leading to the identification of 4 key genes as potential biomarkers associated with severe asthma. The final candidate genes were determined by integrating 6 hub genes from the PPI network and 4 key genes identified via LASSO regression. The expression levels of significant genes were then visualized using boxplots to compare the differences between the severe asthma and control groups in both the training cohorts and the validation cohorts (GSE158752, GSE43696, and GSE67940). 2.6 Construction of TF-mRNA and miRNA-mRNA Regulatory Networks To construct the regulatory network of TFs targeting the 24 NKRDEGs, the gene list was submitted to the NetworkAnalyst platform ( https://www.networkanalyst.ca ). The TF-mRNA interactions were predicted and generated from the ENCODE database. Concurrently, the miRWalk ( http://mirwalk.umm.uni-heidelberg.de/ ) and ENCORI ( https://rnasysu.com/encori/ ) databases were used to predict miRNAs targeting the 24 NKRDEGs. And only those consistently recorded in both databases were retained. Ultimately, the TF-mRNA and miRNA‑mRNA regulatory networks were refined for visualization using Cytoscape software. 2.7 Drug Prediction and Molecular Docking To identify potential small-molecule compounds targeting NKRDEGs for the treatment of severe asthma, IL18R1 was submitted to the Enrichr platform ( https://maayanlab.cloud/Enrichr/ ). Candidate therapeutic compounds were predicted based on Drug Signatures database (DSigDB). Compounds with P.value ≤ 0.05 were selected and summarized in Table 1 . The three-dimensional (3D) structure of the candidate compound tolnaftate was retrieved from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) in SDF format. The protein structure of IL18R1 was obtained from the PDB database ( https://www.rcsb.org/ ) in PDB format. Molecular docking between tolnaftate and IL18R1 was performed on the CB-Dock2 platform ( http://cadd.labshare.cn/cb-dock2/ ), yielding a binding energy of − 7.7 kcal/mol. The Vina score below − 5.0 kcal/mol indicates favorable interaction potential between protein targets and candidate compounds. 2.8 Single-Cell Sequencing Analysis Single-cell RNA sequencing data of PBMCs samples from the GSE172495 dataset were downloaded and processed using the Seurat package for quality control and data preprocessing. Cell quality was assessed based on the percentage of mitochondrial genes, and cell cycle effects were evaluated using CellCycleScoring, revealing no significant bias in the dataset. Cells meeting the criteria of 500 ≤ nCount_RNA ≤ 4000 and mitochondrial gene percentage ≤ 5% were retained for downstream analysis (Supplementary Figs. 4A, B). PCA was performed on the top 2,000 highly variable genes. An elbow plot was used to determine the significant principal components (Supplementary Fig. 4C). Subsequently, uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE) were applied for dimensional reduction and clustering. Automated cell type annotation was initially performed using SingleR with the HumanPrimaryCellAtlas reference database. Subsequently, manual annotation was conducted with known genetic markers to improve classification accuracy, leading to the identification of major cell populations, including NK cells. NK cell subsets were then subjected to reclustering analysis to further explore their heterogeneity. Finally, the expression patterns of significant genes were examined across different experimental conditions (HC_Baseline, HC_polyIC, SA_Baseline, and SA_polyIC) as well as between disease states (HC vs. SA). 2.9 Statistical Analysis Statistical analyses were conducted using R (version 4.5.1). Wilcoxon rank-sum test was applied to evaluate differences between two groups. Correlations between variables were evaluated with Spearman's rank correlation test. P < 0.05 was deemed statistically significant throughout the study. 3. Results 3.1 Screening and Functional Enrichment Analysis of NKRDEGs We successfully integrated the GSE137268 and GSE76262 datasets (Figs. 1 A, B; Supplementary Figs. 1A, B) and extracted the expression data from the healthy control and severe asthma groups for analysis. Using 329 NK cell-associated genes as the background gene set, we calculated a NK cell Score for each sample to reflect the relative enrichment of NK cell-associated genes expression patterns. GSVA results showed that the NK cell Score was been proven higher in the severe asthma group (Fig. 1 C). Differential expression analysis between the severe asthma and healthy control groups identified 766 DEGs, comprising 284 upregulated and 482 downregulated genes (Fig. 1 D; Supplementary Fig. 1C). The Venn diagram analysis revealed that 24 of these DEGs were NK cell–associated genes (Fig. 1 E), most of which were upregulated in the severe asthma (Fig. 1 F). GSEA on the whole transcriptome indicated significant enrichment of NK cell–related pathways in the severe asthma, including natural killer cell activation, natural killer cell mediated immunity, and natural killer cell activation involved in immune response (Fig. 2 A). GO analysis of the 24 NKRDEGs further demonstrated significant enrichment in biological processes such as lymphocyte mediated immunity, cell killing, and natural killer cell mediated cytotoxicity (Fig. 2 B). Cellular components only enriched in the plasma membrane signaling receptor complex and the immunological synapse. Enriched molecular function included cytokine activity, cytokine receptor binding, and chemokine activity. KEGG pathway analysis revealed that these NKRDEGs were primarily enriched in pathways such as viral protein interaction with cytokine and cytokine receptor, TNF signaling pathway, and natural killer cell mediated cytotoxicity (Fig. 2 C; Supplementary Fig. 1D). These findings confirm that NK cells actively participate in the pathophysiological process of severe asthma. 3.2 Identification of Gene Modules by WGCNA WGCNA was performed using the grouping information as the clinical trait. Sample clustering was conducted to remove outliers, with a height cutoff of 75 (Supplementary Figs. 2A, B). By evaluating the scale‑free topology model fit and mean connectivity, the optimal soft‑thresholding power was determined to be β = 8 (Fig. 3 A; Supplementary Fig. 2C). A minimum module size of 30 genes and a dissimilarity threshold of 20% for merging similar modules were set, and the dynamic tree‑cut algorithm was applied, yielding a total of eight modules associated with severe asthma (Figs. 3 B, C; Supplementary Figs. 2D–F, Table 3). The module-trait relationship analysis revealed that the magenta module exhibited the strongest positive correlation with severe asthma, whereas the blue module showed the strongest negative correlation (Fig. 3 D). Therefore, these two modules were identified as key modules, and their constituent genes were considered WGCNA‑derived genes. Scatter plots illustrated the correlation between gene significance and module membership (Figs. 3 E, F). 3.3 Identification of Significant Genes and Validation The intersection of WGCNA‑derived genes and NK cell–associated genes yielded 53 common genes. These 53 genes were further intersected with NKRDEGs, generated 12 overlapping genes (Figs. 4 A, B). Correlation analysis showed that these 12 genes were significantly correlated with each other (Fig. 4 C). Functional enrichment analysis revealed that these genes were primarily enriched in GO terms such as natural killer cell activation, regulation of T cell mediated immunity, and cell population proliferation (Fig. 4 D). A PPI network of overlapping genes was constructed based on the STRING database, which consisted of 6 nodes and 8 edges (Fig. 4 E; Supplementary Fig. 3A). With lambda.1se as the optimal λ value, 4 diagnostic core genes: CSF1, IL18R1, ITGAL, and HDC, were screened out via LASSO regression model (Figs. 4 F, G). By integrating the results from the PPI and LASSO regression model, CSF1, IL18R1, and ITGAL were ultimately identified as 3 significant genes related with severe asthma (Fig. 4 H). Three datasets (GSE158752, GSE43696, and GSE67940) were used to validate the expression levels of the 3 significant genes. The results showed that only IL18R1 exhibited a statistically significant difference between the severe asthma and healthy control groups (Figs. 4 I, J; Supplementary Figs. 3B–E), suggesting that upregulation of IL18R1 may represent a unique NK cell‑related biomarker for severe asthma. 3.4 TF-mRNA and miRNA-mRNA Regulatory Network in Severe Asthma In order to elucidate the potential regulatory mechanisms of 24 NKRDEGs, we constructed TF-mRNA and miRNA-mRNA regulatory networks respectively. The TF–mRNA regulatory network comprised 182TFs, 19 target genes, and 335 interaction edges (Fig. 5 A). Among these, NR2F6 was identified as the most prominent TF, regulating 6 NKRDEGs. In addition, MTA2, TFDP1, KLF16, ZNF589, ZBTB11, and MBD1 also played important roles, each regulating 5 NKRDEGs. As for the miRNA-mRNA regulatory network, there were 80 miRNA-mRNA interaction pairs, involving 10 target genes and 72 miRNAs (Fig. 5 B). These findings provide important insights into the transcriptional and post‑transcriptional regulatory mechanisms of NK cell‑associated genes in severe asthma. 