Investigation of differential expression in phospholipid metabolism-related genes in bronchial epithelial cells of asthma patients 

Investigation of differential expression in phospholipid metabolism-related genes in bronchial epithelial cells of asthma patients | 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 Investigation of differential expression in phospholipid metabolism-related genes in bronchial epithelial cells of asthma patients Zemin Li, Xiao Huo, Huan Liu, Liting Cao, Ying Shang, yingying Ge, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8669205/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Background Asthma is a chronic respiratory disease that substantially compromises quality of life. While phospholipid metabolism is implicated in asthma pathophysiology, its specific role remains unclear. This study investigated metabolic alterations related to phospholipid metabolism in bronchial epithelial cells (BECs) of asthma patients compared to healthy individuals, aiming to identify potential biomarkers. Methods We analyzed gene expression datasets from the GEO database to identify phospholipid metabolism-associated differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) identified asthma-associated modules. A logistic regression predictive model was constructed and validated using an independent dataset. Validation of key differential genes was performed using established in vivo and in vitro approaches. Results Six phospholipid metabolism-related candidate genes exhibited differential expression in asthmatic epithelium. The predictive model demonstrated robust diagnostic performance in BEC samples, with AUC values of 0.76 and 0.83 in the training and validation sets, respectively. In vivo and in vitro validation revealed specific downregulation trends for PCTP and HADHB, and specific upregulation trends for MFSD2A, in asthmatic BECs. This implicates their potential contribution to pathogenesis and utility as diagnostic biomarkers. Conclusion This study reveals that dysregulation of phospholipid metabolism-related genes (particularly PCTP, HADHB, and MFSD2A) in bronchial epithelial cells is a key characteristic of asthma. These alterations not only contribute to the onset and progression of the disease, but the gene signature model derived from them also demonstrates significant potential as novel diagnostic biomarkers. This provides new avenues for understanding the pathological mechanisms of asthma and developing diagnostic tools. Asthma Phospholipid Metabolism Bronchial Epithelial Cells Figures Figure 1 Figure 2 Figure 3 Introduction Bronchial asthma (commonly termed asthma) is a prevalent chronic disease that imposes a substantial global health burden. Epidemiological studies indicate that the rate of poorly controlled asthma among adults is 15.1% in high- and middle-income countries but rises to 32.1% in low- and lower-middle-income countries [ 1 ]. In China, asthma prevalence among individuals aged ≥ 20 years reaches 4.5%, affecting over 47 million people [ 2 ]. Asthma pathogenesis is closely linked to bronchial epithelial cell dysfunction. These cells maintain respiratory barrier integrity and permeability by regulating intercellular connections, adhesion molecule expression, and tight junction formation. As the first line of defense against allergens and inflammatory mediators, they prevent infiltration into deeper tissues while participating in immune regulation and inflammatory responses [ 3 ]. When activated by allergens such as house dust mite (HDM), bronchial epithelial cells secrete cytokines—including thymic stromal lymphopoietin (TSLP) and granulocyte-macrophage colony-stimulating factor (GM-CSF)—that activate dendritic cells, subsequently driving Th2 cell differentiation and immune responses [ 4 – 6 ]. Historically, asthma research focused predominantly on immune mechanisms. However, recent studies highlight the emerging role of metabolic dysregulation in asthma pathogenesis [ 7 ]. Lipids, in particular, are increasingly recognized as potential biomarkers and therapeutic targets [ 8 , 9 ]. For instance, differences in lipid metabolism have been observed in the induced sputum of asthma patients versus healthy individuals [ 10 ]. Lipid metabolites such as phosphatidic acid (PA) and phosphatidylglycerol (PG) may serve as diagnostic biomarkers [ 11 ], and distinct plasma lipid metabolite profiles have been identified in asthma patients compared to healthy controls [ 12 ]. Phospholipids regulate bronchial epithelial cell functions and inflammatory responses through multiple mechanisms: Lysophosphatidic acid (LPA) modulates inflammation-related gene expression via G protein-coupled receptors, inhibits CCL5/RANTES secretion, affects IRF-1 activity, and induces IL-13Rα2 expression to regulate IL-13 signaling [ 13 ]. Lipid phosphate phosphatase-1 (LPP-1) regulates LPA-related signaling and IL-8 secretion [ 14 ], while LPA influences c-Met and E-cadherin localization and function [ 15 ]. Sphingosine-1-phosphate (S1P) promotes epithelial-mesenchymal transition and inflammatory factor expression [ 16 ]. Despite these advances, the mechanisms underlying these metabolic differences remain poorly understood. This study aims to elucidate metabolic differences in bronchial epithelial cells between asthma patients and healthy individuals, specifically focusing on phospholipid metabolism-related gene expression. Using publicly available gene expression datasets, we identified differentially expressed genes, developed predictive models to evaluate their diagnostic potential, and conducted experimental validation. By uncovering key genetic markers associated with asthma, our research provides novel insights to advance diagnostic tools, deepen understanding of asthma pathogenesis, and inform future therapeutic strategies. Materials and methods Data Preparation and Preprocessing Asthma-related gene expression profile datasets were obtained from the GEO database using the following inclusion criteria:Sample size exceeding 50, including both asthma patients and healthy controls;Availability of basic demographic information (e.g., age and gender) to account for potential confounding factors;Restriction to bronchial epithelial cell samples to ensure tissue specificity for phospholipid metabolism gene analysis.Based on these criteria, four datasets comprising 431 samples (321 asthma patients and 110 controls) were selected. Two additional datasets (GSE152004 and GSE69683) were included to validate model performance across diverse sample types. For GSE74986, data preprocessing involved cleaning and normalization using the Robust Multi-Array Average (RMA) method. Gene symbols were assigned based on the GPL570 annotation, averaging values for multiple probes corresponding to the same gene. Candidate lipid metabolism-related genes were validated in GSE85567 through differential expression analysis with DESeq2 (P<0.05).↳ A gene co-expression network was constructed using the WGCNA R package, generating similarity and adjacency matrices, followed by hierarchical clustering to identify modules using the dynamic tree cut method based on average linkage hierarchical clustering, with the minimum module size set to 10 genes. Asthma-related modules were selected based on significant correlations (|r| ≥ 0.5, P < 0.05) with Module Eigengenes (MEs). The correlation analysis between MEs and the asthma (case) and control groups was performed using the Pearson correlation method. Predictive models were built using logistic regression on independent datasets (GSE67472 and GSE63142) with validated differentially expressed genes. Model performance was evaluated using ROC analysis to compute the area under the curve (AUC), with comparisons conducted using the DeLong test using the pROC package. House dust mite model Six-to-eight-week-old female C57BL/6J mice were obtained from the Department of Laboratory Animal Science, Peking University Health Science Center. Mice were briefly anesthetized with isoflurane for intranasal procedures. For sensitization, mice received 1 μg of house dust mite (HDM) extract in 40 μL of phosphate-buffered saline (PBS) via intranasal administration. Seven days after sensitization, mice were treated with intranasal administration of 10 