3.5 Screening Potential Therapeutic Drugs Table 1 Drug prediction of IL18R1 Term P.value Combined.Score fluoride CTD 00005982 0.002749954 117599.13 Calcimycin CTD 00005287 0.004949933 105642.09 AGN-PC-0JHFVD BOSS 0.009549901 92136.10 tolnaftate PC3 UP 0.013299882 85250.88 NICKEL CHLORIDE CTD 00001064 0.019949854 76728.77 Phorbol 12-myristate 13-acetate CTD 00006852 0.024149840 72671.11 ARSENIC CTD 00005442 0.042649798 60403.67 Bortezomib CTD 00003736 0.043899795 59772.42 Silica CTD 00006678 0.044899794 59279.66 Using the DSigDB database from the Enrichr website, we searched for potentially effective drugs targeting IL18R1. A total of 9 candidate compounds with P < 0.05 were identified (Table 1 ), among which Tolnaftate and Bortezomib are clinically approved drugs. Molecular docking results revealed that Tolnaftate exhibited strong binding affinity to the IL18R1 protein, with a binding energy of − 7.7 kcal/mol (Fig. 5 C), suggesting that this agent may exert a potential therapeutic role in severe asthma by targeting IL18R1. 3.6 Single-Cell Sequencing Analysis Revealed NK cells Infiltration in Severe Asthma To validate the expression pattern of IL18R1 at single‑cell RNA, we analyzed PBMCs samples from the GSE172495 dataset. Integrating automated and manual annotation, five major immune cell populations were identified: CD4⁺ T cells, CD8⁺ T cells, B cells, NK cells, and monocytes. UMAP clearly visualized the distribution of these cell populations, and dot plots of cell markers confirmed the accuracy of cell type annotation (Figs. 6 A-C; Supplementary Figs. 4D-G). Quantitative analysis of cellular composition under different disease and stimulation status revealed that, both at baseline and after polyIC stimulated, the absolute number and relative proportion of NK cells were significantly higher in the severe asthma compared to the healthy control. Moreover, polyIC stimulation further increased the abundance of NK cells in both groups (Fig. 6 D). Further analyzing the distribution of IL18R1 expression in each cell type, we found that it was expressed at the highest level in NK cells, which was significantly higher than other immune cells (Figs. 6 E, F). Subgroup analysis of the NK cell population revealed more pronounced IL18R1 expression in NK cells from the severe asthma group (Fig. 6 G). These findings suggest that NK cells may play an important role in the peripheral immune response of patients with severe asthma. 4. Discussion In this study, we systematically explored the potential role of NK cells in the pathogenesis of severe asthma by integrating multiple bioinformatics approaches, including differential expression analysis, WGCNA, and LASSO regression. After multiple screening and validation with external datasets, IL18R1 was identified as a key biomarker. Functional enrichment analysis, GSEA analysis and GSVA analysis consistently revealed activation of NK cell-related pathways in the airways of severe asthma, ranging from partial differential genes to the whole gene set level. Subsequently, at the cellular level, single-cell RNA sequencing confirmed that NK cells were significantly activated in peripheral blood of severe asthma, and the expression of IL18R1 was significantly upregulated. Finally, based on online databases, we predicted the key transcription factor NR2F6, as well as two compounds with potential therapeutic value, Tolnaftate and Bortezomib, providing new clues for targeted intervention of NK cells in severe asthma. Previous studies mostly focused on the changes of NK cells and their subsets in BALF of patients with severe asthma before and after treatment ( 8 – 12 ), whereas investigations exploring the molecular markers linking NK cells to severe asthma remain limited. Furthermore, existing bioinformatics studies on severe asthma have predominantly concentrated on eosinophils and neutrophils ( 14 – 19 ), which are relatively well‑established immune cells in asthma research. We focused on NK cell-related molecules and identified IL18R1 as a key biomarker in severe asthma. The protein encoded by IL18R1 belongs to the interleukin-1 receptor family of cytokine receptors and can specifically bind to IL-18. The IL-18/IL18R1 signaling axis plays a complicated role in immune regulation. On one hand, in synergy with IL‑12, IL‑18 induces IFN‑γ production by NK cells and Th1 cells, thereby promoting Th1‑type immune responses ( 20 , 21 ). On the other hand, IL‑18 also facilitates the secretion of Th2‑type cytokines (such as IL‑4, IL‑5, and IL‑13), enhances IgE synthesis, and increases airway hyperresponsiveness, thus participating in the immune response and inflammatory processes of T2‑type asthma ( 22 – 24 ). Under physiological conditions, NK cells express IL18R1, and IL-18 can cooperate with IL-15 to promote NK cells proliferation, contribute to their functional maturation, and enhance perforin-mediated cytotoxicity ( 25 – 28 ). Despite the above immune-enhancing effects of IL-18, sustained IL-18 stimulation may lead to NK cells dysfunction and promote the reduction of NK cells by inducing their apoptosis or "self-attack", which subsequently amplifies type 2 inflammatory responses ( 29 ). Consistent with this, our immune infiltration analysis of induced sputum samples from severe asthma revealed no significant increase in the proportion of NK cells, but rather a decrease in the percentage of activated NK cells (Supplementary Figs. 5A–C). These findings are in agreement with previous reports by Melody G. Duvall and Laura Bergantini et al. ( 8 , 12 ), jointly supporting the above notion. Accordingly, we hypothesize that the key factor in severe asthma is not simply a change in NK cell numbers, but rather the dysregulation of the IL‑18/IL18R1 signaling axis, leading to abnormal functions of NK cells and immune imbalance. NR2F6 was identified as a key upstream transcription factor in our TF-mRNA regulatory network. Although the network did not show that NR2F6 directly regulated IL18R1 expression, it closely interacted with IL18 and IL18RAP. Johannes Woelk et al. ( 30 ) found that NR2F6-deficient mice had insufficient IL-15 due to reduced numbers of cDC1 and macrophages in the spleen, which in turn caused impaired peripheral maturation of NK cells. At the same time, NR2F6 can directly inhibit the expression of the activating receptor NKp46, which is a key molecule for NK cell-mediated cytotoxicity. With exogenous IL-15 supplementation, NR2F6-deficient NK cells not only matured normally, but also exhibited enhanced effector function owing to the release of NKp46 inhibition. These findings inspire us that simultaneously regulating NR2F6 and IL-15 is expected to become a potential supplementary strategy for regulating NK cell function in patients with severe asthma. However, whether NR2F6 indirectly regulates the expression of IL18R1, and whether specific interactions occur among NR2F6, IL18R1, IL18, and IL18RAP in NK cells from patients with severe asthma remain to be further experimentally verified. Although bortezomib was predicted as a potential compound, previous animal studies have demonstrated limited efficacy in allergic bronchial asthma ( 31 ). Therefore, we focused on the classical antifungal drug tolnaftate. Currently, no literature has reported a functional association between tolnaftate and IL18R1, NK cells, or asthma. However, the prediction from DSigDB suggested a potential interaction between tolnaftate and IL18R1, and the molecular docking result also revealed they have a high binding affinity. These findings only provide a preliminary screening basis, and further biological experiments are required for verification. There are several limitations in our study. First, we did not conduct cellular experiments or clinical trials to validate the results. Second, whether changes in NK cells in peripheral blood can accurately reflect alterations in airway remains to be further investigated. Finally, the drug prediction results based on IL18R1 are unsatisfactory. It may be necessary to supplement additional analyses targeting the IL-18/IL18R1 signaling axis and NR2F6 to expand the scope of potential candidates. Conclusions In this study, we identified IL18R1 as a key NK cell‑related biomarker and confirmed significant activation of NK cell‑associated pathways in the airways of patients with severe asthma. Furthermore, we explored the potential regulatory roles of the IL‑18/IL18R1 signaling axis and the transcription factor NR2F6 in NK cell function in severe asthma. Candidate compounds with potential therapeutic value were also identified, providing clues for subsequent experimental validation and intervention strategies. Abbreviations NK cell, Natural killer cells LASSO, Least absolute shrinkage and selection operator DSigDB, Drug SIGnatures DataBase GO, Gene ontology KEGG, Kyoto encyclopedia of genes and genomes GSVA, Gene set variation analysis miRNA, microRNA TF, Transcription factor BALF, Bronchoalveolar lavage fluid FEV 1 /FVC, Forced expiratory volume/forced vital capacity PBMCs, Peripheral blood mononuclear cells PCA, Principal component analysis DEGs, Differentially expressed genes NKRDEGs, NK cell-related differentially expressed genes MSigDB, Molecular Signatures Database GSEA, Gene set enrichment analysis WGCNA, Weighted gene co-expression network analysis MAD, median absolute deviation TOM, Topological overlap matrix PPI, Protein-protein interaction UMAP, Uniform manifold approximation and projection t-SNE, t-Distributed stochastic neighbor embedding HC, Healthy control SA, Severe asthma IL-18, Interleukin-18 IL-12, Interleukin-12 IFN‑γ, Interferon-γ IL‑4, Interleukin-4 IL‑5, Interleukin-5 IL‑13, Interleukin-13 IL-15, Interleukin-15 IgE, Immunoglobulin E Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials Not applicable Competing interests The authors declare that they have no competing interests Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions JYX designed and performed data curation, formal analysis, and visualization, and was a major contributor in writing the manuscript. SCW contributed to conceptualization and validation. YLZ supervised the study, and reviewed and edited the manuscript. All authors read and approved the final manuscript. Acknowledgements We acknowledge contributors for openly shared data from the GEO database. References Kardas G, Kuna P, Panek M. Biological Therapies of Severe Asthma and Their Possible Effects on Airway Remodeling. Front Immunol. 