μg HDM in 40 μL PBS for five consecutive days. Four days after the final treatment, mice were euthanized by cervical dislocation while under deep anesthesia with isoflurane, in accordance with the institutional guidelines for animal welfare and ethics. Lungs were then collected and dissected for subsequent analysis. Immunohistochemistry Mouse lung tissues were fixed in 4% paraformaldehyde for 24 h at 4°C, embedded in paraffin, and sectioned into 5-μm slices. After deparaffinization in xylene and rehydration through a graded ethanol series, antigen retrieval was performed in citrate buffer (pH 6.0) at 95°C for 30 min. Endogenous peroxidase activity was quenched with 3% H2O2 for 15 min. Tissue sections were then incubated overnight at 4°C with primary antibodies against FIG4, HADHB, MFSD2A, and PCTP (Biodragon, Beijing, China; dilutions: 1:200–1:400). Following PBS washes, sections were incubated with biotinylated secondary antibody for 1 h at room temperature, developed using an ABC kit with DAB substrate, counterstained with hematoxylin, and finally dehydrated and mounted for microscopic analysis. Human Bronchial Epithelial Cell (hBEC) stimulation. hBECs (BEAS-2B) were divided into two groups: the HDM group, which was treated with 50 ug/ml HDM solution; the control group, which was treated with PBS alone. After 24 hours of stimulation, the cells were collected for subsequent experiments. Western Blot hBECs were collected and lysed with RIPA buffer containing protease inhibitors. Equal amounts of protein (10 µg) were separated by 10% SDS-PAGE and transferred to a PVDF membrane (Yamei, China) at 300mA for 1.5 hour. The membrane was incubated overnight at 4°C with primary antibodies(all obtained from Abcam, UK) against MFSD2A, HADHB, FIG4, and PCTP at the recommended dilution (1:1000-1:5000). Quantification of protein bands was performed using ImageJ software (NIH, USA). The expression levels of target proteins were normalized to β-actin (ProteinTech, China) as a loading control. Results Analysis of GEO datasets This study incorporated six Gene Expression Omnibus (GEO) datasets, including four bronchial epithelial cell datasets and two composite datasets containing matched nasal epithelial cells and peripheral blood mononuclear cells (PBMCs). All datasets were rigorously selected based on complete clinical annotations, standardized processing protocols, and demographic matching (age and sex) to ensure comparability. This integrated approach enabled a comprehensive examination of both respiratory and systemic immune profiles while controlling for key confounding variables(Table 1 ). Table 1 ID PMID Epithelia source Asthma Controls Total Mean Age range Male / Female Covariates used in analysis DEGs (Up/Down) GSE74986 26628680 Bronchial 74 12 86 36 (14–63) 46/40 age, sex 4886 (2473/2413) GSE85567 27942592 airway epithelial cells 57 28 85 39 (19–62) 49/36 age, sex 1886 (685/1201) GSE67472 25611785 Airway epithelial 62 43 105 35 (20–68) 51/54 GSE63142 25338189 Bronchial Epithelial Cells 128 27 155 36 (14–63) 36/64 GSE152004 35347136 nasal epithelium 441 254 695 14 (11–17) 331/364 GSE69683 27925796 Peripheral Blood 411 87 498 39 (27–62) 223/275 Total 1173 451 1624 Identification of Key Genes in Phospholipid Metabolism To identify genes involved in phospholipid metabolism, we extracted relevant gene sets from the KEGG ("Glycerophospholipid metabolism"; 77 genes) and REACTOME ("Phospholipid metabolism"; 211 genes) databases. After merging these datasets, we obtained 230 unique genes, including 58 overlapping genes. We performed weighted gene co-expression network analysis (WGCNA) on the 211 genes from the GSE74986 dataset. Following outlier removal and soft threshold determination (power = 5; Fig. 1 A), we identified six distinct modules using the blockwiseModules method (maximum module size = 10): blue (41 genes), brown (40 genes), green (18 genes), gray (25 genes), turquoise (61 genes), and yellow (20 genes) (Fig. 1 B). Notably, the turquoise (r = -0.77, p = 7e-12) and gray (r = 0.57, p = 6e-06) modules showed significant correlations with asthma (Fig. 1 C). The turquoise module genes were highly expressed in healthy samples, whereas gray module genes were upregulated in asthma samples, prompting their selection for further analysis . Prior to network construction, we filtered out genes with low correlation (cutoff < 0.2), excluding 57 genes(Table S1 ). Using Cytoscape, we ranked the remaining genes by degree and retained those with a degree ≥ 3, yielding 49 candidate genes. Figure 1 . WGCNA analysis and module identification for phospholipid metabolism genes in the GSE74986 dataset.(A) Scale-free fit index for various soft-thresholding powers. The power of β = 5 was selected as the soft-threshold to ensure a scale-free network.(B) Dendrogram of all genes clustered based on a dissimilarity measure (1-TOM). The identified modules are displayed in different colors.(C) Module-trait relationships. The turquoise module (r = -0.77, p = 7e-12) and the gray module (r = 0.57, p = 6e-06) showed significant correlation with asthma. The color legend indicates Pearson correlation coefficients.(D) Heatmap of the correlation between module eigengenes and the sample traits (health and asthma). According to the analysis, genes in the turquoise module are highly expressed in healthy samples, whereas genes in the gray module are highly expressed in asthma samples. Cross-Dataset Validation Reveals Key Dysregulated Genes in Asthma Pathogenesis Differential expression analysis of the GSE74986 dataset (P < 0.001) identified 4,886 significantly altered genes in asthma patients (2,473 upregulated; 2,413 downregulated), including 42 of the 49 candidate genes (85.7%) previously identified by WGCNA (Table S2). Validation in the independent GSE85567 dataset confirmed 1,886 DEGs (685 upregulated; 1,201 downregulated), with six genes showing consistent dysregulation across both cohorts (Table 2 ). Notably, while five genes exhibited stable downregulation (fold change 1), suggesting its distinct role in asthma pathophysiology. This robust two-stage analysis highlights the reliability of these phospholipid metabolism-associated gene signatures. Table 2 GSE74986 GSE85567 gName Fold change Ave (Asthma) P value Ave (Healthy) Fold change Ave (Asthma) P value Ave (Healthy) Figure 4 0.5472 19.4502 4.242E-06 35.5476 0.9323 2.5587 0.0290 2.7444 HADHB 0.5649 1.9833 4.512E-09 3.5111 0.9296 28.3186 0.0420 30.4643 MFSD2A 1.3864 1.7886 4.242E-06 1.2901 1.4099 41.6189 0.0003 29.5198 PCTP 0.7557 1.7428 1.680E-05 2.3060 0.8979 2.0604 0.0197 2.2946 PIK3CG 0.6019 2.8116 6.343E-05 4.6712 0.8089 0.7224 0.0105 0.8930 PTEN 0.5645 0.9694 4.843E-06 1.7174 0.9182 7.7794 0.0480 8.4728 Construction of the Predictive Model We developed a classification model based on six-gene expression profiles to discriminate asthma patients from healthy controls, validated across multiple independent datasets. Using bronchial epithelial cell samples, the model demonstrated robust performance with an AUC of 0.76 (95% CI: 0.64–0.83) in the GSE67472 cohort (62 asthma/43 healthy) and 0.83 (95% CI: 0.73–0.90) in the GSE63142 cohort (128 asthma/27 healthy). When applied to alternative sample types, the model maintained moderate diagnostic accuracy, achieving AUCs of 0.64 (95% CI: 0.59–0.67) in nasal epithelial cells (GSE152004; 441 asthma/254 healthy) and 0.72 (95% CI: 0.66–0.76) in peripheral blood cells (GSE69683; 411 asthma/87 healthy)(Fig. 2 ). Notably, the performance in bronchial epithelium significantly outperformed nasal samples (deLong test P < 0.05), suggesting tissue-specific predictive value. These results demonstrate the model's strongest diagnostic utility in bronchial samples while retaining cross-tissue applicability. Verification of six DEGS through In Vivo and In Vitro experiments Evidence indicates that there is a reduction of PTEN in asthmatic mice. PTEN inhibits PI3K activity by dephosphorylating the signaling lipid PIP3, thereby alleviating bronchial inflammation and airway hyperreactivity[ 17 ]. Additionally, mice with a defect in PIK3CG exhibit lower levels of airway hyperreactivity, airway inflammation, and airway remodeling in response to allergen stimulation[ 18 ]. Previous studies have confirmed the roles of these two genes