2020;11. Koh MS, Chan KKP, Fukunaga K, Kim S-H, Lan LTT, Omar A et al. Understanding disease burden, challenges in current treatment strategies and call for action for management of severe asthma in Asia: a position statement from Asian respiratory experts. Front Allergy. 2026;7. Chung KF, Wenzel SE, Brozek JL, Bush A, Castro M, Sterk PJ, et al. International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma. 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Han Y, Ji X, Rao J, Zhang Y, Chen X, Gong F. TNFAIP3, PADI4, and CXCR4 as biomarkers of neutrophil extracellular traps in severe asthma. Int Immunopharmacol. 2026;170. Yoshimoto T, Mizutani H, Tsutsui H, Noben-Trauth N, Yamanaka K, Tanaka M, et al. IL-18 induction of IgE: dependence on CD4 + T cells, IL-4 and STAT6. Nat Immunol. 2000;1(2):132–7. Nakanishi K, Yoshimoto T, Tsutsui H, Okamura H. Interleukin-18 regulates both Th1 and Th2 responses. Annu Rev Immunol. 2001;19:423–74. Zhu G, Whyte MKB, Vestbo J, Carlsen K, Carlsen K-H, Lenney W, et al. Interleukin 18 receptor 1 gene polymorphisms are associated with asthma. Eur J Hum Genet. 2008;16(9):1083–90. Lin K, Wang T, Tang Q, Chen T, Lin M, Jin J, et al. IL18R1-Related Molecules as Biomarkers for Asthma Severity and Prognostic Markers for Idiopathic Pulmonary Fibrosis. J Proteome Res. 2023;22(10):3320–31. Mete F, Ozkaya E, Aras S, Koksal V, Etlik O, Baris I. Association between gene polymorphisms in TIM1, TSLP, IL18R1 and childhood asthma in Turkish population. Int J Clin Exp Med. 2014;7(4):1071–7. Wang J, Zhan M, Liu S, Wang S, He S. Role of IL-18 in Asthma. Med Res Archives. 2024;12(12). Takeda K, Tsutsui H, Yoshimoto T, Adachi O, Yoshida N, Kishimoto T, et al. Defective NK cell activity and Th1 response in IL-18-deficient mice. Immunity. 1998;8(3):383–90. Tomura M, Zhou XY, Maruo S, Ahn HJ, Hamaoka T, Okamura H et al. A critical role for IL-18 in the proliferation and activation of NK1.1 + CD3- cells. Journal of immunology (Baltimore, Md: 1950). 1998;160(10):4738-46. Hyodo Y, Matsui K, Hayashi N, Tsutsui H, Kashiwamura S, Yamauchi H et al. IL-18 up-regulates perforin-mediated NK activity without increasing perforin messenger RNA expression by binding to constitutively expressed IL-18 receptor. Journal of immunology (Baltimore, Md: 1950). 1999;162(3):1662-8. Bähler L, Schärli S, Luther F, Bertschi NL, Skabytska Y, Roediger B, et al. IL-18 in atopic dermatitis—a multifaceted driver of skin inflammation. J Allergy Clin Immunol. 2025;156(5):1160–72. Woelk J, Hornsteiner F, Aschauer-Wallner S, Stoitzner P, Baier G, Hermann-Kleiter N. Regulation of NK cell development, maturation, and antitumor responses by the nuclear receptor NR2F6. Cell Death Dis. 2025;16(1). Wegmann M, Lunding L, Orinska Z, Wong DM, Manz RA, Fehrenbach H. Long-Term Bortezomib Treatment Reduces Allergen-Specific IgE but Fails to Ameliorate Chronic Asthma in Mice. Int Arch Allergy Immunol. 2012;158(1):43–53. Supplementary Files SupplementaryFigures.docx SupplementaryTable1.docx SupplementaryTable2.xlsx SupplementaryTable3.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 04 Apr, 2026 First submitted to journal 02 Apr, 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-9303964","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623407080,"identity":"a656510a-a9e7-4e39-ae95-fb7146be5e6c","order_by":0,"name":"Jiayi Xiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACNvbmgw8+VNjwABkHiNPCx3Ms2XDGmTQ5ICOBOC1yEjlm0rxth4yBDAMiHQZUKTmz7UBim0TOxxtvGOzkdBsIaeF5VmDw4dydxDaet5st5zAkG5sdIKSFPXlD4oyyZ4lt7LnbpHkYDiRuI6iFIcHgMA/b4cQ2hpxnRGrhSDFs5mk7bMzGkcNGpBZgIDOCAhnIMLacY0CEX+Tbm4//AEUlkPHwxpsKOzmCWlCABA+RUYOshVQdo2AUjIJRMCIAAEkFRNn3cccdAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-9966-3662","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Xiang","suffix":""},{"id":623407081,"identity":"18a33087-6a78-47ce-8145-817b2bdaa08d","order_by":1,"name":"Shuchang Wang","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuchang","middleName":"","lastName":"Wang","suffix":""},{"id":623407082,"identity":"91c603c7-4fff-4df2-86c3-0926e080d491","order_by":2,"name":"Yuling Zhang","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuling","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-04-02 13:52:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9303964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9303964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107634341,"identity":"c9083fed-0ec2-45ed-876d-f374c60edd14","added_by":"auto","created_at":"2026-04-23 12:21:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2361451,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed NK cell-related genes. (A) PCA plot of GSE137268 and GSE76262 before batch effect correction. (B) PCA plot after batch effect correction, showing effective integration of the two datasets. (C) GSVA analysis comparing NK cell scores between the two groups. The score was significantly higher in severe asthma. (D) Volcano plot visualizing DEGs between the two groups. Red dots represent upregulated genes, and blue dots represent downregulated. The threshold was set as |log₂ (Fold Change) | \u0026gt; 0.5 and adjusted P‑value \u0026lt; 0.05. (E) Venn diagram showing the overlap between DEGs and 329 NK cell‑related genes. A total of 24 NKRDEGs were identified. (F) Heatmap of the 24 NKRDEGs, showing that most of them were upregulated in severe asthma. ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/89dd32a28142bd1e23c5fd6e.png"},{"id":107707914,"identity":"22eb23ef-47a4-4662-a078-04d8780ef02d","added_by":"auto","created_at":"2026-04-24 09:21:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1823893,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of NKRDEGs. (A) GSEA of the whole gene set, showing that NK cell-associated pathways, such as natural killer cell activation, are significantly activated in severe asthma. (B) Bar plot of GO enrichment analysis of the 24 NKRDEGs. (C) Bubble plot of KEGG pathway enrichment analysis of the 24 NKRDEGs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/62ee736a071deddeae2deff5.png"},{"id":107705798,"identity":"32dc6978-9ac3-41de-9b65-5d4b6d24581a","added_by":"auto","created_at":"2026-04-24 09:15:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2677307,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA was performed to identify severe asthma‑related co‑expression modules and their constituent genes. (A) Determination of the optimal soft‑thresholding power for network construction. Left panel: scale‑free topology model fit index (y‑axis) as a function of the soft‑thresholding power (x‑axis); a power of 8 was selected as optimal. Right panel: mean connectivity (y‑axis) decreases with increasing soft‑thresholding power. (B) Merging of modules with a dissimilarity less than 20%. (C) Modules after merging. Gene dendrogram and module colors obtained by hierarchical clustering based on topological overlap, where branches represent genes and the colors below denote assigned modules. Eight distinct co‑expression modules were identified. (D) Module–trait correlation heatmap showing correlations between module eigengenes and phenotypic traits (healthy control and severe asthma). The magenta module exhibited the strongest positive correlation with severe asthma, whereas the blue module showed the strongest negative correlation. (E, F) Scatter plots of gene significance for severe asthma versus module membership in the magenta module (E) and blue module (F).