in asthma and partially support the accuracy of the predicted genes in this study. Therefore, we focused on the verification of the remaining four genes: MFSD2A, PCTP, HADHB, and Fig. 4. Evidence indicates reduced PTEN levels in asthmatic mice. PTEN inhibits PI3K activity by dephosphorylating the signaling lipid PIP3, thereby mitigating bronchial inflammation and airway hyperreactivity [ 17 ]. Consistent with this, mice with defective PIK3CG exhibit attenuated allergen-induced airway hyperreactivity, inflammation, and remodeling [ 18 ]. These established roles of PTEN and PIK3CG in asthma partially validate our gene prediction approach. Consequently, we prioritized experimental verification of the remaining four candidate genes: MFSD2A, PCTP, HADHB, and Fig. 4. Compared to the control group, the lung tissue of HDM group exhibited pronounced inflammatory cell infiltration around the airways. Additionally, a marked rise in the number of mucus-secreting cells was observed in the airways of HDM-treated mice (Fig. 3 A). Immunohistochemistry (IHC) of lung tissue and cell-specific Western blot analysis demonstrated no significant difference in Fig. 4 expression within epithelial cells. In contrast, bronchial epithelial cells from asthmatic mice exhibited significantly decreased PCTP and HADHB expression, alongside a notable increasing trend in MFSD2A expression. These results indicate that PCTP, HADHB, and MFSD2A exhibit specific dysregulation within the bronchial epithelium in asthma compared to controls. Discussion Bronchial epithelial cells are critical guardians of respiratory barrier integrity, preventing allergen penetration while actively participating in asthma pathogenesis through the regulation of intercellular connections and secretion of Th2-polarizing cytokines. Growing evidence indicates that cellular metabolic processes—particularly lipid metabolism—serve as essential regulators of epithelial cell function and immune responses. Beyond their structural roles in cellular membranes, phospholipids dynamically modulate cell signaling and inflammatory processes. Recent studies report that disturbances in phospholipid metabolism, such as altered levels of lysophosphatidic acid (LPA), phosphatidic acid (PA), and sphingosine-1-phosphate (S1P), can influence bronchial epithelial cell behavior, cytokine secretion, and epithelial-mesenchymal transition—a process relevant to asthma pathogenesis. Although aberrant phospholipid metabolism has been observed in asthmatic airway epithelium, whether these alterations directly drive asthma development requires further investigation. Thus, elucidating the regulatory mechanisms of phospholipid metabolism in bronchial epithelial cells is crucial for advancing our understanding of asthma pathogenesis and identifying novel therapeutic targets. This study systematically investigated phospholipid metabolism-related genes in asthma using an integrated bioinformatics and experimental approach. Through weighted gene co-expression network analysis (WGCNA) of bronchial epithelial cells combined with differential expression profiling, we identified six candidate genes. Subsequent experimental validation using immunohistochemistry (IHC) and Western blotting confirmed three core regulators: PCTP and HADHB were significantly downregulated (*p* < 0.05), whereas MFSD2A showed marked upregulation in asthmatic bronchial epithelium. These findings establish PCTP, HADHB, and MFSD2A as key mediators of asthma-associated lipid metabolic reprogramming and underscore the bronchial epithelium as a critical site of metabolic dysfunction in asthma pathogenesis. Phosphatidylcholine transfer protein (PCTP/StARD2), a key member of the START domain superfamily, mediates phosphatidylcholine (PC) transfer between membranes [ 19 ] and facilitates pulmonary surfactant secretion in alveolar type II cells [ 20 ]. Paradoxically, PCTP-knockout mice exhibit normal lung development and unaltered surfactant PC profiles [ 21 ], suggesting functional redundancy or non-canonical biological roles. Our integrated bioinformatic and experimental analyses reveal significant PCTP downregulation specifically in asthmatic bronchial epithelium (*p* < 0.05), implicating its involvement in asthma pathogenesis .These findings redefine PCTP as a multifunctional regulator in asthma pathophysiology, extending beyond its established surfactant-related functions to encompass essential roles in airway homeostasis and disease progression. As a phosphatidylcholine (PC)-specific transfer protein of the same family as PCTP, StARD7 plays a critical role in maintaining intestinal epithelial barrier integrity and mitochondrial function. Its knockdown leads to mitochondrial degeneration, impaired oxidative respiration, and reduced expression of tight junction proteins. Moreover, the loss of StARD7 increases susceptibility to inflammatory bowel disease. [ 22 ]. Given these conserved functional properties and their shared START domain architecture, the observed PCTP downregulation in asthmatic bronchial epithelium may similarly compromise epithelial function through: (1) mitochondrial dysfunction, (2) impaired cellular energetics, and (3) disruption of junctional complexes, thereby contributing to asthma pathogenesis. These mechanistic parallels suggest that PCTP may fulfill analogous barrier-protective roles in respiratory epithelium that warrant systematic investigation. The trifunctional protein β-subunit (HADHB), a key mitochondrial enzyme in fatty acid β-oxidation [ 23 ], is significantly downregulated in asthmatic bronchial epithelium. HADHB regulates fatty acid oxidation (FAO) in macrophages and is essential for inflammation resolution and tissue repair. Its upregulation promotes conversion of pro-inflammatory M1-like macrophages to anti-inflammatory M2-like macrophages, thereby alleviating inflammatory conditions like ulcerative colitis [ 24 , 25 ]. Thus, HADHB mediates inflammatory responses and enables metabolic reprogramming of macrophages. HADHB deficiency likely impairs fatty acid metabolism, disrupts cellular energy homeostasis, and contributes to epithelial dysfunction—a recognized pathogenic mechanism in asthma [ 26 ]. While mitochondrial dysfunction in airway epithelium has been linked to asthma development, the causal relationships between altered lipid metabolism (particularly β-oxidation defects) and epithelial impairment remain unclear, highlighting a critical knowledge gap in metabolic aspects of asthma pathogenesis. MFSD2A demonstrates selective expression at the blood-brain barrier (BBB), where it maintains endothelial integrity. Genetic ablation studies confirm its absence increases BBB permeability without altering vascular architecture [ 27 ]. Beyond structural functions, MFSD2A mediates critical lipid transport processes: it facilitates uptake of esterified docosahexaenoic acid (DHA) for producing inflammation-resolving mediators [ 28 ], and regulates lysophosphatidylcholine (LPC) transport in alveolar type II epithelial cells. Deficiency in these cells disrupts morphology and reduces surfactant phospholipid content [ 29 ]. Collectively, MFSD2A emerges as a multifunctional regulator of barrier integrity and lipid homeostasis, with its tissue-specific roles contributing to protective or pathogenic outcomes depending on cellular context. While MFSD2A is well-established as a protective factor in various physiological contexts, our study reveals its paradoxical upregulation in asthmatic airways, suggesting potential involvement in disease pathogenesis. This apparent contradiction may be explained by MFSD2A's role in transporting sphingosine-1-phosphate (S1P), a bioactive lipid known to induce airway smooth muscle hyperreactivity and pulmonary inflammation in endothelial cells [ 30 , 31 ]. The concurrent dysregulation of key lipid metabolism genes—specifically, increased MFSD2A alongside decreased expression of PCTP and HADHB in the asthmatic epithelium—reveals a complex metabolic signature that could drive disease progression. These findings establish MFSD2A as a context-dependent regulator in asthma, where its functional outcome (protective versus pathogenic) likely depends on: (1) the specific cellular microenvironment, (2) the relative abundance of transported lipid species, and (3) disease state-specific signaling pathways. This metabolic paradigm shift identifies MFSD2A, PCTP, and HADHB as promising targets for novel therapeutic strategies designed to restore lipid homeostasis in asthma. While this study successfully identified asthma-associated core genes (PCTP, HADHB, and MFSD2A) through transcriptomic data mining and experimental validation, several limitations warrant consideration. Primary reliance on transcriptomic data may overlook crucial regulatory layers detectable via integrated multi-omics approaches (e.g., proteomics, metabolomics). Although wet-lab experiments confirmed dysregulation of these genes in asthma, their functional consequences for bronchial epithelial cell biology and disease pathogenesis remain incompletely characterized. Declarations Ethical Approval The animal study protocol was approved by the Institutional Animal Care and Use Committee (approval number BCJI0259). All procedures were conducted in accordance with the IACUC of Peking University Health Science Center for the Care and Use of Laboratory Animals. Funding This work was supported by the National Natural Science Foundation of China (grant numbers 82370032 and 82170028 to C.Chang, 81902909 to A. Aili), Beijing Natural Science Foundation (grant number 7232205 to C.Chang), and Key Clinical Project of Peking University Third Hospital (BYSYZD2023009 to C.Chang). Availability of data and materials All datasets (GSE series) used in this study are publicly available in the Gene Expression Omnibus (GEO) repository at https://www.ncbi.nlm.nih.gov/geo/. Competing Interests The authors declare that they have no competing interests Clinical trial number not applicable Author Contribution Z.L. and X.H. wrote the main manuscript text and prepared all figures and tables.H.L., L.C., Y.S., Y.G., T.H., X.Z. and D.M. conducted experiments, generated data and performed statistical analyses.A.A. supervised experimental work, interpreted data and critically revised the manuscript.C.C. conceived and designed the study, acquired funding, supervised the project and gave final approval for submission.All authors read, edited and approved the final manuscript. Data Availability All raw sequencing data analyzed in this study were downloaded from the publicly accessible Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE74986，GSE85567，GSE67472，GSE63142，GSE152004，GSE69683. No new datasets were generated for this article. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8669205","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604039170,"identity":"20274d06-a108-4c42-9e24-33bf5c36a4d0","order_by":0,"name":"Zemin Li","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zemin","middleName":"","lastName":"Li","suffix":""},{"id":604039171,"identity":"226839d2-3e9a-42fc-8bd6-c198b542a66b","order_by":1,"name":"Xiao Huo","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Huo","suffix":""},{"id":604039172,"identity":"187fb6e2-759b-4c11-af5a-8026a00c67c0","order_by":2,"name":"Huan Liu","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Liu","suffix":""},{"id":604039173,"identity":"513e326a-be51-4c67-bc60-5384d9a4bd79","order_by":3,"name":"Liting Cao","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liting","middleName":"","lastName":"Cao","suffix":""},{"id":604039174,"identity":"1cc2b723-60e5-426c-9d4b-907a3541f806","order_by":4,"name":"Ying Shang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Shang","suffix":""},{"id":604039175,"identity":"8e1917b2-1d2e-4241-9e26-124ca62b6427","order_by":5,"name":"yingying Ge","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"yingying","middleName":"","lastName":"Ge","suffix":""},{"id":604039176,"identity":"fe1377af-5183-4cfc-be66-00d74fd43291","order_by":6,"name":"Tingting Hu","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Hu","suffix":""},{"id":604039177,"identity":"af9e038f-b6d1-431a-b5e9-7606dc0fdf24","order_by":7,"name":"Xiaoqin Zhang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqin","middleName":"","lastName":"Zhang","suffix":""},{"id":604039178,"identity":"95da3ea2-129e-44f2-b128-1521ebd57355","order_by":8,"name":"Duo Mou","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Duo","middleName":"","lastName":"Mou","suffix":""},{"id":604039179,"identity":"5f707e3e-46bd-4f59-954d-23de86c89f8b","order_by":9,"name":"Abudureyimujiang Aili","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Abudureyimujiang","middleName":"","lastName":"Aili","suffix":""},{"id":604039180,"identity":"3c17a487-1ec3-4985-b2bd-81b10abd0518","order_by":10,"name":"Chun Chang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACCTBpk8DYAKLZiNeSRrqWwwkQHjFa+Gc3P3v4peZ8HvO0MwYMH8oOA0UaCFhy55i5scyx28WMs3MMGGecOwwUOYBfi4FEgpm0ZMPtxEagFmbetsMgEUJa0r8BtZyDaPlLnJYcM8mPDQcgWhiJ0SJxI6dMmuFYMtAvaQUHe86l80jcIKCFf0b6NskfNXZ5hrOTNz74UWYtxz+DgBYQYOYBEoYNDAwHgDQPYfVAwPgDSMgTpXQUjIJRMApGJAAADZ5C1wb+dUMAAAAASUVORK5CYII=","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chun","middleName":"","lastName":"Chang","suffix":""}],"badges":[],"createdAt":"2026-01-22 11:38:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8669205/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8669205/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104471864,"identity":"7caebf2c-803f-4da9-8b44-e009458c1f6c","added_by":"auto","created_at":"2026-03-12 07:28:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":213983,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA analysis and module identification for phospholipid metabolism genes in the GSE74986 dataset.(A) Scale-free fit index for various soft-thresholding powers. The power of β = 5 was selected as the soft-threshold to ensure a scale-free network.(B) Dendrogram of all genes clustered based on a dissimilarity measure (1-TOM). The identified modules are displayed in different colors.(C) Module-trait relationships. The turquoise module (r = -0.77, p = 7e-12) and the gray module (r = 0.57, p = 6e-06) showed significant correlation with asthma. The color legend indicates Pearson correlation coefficients.(D) Heatmap of the correlation between module eigengenes and the sample traits (health and asthma). According to the analysis, genes in the turquoise module are highly expressed in healthy samples, whereas genes in the gray module are highly expressed in asthma samples.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8669205/v1/73aa803bebc1384732ee45e5.png"},{"id":104471968,"identity":"5de09809-839c-41da-bf05-060c540d6d7d","added_by":"auto","created_at":"2026-03-12 07:28:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83169,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of the six-gene asthma prediction model across different datasets.(A) The ROC curve for the GSE67472 dataset, showing an AUC of 0.76 (95% CI: 0.64-0.83).(B) The ROC curve for the GSE63142 dataset, showing an AUC of 0.83 (95% CI: 0.73-0.90).(C) The ROC curve for the nasal epithelial cell dataset GSE152004, showing an AUC of 0.64 (95% CI: 0.59-0.67).(D) The ROC curve for the peripheral blood cell dataset GSE69683, showing an AUC of 0.72 (95% CI: 0.66-0.76).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8669205/v1/feaa4ed8d31b552e5311f703.png"},{"id":104471947,"identity":"b615fe3e-2a8a-4a26-a328-f93145fd8c09","added_by":"auto","created_at":"2026-03-12 07:28:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":339532,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differential gene expression in lung tissues and asthma cell model.\u003cstrong\u003e(A)\u003c/strong\u003eRepresentative histopathology of lung tissues from control and asthma groups, showing structural differences. Scale bars: 100 μm.\u003cstrong\u003e(B)\u003c/strong\u003eIHC results of target genes (PCTP, and FIG4) in lung tissues. \u003cstrong\u003e(C)\u003c/strong\u003e IHC results of target genes (MFSD2A, HADHB) in lung tissues. \u003cstrong\u003e(D)\u003c/strong\u003eWB analysis in the asthma cell model .Data are presented as mean ± SD. Statistical significance is indicated as *P \u0026lt; 0.05, *\u003cem\u003eP \u0026lt; 0.01.