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/39f6f09d0457485ddc5884c3.png"},{"id":107705800,"identity":"64460328-8bea-419a-9e63-ac776a5b7013","added_by":"auto","created_at":"2026-04-24 09:15:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1122250,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and validation of significant genes. (A) Venn diagram showing the overlap between WGCNA‑derived genes and NK cell‑related genes, yielding 53 common genes. (B) Venn diagram showing the overlap between the 53 common genes and the 24 NKRDEGs, yielding 12 overlapping genes. (C) Correlation matrix of the 12 overlapping genes. Color intensity and asterisks indicate the strength and significance level of correlations. (D) Functional enrichment analysis of the overlapping genes. (E) Protein‑protein interaction (PPI) network of the overlapping genes, consisting of 6 nodes and 8 edges. (F) LASSO coefficient profiles of the 12 overlapping genes. The x‑axis represents log(λ), and the y‑axis represents the coefficients. Each curve shows the trajectory of a coefficient for a gene as log(λ) increases. (G) Cross‑validation error curves for LASSO regression. The left vertical dashed line (lambda.min) corresponds to the λ with minimum mean cross‑validated error, and the right vertical dashed line (lambda.1se) corresponds to the largest λ within one standard error of the minimum, which was chosen as the optimal λ. The numbers at the top indicate the number of non‑zero coefficients at each λ. (H) Expression levels of three significant genes, CSF1, IL18R1, and ITGAL, in induced sputum from healthy control and severe asthma (training set). Boxplots show that CSF1 and IL18R1 were significantly upregulated, while ITGAL was downregulated in severe asthma. (I, J) Expression of the three significant genes in validation datasets. Both the GSE158752 dataset (I) and the combined GSE43696 and GSE67940 dataset (J) revealed that only IL18R1 was significantly upregulated in bronchial epithelial cells of patients with severe asthma. ****P \u0026lt; 0.0001, ns (not significant).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/dce5f7291e2e3eac56230e7a.png"},{"id":107634349,"identity":"6088fc3d-a9b5-4575-835e-321a4ad978a7","added_by":"auto","created_at":"2026-04-23 12:21:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9273489,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of regulatory networks and molecular docking model. (A) TF‑mRNA regulatory network of the 24 NKRDEGs, comprising 182 TFs, 19 target genes, and 335 interaction edges. Light blue diamond nodes represent TFs, and orange circular nodes represent target genes. (B) The miRNA‑mRNA regulatory network consists of 10 target genes, 72 miRNAs, and 80 regulatory edges. Lavender circular nodes represent target genes, and grass green circular nodes represent miRNAs. (C) Molecular docking of tolnaftate to IL18R1 with a binding energy of −7.7 kcal/mol. These findings provide insights into the molecular regulatory mechanisms underlying NK cell involvement in severe asthma and highlight potential therapeutic targets.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/f75198b4658675ae26d2bc53.png"},{"id":107707544,"identity":"5bb3c39f-e0a7-4d17-b1e3-4def5216b588","added_by":"auto","created_at":"2026-04-24 09:20:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1559578,"visible":true,"origin":"","legend":"\u003cp\u003eSingle‑cell transcriptomic analysis of PBMCs from severe asthma and healthy control. (A) UMAP plot showing five major immune cell types identified by manual annotation: CD4⁺ T cells, CD8⁺ T cells, B cells, NK cells, and monocytes. (B) UMAP plots split by groups, illustrating the distribution of cells under different conditions (HC_Baseline, HC_polyIC, SA_Baseline, SA_polyIC). (C) Dot plot of marker genes for each cell type, confirming the accuracy of cell type annotation. Dot size represents the percentage of cells expressing the gene, and color intensity indicates the average expression level. (D) Bar charts illustrating absolute cell numbers (left) and relative cell proportions (right) for each cell type under different conditions. The absolute number and relative proportion of NK cells in severe asthma were significantly higher than those in healthy control, particularly after polyIC stimulation. (E) Dot plot showing the expression of IL18R1 across different cell types. IL18R1 is predominantly expressed in NK cells. (F) UMAP feature plot showing the expression distribution of IL18R1 across the entire PBMCs population, with color intensity indicating expression level. (G) UMAP feature plots of IL18R1 expression split by disease status (HC vs. SA), demonstrating upregulated IL18R1 expression in NK cells from patients with severe asthma.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/b2d1a14a5eaaf29582a2b177.png"},{"id":107709429,"identity":"c43f5077-d9ad-4ff9-9c3d-8eec4d26d2c7","added_by":"auto","created_at":"2026-04-24 09:35:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18831632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/8c42d2cf-d8df-41ad-8ac5-0b8497011378.pdf"},{"id":107634342,"identity":"bd29b57b-8c86-46a7-b73a-c6f06f28573d","added_by":"auto","created_at":"2026-04-23 12:21:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2818274,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/0dd64ac6496bbf16f05afbdc.docx"},{"id":107634344,"identity":"34229ab2-a120-4d02-8236-e3e7bc866940","added_by":"auto","created_at":"2026-04-23 12:21:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18078,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/f3207441c8eea630fcb93486.docx"},{"id":107634345,"identity":"02b0bf09-693e-4a88-b3a6-646b1c7a4940","added_by":"auto","created_at":"2026-04-23 12:21:52","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13732,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/073337ca9876f9acec8262b5.xlsx"},{"id":107634347,"identity":"2a0d83d1-9507-4a55-9582-01f7cb9654a8","added_by":"auto","created_at":"2026-04-23 12:21:52","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18884,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9303964/v1/f809ad75a512745415f7b80c.docx"}],"financialInterests":"","formattedTitle":"Identification of novel NK cell‑associated targets in severe asthma using integrated bioinformatics analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAsthma is a chronic inflammatory airway disease characterized by reversible airflow limitation and airway hyperresponsiveness. Despite the availability of standard and well‑established treatment regimens, there were 3.6%\u0026ndash;10% of asthma patients worldwide develop to severe asthma (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). According to the 2025 Global Initiative for Asthma Severe Asthma Guide, severe asthma was defined as patients who failed to achieve disease control after strictly managing contributory factors and regular combined inhalation of high-dose glucocorticoids and long-acting β\u003csub\u003e2\u003c/sub\u003e-agonist, excluding treatment compliance and inhaler techniques, or who developed uncontrolled asthma after de-escalation of treatment (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Severe asthma imposes substantial burdens on patients\u0026rsquo; quality of life and on global public health. Therefore, it is of great significance to explore the pathophysiological process, immune mechanism and new biomarkers of severe asthma to improve the prognosis of these patients.\u003c/p\u003e \u003cp\u003eNK cells are innate lymphocytes with cytotoxicity (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Based on the expression of CD56 and CD16, human NK cells can be subdivided into two distinct functional populations. CD56\u003csup\u003ebright\u003c/sup\u003eCD16\u003csup\u003e\u0026minus;\u003c/sup\u003e NK cells, which are mainly distributed in secondary lymphoid organs and exert regulatory functions on immune responses by secreting cytokines; CD56\u003csup\u003edim\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003e NK cells, which have cytotoxic functions and are mainly located in peripheral blood, can mediate antibody-dependent cellular cytotoxicity and play a major role in controlling acute inflammation (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Multiple studies have shown that compared with healthy controls, patients with severe asthma exhibit increased numbers of pro-inflammatory granulocytes and CD4⁺ T cells in bronchoalveolar lavage fluid (BALF), whereas the total number of NK cells is significantly reduced. Although the proportion of CD56\u003csup\u003edim\u003c/sup\u003e NK cell subsets is relatively increased in severe asthma, their actual cytotoxic capacity is markedly impaired. For instance, the ability to induce the apoptosis of eosinophils has decreased significantly. Moreover, it has been confirmed that glucocorticoids can suppress NK cell, further weakening the capacity of NK cells to eliminate activated leukocytes and pathogens in severe asthma (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The quantity and functional status of NK cells are closely associated to the severity of asthma. The proportion of activated (CD69+) NK cells in circulation is significantly negatively correlated with the ratio of forced expiratory volume/forced vital capacity (FEV\u003csub\u003e1\u003c/sub\u003e/FVC) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, some studies have pointed out that there was no significant difference in the total number of NK cells among healthy control, mild to moderate asthma, and severe asthma. But the ratio of key cytotoxic NK cell subset, CD56\u003csup\u003edim/neg\u003c/sup\u003eCD16\u003csup\u003ebr\u003c/sup\u003e, was significantly decreased in severe asthma (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Another study utilized the CIBERSORT algorithm to assess the infiltration of immune cells in BALF and found that CD8 T cells, resting and activated memory CD4 T cells, activated NK cells, and activated mast cells were significantly enriched in severe asthma (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, the specific mechanism of NK cells in severe asthma has not been fully elucidated. There is still a large gap in the search for NK cell-related biomarkers and potential therapeutic targets in severe asthma. We employed integrated bioinformatics approaches to identify key genes linked to NK cells and potential therapeutic agents in severe asthma. Single-cell transcriptome analysis was used to investigate the expression characteristics of key genes under different disease states. Additionally, we constructed NK cell‑related transcription factor (TF)-mRNA and miRNA-mRNA regulatory networks in severe asthma. These findings aim to provide new theoretical insights into the immune mechanisms of severe asthma and to facilitate the identification of novel therapeutic targets.