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8669205/v1/77bd74bd415192564217f841.png"},{"id":104781020,"identity":"58d56371-8e04-475f-9796-c01cdfbb7f0e","added_by":"auto","created_at":"2026-03-17 07:54:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1391793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8669205/v1/0ad7d43b-d5bd-4048-bf61-1d8f1c3f0b24.pdf"},{"id":104471950,"identity":"9971c172-76c8-4a2c-8990-2bd00d6e0b93","added_by":"auto","created_at":"2026-03-12 07:28:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6653404,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8669205/v1/3ea54837e4380da28b81c021.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of differential expression in phospholipid metabolism-related genes in bronchial epithelial cells of asthma patients ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBronchial asthma (commonly termed asthma) is a prevalent chronic disease that imposes a substantial global health burden. Epidemiological studies indicate that the rate of poorly controlled asthma among adults is 15.1% in high- and middle-income countries but rises to 32.1% in low- and lower-middle-income countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, asthma prevalence among individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years reaches 4.5%, affecting over 47\u0026nbsp;million people [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAsthma pathogenesis is closely linked to bronchial epithelial cell dysfunction. These cells maintain respiratory barrier integrity and permeability by regulating intercellular connections, adhesion molecule expression, and tight junction formation. As the first line of defense against allergens and inflammatory mediators, they prevent infiltration into deeper tissues while participating in immune regulation and inflammatory responses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. When activated by allergens such as house dust mite (HDM), bronchial epithelial cells secrete cytokines\u0026mdash;including thymic stromal lymphopoietin (TSLP) and granulocyte-macrophage colony-stimulating factor (GM-CSF)\u0026mdash;that activate dendritic cells, subsequently driving Th2 cell differentiation and immune responses [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistorically, asthma research focused predominantly on immune mechanisms. However, recent studies highlight the emerging role of metabolic dysregulation in asthma pathogenesis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Lipids, in particular, are increasingly recognized as potential biomarkers and therapeutic targets [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For instance, differences in lipid metabolism have been observed in the induced sputum of asthma patients versus healthy individuals [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Lipid metabolites such as phosphatidic acid (PA) and phosphatidylglycerol (PG) may serve as diagnostic biomarkers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and distinct plasma lipid metabolite profiles have been identified in asthma patients compared to healthy controls [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Phospholipids regulate bronchial epithelial cell functions and inflammatory responses through multiple mechanisms: Lysophosphatidic acid (LPA) modulates inflammation-related gene expression via G protein-coupled receptors, inhibits CCL5/RANTES secretion, affects IRF-1 activity, and induces IL-13Rα2 expression to regulate IL-13 signaling [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Lipid phosphate phosphatase-1 (LPP-1) regulates LPA-related signaling and IL-8 secretion [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], while LPA influences c-Met and E-cadherin localization and function [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Sphingosine-1-phosphate (S1P) promotes epithelial-mesenchymal transition and inflammatory factor expression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Despite these advances, the mechanisms underlying these metabolic differences remain poorly understood.\u003c/p\u003e \u003cp\u003eThis study aims to elucidate metabolic differences in bronchial epithelial cells between asthma patients and healthy individuals, specifically focusing on phospholipid metabolism-related gene expression. Using publicly available gene expression datasets, we identified differentially expressed genes, developed predictive models to evaluate their diagnostic potential, and conducted experimental validation. By uncovering key genetic markers associated with asthma, our research provides novel insights to advance diagnostic tools, deepen understanding of asthma pathogenesis, and inform future therapeutic strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Preparation and Preprocessing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAsthma-related gene expression profile datasets were obtained from the GEO database using the following inclusion criteria:Sample size exceeding 50, including both asthma patients and healthy controls;Availability of basic demographic information (e.g., age and gender) to account for potential confounding factors;Restriction to bronchial epithelial cell samples to ensure tissue specificity for phospholipid metabolism gene analysis.Based on these criteria, four datasets comprising 431 samples (321 asthma patients and 110 controls) were selected. Two additional datasets (GSE152004 and GSE69683) were included to validate model performance across diverse sample types.\u003c/p\u003e\n\u003cp\u003eFor GSE74986, data preprocessing involved cleaning and normalization using the Robust Multi-Array Average (RMA) method. Gene symbols were assigned based on the GPL570 annotation, averaging values for multiple probes corresponding to the same gene. Candidate lipid metabolism-related genes were validated in GSE85567 through differential expression analysis with DESeq2 (P\u0026lt;0.05).↳\u003c/p\u003e\n\u003cp\u003eA gene co-expression network was constructed using the WGCNA R package, generating similarity and adjacency matrices, followed by hierarchical clustering to identify modules using the dynamic tree cut method based on average linkage hierarchical clustering, with the minimum module size set to 10 genes. Asthma-related modules were selected based on significant correlations (|r|\u0026nbsp;≥\u0026nbsp;0.5, P \u0026lt; 0.05) with Module Eigengenes (MEs). The correlation analysis between MEs and the asthma (case) and control groups was performed using the Pearson correlation method.\u003c/p\u003e\n\u003cp\u003ePredictive models were built using logistic regression on independent datasets (GSE67472 and GSE63142) with validated differentially expressed genes. Model performance was evaluated using ROC analysis to compute the area under the curve (AUC), with comparisons conducted using the DeLong test using the pROC package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHouse dust mite model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix-to-eight-week-old female C57BL/6J mice were obtained from the Department of Laboratory Animal Science, Peking University Health Science Center. Mice were briefly anesthetized with isoflurane for intranasal procedures. For sensitization, mice received 1\u0026nbsp;μg of house dust mite (HDM) extract in 40\u0026nbsp;μL of phosphate-buffered saline (PBS) via intranasal administration. Seven days after sensitization, mice were treated with intranasal administration of 10\u0026nbsp;μg HDM in 40\u0026nbsp;μL PBS for five consecutive days. Four days after the final treatment, mice were euthanized by cervical dislocation while under deep anesthesia with isoflurane, in accordance with the institutional guidelines for animal welfare and ethics. Lungs were then collected and dissected for subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImmunohistochemistry\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMouse lung tissues were fixed in 4% paraformaldehyde for 24 h at 4°C, embedded in paraffin, and sectioned into 5-μm slices. After deparaffinization in xylene and rehydration through a graded ethanol series, antigen retrieval was performed in citrate buffer (pH 6.0) at 95°C for 30 min. Endogenous peroxidase activity was quenched with 3% H\u0026lt;sub\u0026gt;2\u0026lt;/sub\u0026gt;O\u0026lt;sub\u0026gt;2\u0026lt;/sub\u0026gt; for 15 min. Tissue sections were then incubated overnight at 4°C with primary antibodies against FIG4, HADHB, MFSD2A, and PCTP (Biodragon, Beijing, China; dilutions: 1:200–1:400). Following PBS washes, sections were incubated with biotinylated secondary antibody for 1 h at room temperature, developed using an ABC kit with DAB substrate, counterstained with hematoxylin, and finally dehydrated and mounted for microscopic analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHuman Bronchial Epithelial Cell (hBEC) stimulation.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehBECs (BEAS-2B) were divided into two groups: the HDM group, which was treated with 50 ug/ml HDM solution; the control group, which was treated with PBS alone. After 24 hours of stimulation, the cells were collected for subsequent experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWestern Blot\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehBECs\u0026nbsp;were collected and lysed with RIPA buffer containing protease inhibitors. \u0026nbsp;Equal amounts of protein (10 µg) were separated by 10% SDS-PAGE and transferred to a PVDF membrane (Yamei, China) at 300mA for 1.5 hour.\u003c/p\u003e\n\u003cp\u003eThe membrane was incubated overnight at 4°C with primary antibodies(all obtained from Abcam, UK) against MFSD2A, HADHB, FIG4, and PCTP \u0026nbsp;at the recommended dilution (1:1000-1:5000). Quantification of protein bands was performed using ImageJ software (NIH, USA). The expression levels of target proteins were normalized to β-actin (ProteinTech, China) as a loading control.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of GEO datasets\u003c/h2\u003e \u003cp\u003eThis study incorporated six Gene Expression Omnibus (GEO) datasets, including four bronchial epithelial cell datasets and two composite datasets containing matched nasal epithelial cells and peripheral blood mononuclear cells (PBMCs). All datasets were rigorously selected based on complete clinical annotations, standardized processing protocols, and demographic matching (age and sex) to ensure comparability. This integrated approach enabled a comprehensive examination of both respiratory and systemic immune profiles while controlling for key confounding variables(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEpithelia source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean Age range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMale / Female\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCovariates used in analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDEGs (Up/Down)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE74986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26628680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBronchial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36 (14\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eage, sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4886 (2473/2413)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE85567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27942592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eairway epithelial cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39 (19\u0026ndash;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49/36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eage, sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1886 (685/1201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE67472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25611785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAirway epithelial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35 (20\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51/54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE63142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25338189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBronchial Epithelial Cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36 (14\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36/64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE152004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35347136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enasal epithelium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (11\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e331/364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE69683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27925796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeripheral Blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39 (27\u0026ndash;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e223/275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1173\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e451\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1624\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of Key Genes in Phospholipid Metabolism\u003c/h3\u003e\n\u003cp\u003eTo identify genes involved in phospholipid metabolism, we extracted relevant gene sets from the KEGG (\"Glycerophospholipid metabolism\"; 77 genes) and REACTOME (\"Phospholipid metabolism\"; 211 genes) databases. After merging these datasets, we obtained 230 unique genes, including 58 overlapping genes.\u003c/p\u003e \u003cp\u003eWe performed weighted gene co-expression network analysis (WGCNA) on the 211 genes from the GSE74986 dataset. Following outlier removal and soft threshold determination (power\u0026thinsp;=\u0026thinsp;5; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), we identified six distinct modules using the blockwiseModules method (maximum module size\u0026thinsp;=\u0026thinsp;10): blue (41 genes), brown (40 genes), green (18 genes), gray (25 genes), turquoise (61 genes), and yellow (20 genes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Notably, the turquoise (r = -0.77, p\u0026thinsp;=\u0026thinsp;7e-12) and gray (r\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;=\u0026thinsp;6e-06) modules showed significant correlations with asthma (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The turquoise module genes were highly expressed in healthy samples, whereas gray module genes were upregulated in asthma samples, prompting their selection for further analysis .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrior to network construction, we filtered out genes with low correlation (cutoff\u0026thinsp;\u0026lt;\u0026thinsp;0.2), excluding 57 genes(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Using Cytoscape, we ranked the remaining genes by degree and retained those with a degree\u0026thinsp;\u0026ge;\u0026thinsp;3, yielding 49 candidate genes.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. WGCNA analysis and module identification for phospholipid metabolism genes in the GSE74986 dataset.(A) Scale-free fit index for various soft-thresholding powers. The power of β\u0026thinsp;=\u0026thinsp;5 was selected as the soft-threshold to ensure a scale-free network.(B) Dendrogram of all genes clustered based on a dissimilarity measure (1-TOM). The identified modules are displayed in different colors.(C) Module-trait relationships. The turquoise module (r = -0.77, p\u0026thinsp;=\u0026thinsp;7e-12) and the gray module (r\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;=\u0026thinsp;6e-06) showed significant correlation with asthma. The color legend indicates Pearson correlation coefficients.(D) Heatmap of the correlation between module eigengenes and the sample traits (health and asthma). According to the analysis, genes in the turquoise module are highly expressed in healthy samples, whereas genes in the gray module are highly expressed in asthma samples.