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Acquisition\u003c/h2\u003e \u003cp\u003eIn this study, we obtained GSE137268, GSE76262, GSE158752, GSE43696, GSE67940, and GSE172495 datasets from the Gene Expression Omnibus (GEO) of NCBI. Among them, GSE137268 and GSE76262 were used as the training set, comprising induced sputum samples from 36 healthy controls and 105 patients with severe asthma. GSE158752, GSE43696, and GSE67940 served as the validation set, consisting of bronchial epithelial cell samples. GSE172495 was used for single-cell transcriptome analysis of peripheral blood mononuclear cells (PBMCs). Detailed sample information for each dataset is provided in Supplementary Table\u0026nbsp;1. We also obtained 329 NK cell-associated genes from previous studies (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Processing and the Identification of NK cell-Related DEGs\u003c/h2\u003e \u003cp\u003eGSE137268 was normalized using the normalizeBetweenArrays function in the limma package. GSE76262 was normalized using the rma function in the oligo package. The two datasets were then merged and the batch effect was corrected using the ComBat function in the sva package. Principal component analysis (PCA) was performed utilizing the FactoMineR package, and boxplots of the combined data sets were drawn to visualize sample clustering after batch correction and confirm the effectiveness of batch effect removal.\u003c/p\u003e \u003cp\u003eThe Gene Set Variation Analysis (GSVA) was used to compare the enrichment scores of the NK cell-related gene sets between the control and severe asthma groups with 329 NK cell-associated genes as a reference.\u003c/p\u003e \u003cp\u003eDifferentially expressed genes (DEGs) between severe asthma and healthy control were identified utilizing the limma package. The criteria for DEGs were as follows: |log2(Fold Change) | \u0026gt; 0.5 and adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The ggplot2 package was used to generate the volcano plots and the pheatmap package was used to generate the heatmaps to visualize the distribution of DEGs.\u003c/p\u003e \u003cp\u003eTo identify NK cell-related differentially expressed genes (NKRDEGs), we intersected DEGs and 329 NK cell-associated genes. Venn diagram was created using the ggvenn package to illustrate the overlap, and heatmap was used to visualize the expression patterns of 24 NKRDEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eWe ranked all genes according to the degree of differential expression. We downloaded the gene set \u0026ldquo;c5.go.bp.v2026.1.Hs.symbols.gmt\u0026rdquo; from the Molecular Signatures Database (MSigDB). The Gene Set Enrichment Analysis (GSEA) was performed using the clusterProfiler package to evaluate the distribution of gene sets in predefined gene lists and for visualization. In our analysis, we specified a sample permutation number of 1000 and set the threshold for significance at an adjusted P value of less than 0.05.\u003c/p\u003e \u003cp\u003eWith clusterProfiler package, we converted NKRDEGs in Gene Symbol format into Entrez gene IDs, and then performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The results were visualized by ggplot2 package and pathview package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Weighted Gene Co-expression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eWe performed WGCNA analysis on the expression matrix to identify gene modules associated with severe asthma. Only genes with the top 5000 median absolute deviation (MAD) were retained. Then, we conducted hierarchical clustering of the samples to exclude outliers. The soft thresholding power (β) was selected based on a fit index\u0026thinsp;\u0026gt;\u0026thinsp;0.85. Subsequently, we computed the adjacency matrix and transformed it into the Topological Overlap Matrix (TOM) to enhance the robustness of the network. The dynamic tree cutting was applied to cluster genes into modules, and highly similar modules were merged. Eight modules related to severe asthma phenotypes were identified, and the association between gene modular and phenotypes trait was visualized by heatmap and scatter plots. The final WGCNA genes were derived from the magenta module, which exhibited the most significant positive correlation, and the blue module, which exhibited the most significant negative.\u003c/p\u003e \u003cp\u003eThe overlapping genes were determined by the intersection of the NKRDEGs and WGCNA-derived genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Identification of Significant Genes and Validation\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was performed on the 12 overlapping genes with corrplot package. And we uploaded them to the Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/\u003c/span\u003e\u003cspan address=\"https://metascape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to acquire enrichment analysis results.\u003c/p\u003e \u003cp\u003eThe 12 overlapping genes were submitted to the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to construct a protein-protein interaction (PPI) network, with a minimum required interaction score of 0.4. The PPI network, which comprised 6 nodes and 8 edges, was visualized using Cytoscape software (version v3.10.4).\u003c/p\u003e \u003cp\u003eLeast Absolute Shrinkage and Selection Operator (LASSO) regression is a widely used method for variable selection in high-dimensional data. The overlapping genes were subjected to LASSO regression analysis using the glmnet package, with the penalty parameter α set to 1. The optimal λ value was determined through 10‑fold cross‑validation, leading to the identification of 4 key genes as potential biomarkers associated with severe asthma.\u003c/p\u003e \u003cp\u003eThe final candidate genes were determined by integrating 6 hub genes from the PPI network and 4 key genes identified via LASSO regression. The expression levels of significant genes were then visualized using boxplots to compare the differences between the severe asthma and control groups in both the training cohorts and the validation cohorts (GSE158752, GSE43696, and GSE67940).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Construction of TF-mRNA and miRNA-mRNA Regulatory Networks\u003c/h2\u003e \u003cp\u003eTo construct the regulatory network of TFs targeting the 24 NKRDEGs, the gene list was submitted to the NetworkAnalyst platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The TF-mRNA interactions were predicted and generated from the ENCODE database. Concurrently, the miRWalk (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ENCORI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases were used to predict miRNAs targeting the 24 NKRDEGs. And only those consistently recorded in both databases were retained. Ultimately, the TF-mRNA and miRNA‑mRNA regulatory networks were refined for visualization using Cytoscape software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Drug Prediction and Molecular Docking\u003c/h2\u003e \u003cp\u003eTo identify potential small-molecule compounds targeting NKRDEGs for the treatment of severe asthma, IL18R1 was submitted to the Enrichr platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Candidate therapeutic compounds were predicted based on Drug Signatures database (DSigDB). Compounds with P.value\u0026thinsp;\u0026le;\u0026thinsp;0.05 were selected and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe three-dimensional (3D) structure of the candidate compound tolnaftate was retrieved from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in SDF format. The protein structure of IL18R1 was obtained from the PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in PDB format. Molecular docking between tolnaftate and IL18R1 was performed on the CB-Dock2 platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cadd.labshare.cn/cb-dock2/\u003c/span\u003e\u003cspan address=\"http://cadd.labshare.cn/cb-dock2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), yielding a binding energy of \u0026minus;\u0026thinsp;7.7 kcal/mol. The Vina score below \u0026minus;\u0026thinsp;5.0 kcal/mol indicates favorable interaction potential between protein targets and candidate compounds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Single-Cell Sequencing Analysis\u003c/h2\u003e \u003cp\u003eSingle-cell RNA sequencing data of PBMCs samples from the GSE172495 dataset were downloaded and processed using the Seurat package for quality control and data preprocessing. Cell quality was assessed based on the percentage of mitochondrial genes, and cell cycle effects were evaluated using CellCycleScoring, revealing no significant bias in the dataset. Cells meeting the criteria of 500\u0026thinsp;\u0026le;\u0026thinsp;nCount_RNA\u0026thinsp;\u0026le;\u0026thinsp;4000 and mitochondrial gene percentage\u0026thinsp;\u0026le;\u0026thinsp;5% were retained for downstream analysis (Supplementary Figs.