\u003c/p\u003e\n\u003ch3\u003eCross-Dataset Validation Reveals Key Dysregulated Genes in Asthma Pathogenesis\u003c/h3\u003e\n\u003cp\u003eDifferential expression analysis of the GSE74986 dataset (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) identified 4,886 significantly altered genes in asthma patients (2,473 upregulated; 2,413 downregulated), including 42 of the 49 candidate genes (85.7%) previously identified by WGCNA (Table S2). Validation in the independent GSE85567 dataset confirmed 1,886 DEGs (685 upregulated; 1,201 downregulated), with six genes showing consistent dysregulation across both cohorts (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, while five genes exhibited stable downregulation (fold change\u0026thinsp;\u0026lt;\u0026thinsp;1), MFSD2A demonstrated unique upregulation (fold change\u0026thinsp;\u0026gt;\u0026thinsp;1), suggesting its distinct role in asthma pathophysiology. This robust two-stage analysis highlights the reliability of these phospholipid metabolism-associated gene signatures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eGSE74986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eGSE85567\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egName\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFold change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAve (Asthma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAve (Healthy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFold change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAve (Asthma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAve (Healthy)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure\u0026nbsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.4502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.242E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.5476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.7444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHADHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.512E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.3186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.4643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFSD2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.242E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.6189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.5198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.680E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.2946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIK3CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.343E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.843E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.7794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.4728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the Predictive Model\u003c/h2\u003e \u003cp\u003eWe developed a classification model based on six-gene expression profiles to discriminate asthma patients from healthy controls, validated across multiple independent datasets. Using bronchial epithelial cell samples, the model demonstrated robust performance with an AUC of 0.76 (95% CI: 0.64\u0026ndash;0.83) in the GSE67472 cohort (62 asthma/43 healthy) and 0.83 (95% CI: 0.73\u0026ndash;0.90) in the GSE63142 cohort (128 asthma/27 healthy). When applied to alternative sample types, the model maintained moderate diagnostic accuracy, achieving AUCs of 0.64 (95% CI: 0.59\u0026ndash;0.67) in nasal epithelial cells (GSE152004; 441 asthma/254 healthy) and 0.72 (95% CI: 0.66\u0026ndash;0.76) in peripheral blood cells (GSE69683; 411 asthma/87 healthy)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the performance in bronchial epithelium significantly outperformed nasal samples (deLong test P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting tissue-specific predictive value. These results demonstrate the model's strongest diagnostic utility in bronchial samples while retaining cross-tissue applicability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eVerification of six DEGS through In Vivo and In Vitro experiments\u003c/h2\u003e \u003cp\u003eEvidence indicates that there is a reduction of PTEN in asthmatic mice. PTEN inhibits PI3K activity by dephosphorylating the signaling lipid PIP3, thereby alleviating bronchial inflammation and airway hyperreactivity[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, mice with a defect in PIK3CG exhibit lower levels of airway hyperreactivity, airway inflammation, and airway remodeling in response to allergen stimulation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Previous studies have confirmed the roles of these two genes in asthma and partially support the accuracy of the predicted genes in this study. Therefore, we focused on the verification of the remaining four genes: MFSD2A, PCTP, HADHB, and Fig.\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eEvidence indicates reduced PTEN levels in asthmatic mice. PTEN inhibits PI3K activity by dephosphorylating the signaling lipid PIP3, thereby mitigating bronchial inflammation and airway hyperreactivity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consistent with this, mice with defective PIK3CG exhibit attenuated allergen-induced airway hyperreactivity, inflammation, and remodeling [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These established roles of PTEN and PIK3CG in asthma partially validate our gene prediction approach. Consequently, we prioritized experimental verification of the remaining four candidate genes: MFSD2A, PCTP, HADHB, and Fig.\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eCompared to the control group, the lung tissue of HDM group exhibited pronounced inflammatory cell infiltration around the airways. Additionally, a marked rise in the number of mucus-secreting cells was observed in the airways of HDM-treated mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Immunohistochemistry (IHC) of lung tissue and cell-specific Western blot analysis demonstrated no significant difference in Fig.\u0026nbsp;4 expression within epithelial cells. In contrast, bronchial epithelial cells from asthmatic mice exhibited significantly decreased PCTP and HADHB expression, alongside a notable increasing trend in MFSD2A expression. These results indicate that PCTP, HADHB, and MFSD2A exhibit specific dysregulation within the bronchial epithelium in asthma compared to controls.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBronchial epithelial cells are critical guardians of respiratory barrier integrity, preventing allergen penetration while actively participating in asthma pathogenesis through the regulation of intercellular connections and secretion of Th2-polarizing cytokines. Growing evidence indicates that cellular metabolic processes\u0026mdash;particularly lipid metabolism\u0026mdash;serve as essential regulators of epithelial cell function and immune responses. Beyond their structural roles in cellular membranes, phospholipids dynamically modulate cell signaling and inflammatory processes. Recent studies report that disturbances in phospholipid metabolism, such as altered levels of lysophosphatidic acid (LPA), phosphatidic acid (PA), and sphingosine-1-phosphate (S1P), can influence bronchial epithelial cell behavior, cytokine secretion, and epithelial-mesenchymal transition\u0026mdash;a process relevant to asthma pathogenesis. Although aberrant phospholipid metabolism has been observed in asthmatic airway epithelium, whether these alterations directly drive asthma development requires further investigation. Thus, elucidating the regulatory mechanisms of phospholipid metabolism in bronchial epithelial cells is crucial for advancing our understanding of asthma pathogenesis and identifying novel therapeutic targets.\u003c/p\u003e \u003cp\u003eThis study systematically investigated phospholipid metabolism-related genes in asthma using an integrated bioinformatics and experimental approach. Through weighted gene co-expression network analysis (WGCNA) of bronchial epithelial cells combined with differential expression profiling, we identified six candidate genes. Subsequent experimental validation using immunohistochemistry (IHC) and Western blotting confirmed three core regulators: PCTP and HADHB were significantly downregulated (*p* \u0026lt; 0.05), whereas MFSD2A showed marked upregulation in asthmatic bronchial epithelium. These findings establish PCTP, HADHB, and MFSD2A as key mediators of asthma-associated lipid metabolic reprogramming and underscore the bronchial epithelium as a critical site of metabolic dysfunction in asthma pathogenesis.