\u0026nbsp;4A, B). PCA was performed on the top 2,000 highly variable genes. An elbow plot was used to determine the significant principal components (Supplementary Fig.\u0026nbsp;4C). Subsequently, uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE) were applied for dimensional reduction and clustering. Automated cell type annotation was initially performed using SingleR with the HumanPrimaryCellAtlas reference database. Subsequently, manual annotation was conducted with known genetic markers to improve classification accuracy, leading to the identification of major cell populations, including NK cells. NK cell subsets were then subjected to reclustering analysis to further explore their heterogeneity. Finally, the expression patterns of significant genes were examined across different experimental conditions (HC_Baseline, HC_polyIC, SA_Baseline, and SA_polyIC) as well as between disease states (HC vs. SA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using R (version 4.5.1). Wilcoxon rank-sum test was applied to evaluate differences between two groups. Correlations between variables were evaluated with Spearman's rank correlation test. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant throughout the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Screening and Functional Enrichment Analysis of NKRDEGs\u003c/h2\u003e \u003cp\u003eWe successfully integrated the GSE137268 and GSE76262 datasets (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B; Supplementary Figs.\u0026nbsp;1A, B) and extracted the expression data from the healthy control and severe asthma groups for analysis. Using 329 NK cell-associated genes as the background gene set, we calculated a NK cell Score for each sample to reflect the relative enrichment of NK cell-associated genes expression patterns. GSVA results showed that the NK cell Score was been proven higher in the severe asthma group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Differential expression analysis between the severe asthma and healthy control groups identified 766 DEGs, comprising 284 upregulated and 482 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD; Supplementary Fig.\u0026nbsp;1C). The Venn diagram analysis revealed that 24 of these DEGs were NK cell\u0026ndash;associated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), most of which were upregulated in the severe asthma (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGSEA on the whole transcriptome indicated significant enrichment of NK cell\u0026ndash;related pathways in the severe asthma, including natural killer cell activation, natural killer cell mediated immunity, and natural killer cell activation involved in immune response (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). GO analysis of the 24 NKRDEGs further demonstrated significant enrichment in biological processes such as lymphocyte mediated immunity, cell killing, and natural killer cell mediated cytotoxicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Cellular components only enriched in the plasma membrane signaling receptor complex and the immunological synapse. Enriched molecular function included cytokine activity, cytokine receptor binding, and chemokine activity. KEGG pathway analysis revealed that these NKRDEGs were primarily enriched in pathways such as viral protein interaction with cytokine and cytokine receptor, TNF signaling pathway, and natural killer cell mediated cytotoxicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; Supplementary Fig.\u0026nbsp;1D). These findings confirm that NK cells actively participate in the pathophysiological process of severe asthma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Identification of Gene Modules by WGCNA\u003c/h2\u003e \u003cp\u003eWGCNA was performed using the grouping information as the clinical trait. Sample clustering was conducted to remove outliers, with a height cutoff of 75 (Supplementary Figs.\u0026nbsp;2A, B). By evaluating the scale‑free topology model fit and mean connectivity, the optimal soft‑thresholding power was determined to be β\u0026thinsp;=\u0026thinsp;8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; Supplementary Fig.\u0026nbsp;2C). A minimum module size of 30 genes and a dissimilarity threshold of 20% for merging similar modules were set, and the dynamic tree‑cut algorithm was applied, yielding a total of eight modules associated with severe asthma (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C; Supplementary Figs.\u0026nbsp;2D\u0026ndash;F, Table\u0026nbsp;3). The module-trait relationship analysis revealed that the magenta module exhibited the strongest positive correlation with severe asthma, whereas the blue module showed the strongest negative correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Therefore, these two modules were identified as key modules, and their constituent genes were considered WGCNA‑derived genes. Scatter plots illustrated the correlation between gene significance and module membership (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of Significant Genes and Validation\u003c/h2\u003e \u003cp\u003eThe intersection of WGCNA‑derived genes and NK cell\u0026ndash;associated genes yielded 53 common genes. These 53 genes were further intersected with NKRDEGs, generated 12 overlapping genes (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Correlation analysis showed that these 12 genes were significantly correlated with each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Functional enrichment analysis revealed that these genes were primarily enriched in GO terms such as natural killer cell activation, regulation of T cell mediated immunity, and cell population proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA PPI network of overlapping genes was constructed based on the STRING database, which consisted of 6 nodes and 8 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE; Supplementary Fig.\u0026nbsp;3A). With lambda.1se as the optimal λ value, 4 diagnostic core genes: CSF1, IL18R1, ITGAL, and HDC, were screened out via LASSO regression model (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, G). By integrating the results from the PPI and LASSO regression model, CSF1, IL18R1, and ITGAL were ultimately identified as 3 significant genes related with severe asthma (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eThree datasets (GSE158752, GSE43696, and GSE67940) were used to validate the expression levels of the 3 significant genes. The results showed that only IL18R1 exhibited a statistically significant difference between the severe asthma and healthy control groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI, J; Supplementary Figs.\u0026nbsp;3B\u0026ndash;E), suggesting that upregulation of IL18R1 may represent a unique NK cell‑related biomarker for severe asthma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 TF-mRNA and miRNA-mRNA Regulatory Network in Severe Asthma\u003c/h2\u003e \u003cp\u003eIn order to elucidate the potential regulatory mechanisms of 24 NKRDEGs, we constructed TF-mRNA and miRNA-mRNA regulatory networks respectively. The TF\u0026ndash;mRNA regulatory network comprised 182TFs, 19 target genes, and 335 interaction edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Among these, NR2F6 was identified as the most prominent TF, regulating 6 NKRDEGs. In addition, MTA2, TFDP1, KLF16, ZNF589, ZBTB11, and MBD1 also played important roles, each regulating 5 NKRDEGs. As for the miRNA-mRNA regulatory network, there were 80 miRNA-mRNA interaction pairs, involving 10 target genes and 72 miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These findings provide important insights into the transcriptional and post‑transcriptional regulatory mechanisms of NK cell‑associated genes in severe asthma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Screening Potential Therapeutic Drugs\u003c/h2\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\u003eDrug prediction of IL18R1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP.value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined.Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efluoride CTD 00005982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002749954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117599.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcimycin CTD 00005287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004949933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105642.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGN-PC-0JHFVD BOSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009549901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92136.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etolnaftate PC3 UP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.013299882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85250.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICKEL CHLORIDE CTD 00001064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019949854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76728.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhorbol 12-myristate 13-acetate CTD 00006852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024149840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72671.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARSENIC CTD 00005442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.042649798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60403.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBortezomib CTD 00003736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.043899795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59772.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilica CTD 00006678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.044899794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59279.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the DSigDB database from the Enrichr website, we searched for potentially effective drugs targeting IL18R1. A total of 9 candidate compounds with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), among which Tolnaftate and Bortezomib are clinically approved drugs. Molecular docking results revealed that Tolnaftate exhibited strong binding affinity to the IL18R1 protein, with a binding energy of \u0026minus;\u0026thinsp;7.7 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), suggesting that this agent may exert a potential therapeutic role in severe asthma by targeting IL18R1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Single-Cell Sequencing Analysis Revealed NK cells Infiltration in Severe Asthma\u003c/h2\u003e \u003cp\u003eTo validate the expression pattern of IL18R1 at single‑cell RNA, we analyzed PBMCs samples from the GSE172495 dataset. Integrating automated and manual annotation, five major immune cell populations were identified: CD4⁺ T cells, CD8⁺ T cells, B cells, NK cells, and monocytes. UMAP clearly visualized the distribution of these cell populations, and dot plots of cell markers confirmed the accuracy of cell type annotation (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C; Supplementary Figs.\u0026nbsp;4D-G). Quantitative analysis of cellular composition under different disease and stimulation status revealed that, both at baseline and after polyIC stimulated, the absolute number and relative proportion of NK cells were significantly higher in the severe asthma compared to the healthy control. Moreover, polyIC stimulation further increased the abundance of NK cells in both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Further analyzing the distribution of IL18R1 expression in each cell type, we found that it was expressed at the highest level in NK cells, which was significantly higher than other immune cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, F). Subgroup analysis of the NK cell population revealed more pronounced IL18R1 expression in NK cells from the severe asthma group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). These findings suggest that NK cells may play an important role in the peripheral immune response of patients with severe asthma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we systematically explored the potential role of NK cells in the pathogenesis of severe asthma by integrating multiple bioinformatics approaches, including differential expression analysis, WGCNA, and LASSO regression. After multiple screening and validation with external datasets, IL18R1 was identified as a key biomarker. Functional enrichment analysis, GSEA analysis and GSVA analysis consistently revealed activation of NK cell-related pathways in the airways of severe asthma, ranging from partial differential genes to the whole gene set level. Subsequently, at the cellular level, single-cell RNA sequencing confirmed that NK cells were significantly activated in peripheral blood of severe asthma, and the expression of IL18R1 was significantly upregulated. Finally, based on online databases, we predicted the key transcription factor NR2F6, as well as two compounds with potential therapeutic value, Tolnaftate and Bortezomib, providing new clues for targeted intervention of NK cells in severe asthma.\u003c/p\u003e \u003cp\u003ePrevious studies mostly focused on the changes of NK cells and their subsets in BALF of patients with severe asthma before and after treatment (\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), whereas investigations exploring the molecular markers linking NK cells to severe asthma remain limited. Furthermore, existing bioinformatics studies on severe asthma have predominantly concentrated on eosinophils and neutrophils (\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), which are relatively well‑established immune cells in asthma research. We focused on NK cell-related molecules and identified IL18R1 as a key biomarker in severe asthma. The protein encoded by IL18R1 belongs to the interleukin-1 receptor family of cytokine receptors and can specifically bind to IL-18. The IL-18/IL18R1 signaling axis plays a complicated role in immune regulation. On one hand, in synergy with IL‑12, IL‑18 induces IFN‑γ production by NK cells and Th1 cells, thereby promoting Th1‑type immune responses (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). On the other hand, IL‑18 also facilitates the secretion of Th2‑type cytokines (such as IL‑4, IL‑5, and IL‑13), enhances IgE synthesis, and increases airway hyperresponsiveness, thus participating in the immune response and inflammatory processes of T2‑type asthma (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Under physiological conditions, NK cells express IL18R1, and IL-18 can cooperate with IL-15 to promote NK cells proliferation, contribute to their functional maturation, and enhance perforin-mediated cytotoxicity (\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Despite the above immune-enhancing effects of IL-18, sustained IL-18 stimulation may lead to NK cells dysfunction and promote the reduction of NK cells by inducing their apoptosis or \"self-attack\", which subsequently amplifies type 2 inflammatory responses (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Consistent with this, our immune infiltration analysis of induced sputum samples from severe asthma revealed no significant increase in the proportion of NK cells, but rather a decrease in the percentage of activated NK cells (Supplementary Figs.\u0026nbsp;5A\u0026ndash;C). These findings are in agreement with previous reports by Melody G. Duvall and Laura Bergantini et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), jointly supporting the above notion. Accordingly, we hypothesize that the key factor in severe asthma is not simply a change in NK cell numbers, but rather the dysregulation of the IL‑18/IL18R1 signaling axis, leading to abnormal functions of NK cells and immune imbalance.\u003c/p\u003e \u003cp\u003eNR2F6 was identified as a key upstream transcription factor in our TF-mRNA regulatory network. Although the network did not show that NR2F6 directly regulated IL18R1 expression, it closely interacted with IL18 and IL18RAP. Johannes Woelk et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) found that NR2F6-deficient mice had insufficient IL-15 due to reduced numbers of cDC1 and macrophages in the spleen, which in turn caused impaired peripheral maturation of NK cells. At the same time, NR2F6 can directly inhibit the expression of the activating receptor NKp46, which is a key molecule for NK cell-mediated cytotoxicity. With exogenous IL-15 supplementation, NR2F6-deficient NK cells not only matured normally, but also exhibited enhanced effector function owing to the release of NKp46 inhibition. These findings inspire us that simultaneously regulating NR2F6 and IL-15 is expected to become a potential supplementary strategy for regulating NK cell function in patients with severe asthma. However, whether NR2F6 indirectly regulates the expression of IL18R1, and whether specific interactions occur among NR2F6, IL18R1, IL18, and IL18RAP in NK cells from patients with severe asthma remain to be further experimentally verified.\u003c/p\u003e \u003cp\u003eAlthough bortezomib was predicted as a potential compound, previous animal studies have demonstrated limited efficacy in allergic bronchial asthma (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Therefore, we focused on the classical antifungal drug tolnaftate. Currently, no literature has reported a functional association between tolnaftate and IL18R1, NK cells, or asthma. However, the prediction from DSigDB suggested a potential interaction between tolnaftate and IL18R1, and the molecular docking result also revealed they have a high binding affinity. These findings only provide a preliminary screening basis, and further biological experiments are required for verification.\u003c/p\u003e \u003cp\u003eThere are several limitations in our study. First, we did not conduct cellular experiments or clinical trials to validate the results. Second, whether changes in NK cells in peripheral blood can accurately reflect alterations in airway remains to be further investigated. Finally, the drug prediction results based on IL18R1 are unsatisfactory. It may be necessary to supplement additional analyses targeting the IL-18/IL18R1 signaling axis and NR2F6 to expand the scope of potential candidates.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we identified IL18R1 as a key NK cell‑related biomarker and confirmed significant activation of NK cell‑associated pathways in the airways of patients with severe asthma. Furthermore, we explored the potential regulatory roles of the IL‑18/IL18R1 signaling axis and the transcription factor NR2F6 in NK cell function in severe asthma. Candidate compounds with potential therapeutic value were also identified, providing clues for subsequent experimental validation and intervention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNK cell, Natural killer cells\u003cbr\u003e\u0026nbsp;LASSO, Least absolute shrinkage and selection operator\u003cbr\u003e\u0026nbsp;DSigDB, Drug SIGnatures DataBase\u003cbr\u003e\u0026nbsp;GO, Gene ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG, Kyoto encyclopedia of genes and genomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSVA, Gene set variation analysis\u003cbr\u003e\u0026nbsp;miRNA, microRNA\u003cbr\u003e\u0026nbsp;TF, Transcription factor\u003cbr\u003e\u0026nbsp;BALF, Bronchoalveolar lavage fluid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC, Forced expiratory volume/forced vital capacity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePBMCs, Peripheral blood mononuclear cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA, Principal component analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEGs, Differentially expressed genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNKRDEGs, NK cell-related differentially expressed genes\u003c/p\u003e\n\u003cp\u003eMSigDB, Molecular Signatures Database\u003c/p\u003e\n\u003cp\u003eGSEA, Gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eWGCNA, Weighted gene co-expression network analysis\u003c/p\u003e\n\u003cp\u003eMAD, median absolute deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTOM, Topological overlap matrix\u003c/p\u003e\n\u003cp\u003ePPI, Protein-protein interaction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUMAP, Uniform manifold approximation and projection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003et-SNE, t-Distributed stochastic neighbor embedding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHC, Healthy control\u003c/p\u003e\n\u003cp\u003eSA, Severe asthma\u003c/p\u003e\n\u003cp\u003eIL-18, Interleukin-18\u003c/p\u003e\n\u003cp\u003eIL-12, Interleukin-12\u003c/p\u003e\n\u003cp\u003eIFN‑\u0026gamma;, Interferon-\u0026gamma;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIL‑4, Interleukin-4\u003c/p\u003e\n\u003cp\u003eIL‑5, Interleukin-5\u003c/p\u003e\n\u003cp\u003eIL‑13, Interleukin-13\u003c/p\u003e\n\u003cp\u003eIL-15, Interleukin-15\u003c/p\u003e\n\u003cp\u003eIgE, Immunoglobulin E\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJYX designed and performed data curation, formal analysis, and visualization, and was a major contributor in writing the manuscript. SCW contributed to conceptualization and validation. YLZ supervised the study, and reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge contributors for openly shared data from the GEO database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKardas G, Kuna P, Panek M. Biological Therapies of Severe Asthma and Their Possible Effects on Airway Remodeling. Front Immunol. 2020;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoh MS, Chan KKP, Fukunaga K, Kim S-H, Lan LTT, Omar A et al. Understanding disease burden, challenges in current treatment strategies and call for action for management of severe asthma in Asia: a position statement from Asian respiratory experts. 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TNFAIP3, PADI4, and CXCR4 as biomarkers of neutrophil extracellular traps in severe asthma. Int Immunopharmacol. 2026;170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshimoto T, Mizutani H, Tsutsui H, Noben-Trauth N, Yamanaka K, Tanaka M, et al. IL-18 induction of IgE: dependence on CD4\u0026thinsp;+\u0026thinsp;T cells, IL-4 and STAT6. Nat Immunol. 2000;1(2):132\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakanishi K, Yoshimoto T, Tsutsui H, Okamura H. Interleukin-18 regulates both Th1 and Th2 responses. Annu Rev Immunol. 2001;19:423\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu G, Whyte MKB, Vestbo J, Carlsen K, Carlsen K-H, Lenney W, et al. Interleukin 18 receptor 1 gene polymorphisms are associated with asthma. Eur J Hum Genet. 2008;16(9):1083\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin K, Wang T, Tang Q, Chen T, Lin M, Jin J, et al. 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A critical role for IL-18 in the proliferation and activation of NK1.1\u0026thinsp;+\u0026thinsp;CD3- cells. Journal of immunology (Baltimore, Md: 1950). 1998;160(10):4738-46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHyodo Y, Matsui K, Hayashi N, Tsutsui H, Kashiwamura S, Yamauchi H et al. IL-18 up-regulates perforin-mediated NK activity without increasing perforin messenger RNA expression by binding to constitutively expressed IL-18 receptor. Journal of immunology (Baltimore, Md: 1950). 1999;162(3):1662-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026auml;hler L, Sch\u0026auml;rli S, Luther F, Bertschi NL, Skabytska Y, Roediger B, et al. IL-18 in atopic dermatitis\u0026mdash;a multifaceted driver of skin inflammation. J Allergy Clin Immunol. 2025;156(5):1160\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoelk J, Hornsteiner F, Aschauer-Wallner S, Stoitzner P, Baier G, Hermann-Kleiter N. Regulation of NK cell development, maturation, and antitumor responses by the nuclear receptor NR2F6. Cell Death Dis. 2025;16(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWegmann M, Lunding L, Orinska Z, Wong DM, Manz RA, Fehrenbach H. Long-Term Bortezomib Treatment Reduces Allergen-Specific IgE but Fails to Ameliorate Chronic Asthma in Mice. Int Arch Allergy Immunol. 2012;158(1):43\u0026ndash;53.\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":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Severe asthma, Natural killer cells, IL18R1, NR2F6, Bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-9303964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9303964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough there are standard treatments, severe asthma still imposes a substantial burden on global health. Natural killer cells (NK cell) are closely related to the severity of asthma, but the precise mechanisms of their action haven't been fully elucidated. This study aims to identify NK cell‑related hub genes in severe asthma, providing a theoretical basis for understanding the immune mechanisms and finding potential therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe integrated differential expression analysis, WGCNA, LASSO regression and other methods to identify NK cell-related genes. Multiple functional enrichment analysis and single-cell sequencing were used to explore the functional status of NK cells in severe asthma. Additionally, we constructed regulatory networks and predicted potential therapeutic compounds through the DSigDB database and molecular docking.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified IL18R1 as a key NK cell‑related biomarker in severe asthma. Enrichment analyses, including GO, KEGG, and GSVA, consistently demonstrated that NK cell-related pathways were significantly activated in the airways of patients with severe asthma. Single‑cell transcriptomic analysis further confirmed that NK cells were enriched in the peripheral blood in severe asthma, accompanied by upregulated IL18R1 expression. Regulatory network analysis identified NR2F6 as an important transcription factor modulating NK cell function. The miRNA-mRNA regulatory network revealed a complex post-transcriptional regulatory mechanism. In addition, two potential therapeutic compounds, Tolnaftate and Bortezomib, were screened through drug prediction, providing new insights for targeting NK cells to intervene in severe asthma.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study provides new theoretical insights into NK cell‑related targets and upstream regulators in severe asthma, and enhances the understanding of the underlying immune mechanisms. However, these findings warrant further experimental investigation.\u003c/p\u003e","manuscriptTitle":"Identification of novel NK cell‑associated targets in severe asthma using integrated bioinformatics analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 12:21:46","doi":"10.21203/rs.3.rs-9303964/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-22T17:42:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T07:11:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-04T04:17:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2026-04-02T09:50:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"660da258-962f-4b12-9bfd-807eef5115e2","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T12:21:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 12:21:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9303964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9303964","identity":"rs-9303964","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}