\u003c/p\u003e \u003cp\u003ePhosphatidylcholine transfer protein (PCTP/StARD2), a key member of the START domain superfamily, mediates phosphatidylcholine (PC) transfer between membranes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and facilitates pulmonary surfactant secretion in alveolar type II cells [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Paradoxically, PCTP-knockout mice exhibit normal lung development and unaltered surfactant PC profiles [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], suggesting functional redundancy or non-canonical biological roles. Our integrated bioinformatic and experimental analyses reveal significant PCTP downregulation specifically in asthmatic bronchial epithelium (*p* \u0026lt; 0.05), implicating its involvement in asthma pathogenesis .These findings redefine PCTP as a multifunctional regulator in asthma pathophysiology, extending beyond its established surfactant-related functions to encompass essential roles in airway homeostasis and disease progression.\u003c/p\u003e \u003cp\u003eAs a phosphatidylcholine (PC)-specific transfer protein of the same family as PCTP, StARD7 plays a critical role in maintaining intestinal epithelial barrier integrity and mitochondrial function. Its knockdown leads to mitochondrial degeneration, impaired oxidative respiration, and reduced expression of tight junction proteins. Moreover, the loss of StARD7 increases susceptibility to inflammatory bowel disease. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Given these conserved functional properties and their shared START domain architecture, the observed PCTP downregulation in asthmatic bronchial epithelium may similarly compromise epithelial function through: (1) mitochondrial dysfunction, (2) impaired cellular energetics, and (3) disruption of junctional complexes, thereby contributing to asthma pathogenesis. These mechanistic parallels suggest that PCTP may fulfill analogous barrier-protective roles in respiratory epithelium that warrant systematic investigation.\u003c/p\u003e \u003cp\u003eThe trifunctional protein β-subunit (HADHB), a key mitochondrial enzyme in fatty acid β-oxidation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], is significantly downregulated in asthmatic bronchial epithelium. HADHB regulates fatty acid oxidation (FAO) in macrophages and is essential for inflammation resolution and tissue repair. Its upregulation promotes conversion of pro-inflammatory M1-like macrophages to anti-inflammatory M2-like macrophages, thereby alleviating inflammatory conditions like ulcerative colitis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, HADHB mediates inflammatory responses and enables metabolic reprogramming of macrophages. HADHB deficiency likely impairs fatty acid metabolism, disrupts cellular energy homeostasis, and contributes to epithelial dysfunction\u0026mdash;a recognized pathogenic mechanism in asthma [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. While mitochondrial dysfunction in airway epithelium has been linked to asthma development, the causal relationships between altered lipid metabolism (particularly β-oxidation defects) and epithelial impairment remain unclear, highlighting a critical knowledge gap in metabolic aspects of asthma pathogenesis.\u003c/p\u003e \u003cp\u003eMFSD2A demonstrates selective expression at the blood-brain barrier (BBB), where it maintains endothelial integrity. Genetic ablation studies confirm its absence increases BBB permeability without altering vascular architecture [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Beyond structural functions, MFSD2A mediates critical lipid transport processes: it facilitates uptake of esterified docosahexaenoic acid (DHA) for producing inflammation-resolving mediators [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and regulates lysophosphatidylcholine (LPC) transport in alveolar type II epithelial cells. Deficiency in these cells disrupts morphology and reduces surfactant phospholipid content [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Collectively, MFSD2A emerges as a multifunctional regulator of barrier integrity and lipid homeostasis, with its tissue-specific roles contributing to protective or pathogenic outcomes depending on cellular context.\u003c/p\u003e \u003cp\u003eWhile MFSD2A is well-established as a protective factor in various physiological contexts, our study reveals its paradoxical upregulation in asthmatic airways, suggesting potential involvement in disease pathogenesis. This apparent contradiction may be explained by MFSD2A's role in transporting sphingosine-1-phosphate (S1P), a bioactive lipid known to induce airway smooth muscle hyperreactivity and pulmonary inflammation in endothelial cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe concurrent dysregulation of key lipid metabolism genes\u0026mdash;specifically, increased MFSD2A alongside decreased expression of PCTP and HADHB in the asthmatic epithelium\u0026mdash;reveals a complex metabolic signature that could drive disease progression. These findings establish MFSD2A as a context-dependent regulator in asthma, where its functional outcome (protective versus pathogenic) likely depends on: (1) the specific cellular microenvironment, (2) the relative abundance of transported lipid species, and (3) disease state-specific signaling pathways. This metabolic paradigm shift identifies MFSD2A, PCTP, and HADHB as promising targets for novel therapeutic strategies designed to restore lipid homeostasis in asthma.\u003c/p\u003e \u003cp\u003eWhile this study successfully identified asthma-associated core genes (PCTP, HADHB, and MFSD2A) through transcriptomic data mining and experimental validation, several limitations warrant consideration. Primary reliance on transcriptomic data may overlook crucial regulatory layers detectable via integrated multi-omics approaches (e.g., proteomics, metabolomics). Although wet-lab experiments confirmed dysregulation of these genes in asthma, their functional consequences for bronchial epithelial cell biology and disease pathogenesis remain incompletely characterized.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal study protocol was approved by the Institutional Animal Care and Use Committee (approval number BCJI0259). All procedures were conducted in accordance with the IACUC of Peking University Health Science Center for the Care and Use of Laboratory Animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant numbers 82370032 and 82170028 to C.Chang, 81902909 to A. Aili), Beijing Natural Science Foundation (grant number 7232205 to C.Chang), and Key Clinical Project of Peking University Third Hospital (BYSYZD2023009 to C.Chang).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets (GSE series) used in this study are publicly available in the Gene Expression Omnibus (GEO) repository at https://www.ncbi.nlm.nih.gov/geo/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;not applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.L. and X.H. wrote the main manuscript text and prepared all figures and tables.H.L., L.C., Y.S., Y.G., T.H., X.Z. and D.M. conducted experiments, generated data and performed statistical analyses.A.A. supervised experimental work, interpreted data and critically revised the manuscript.C.C. conceived and designed the study, acquired funding, supervised the project and gave final approval for submission.All authors read, edited and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll raw sequencing data analyzed in this study were downloaded from the publicly accessible Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE74986，GSE85567，GSE67472，GSE63142，GSE152004，GSE69683. No new datasets were generated for this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGarc\u0026iacute;a-Marcos, L., et al., \u003cem\u003eAsthma management and control in children, adolescents, and adults in 25 countries: a Global Asthma Network Phase I cross-sectional study.\u003c/em\u003e Lancet Glob Health, 2023. \u003cstrong\u003e11\u003c/strong\u003e(2): p. e218-e228.\u003c/li\u003e\n\u003cli\u003eHuang, K., et al., \u003cem\u003ePrevalence, risk factors, and management of asthma in China: a national cross-sectional study.\u003c/em\u003e Lancet, 2019. \u003cstrong\u003e394\u003c/strong\u003e(10196): p. 407-418.\u003c/li\u003e\n\u003cli\u003eHolgate, S.T. and D.E. 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Wei, and D.E. Cohen, \u003cem\u003ePC-TP/StARD2: Of membranes and metabolism.\u003c/em\u003e Trends Endocrinol Metab, 2010. \u003cstrong\u003e21\u003c/strong\u003e(7): p. 449-56.\u003c/li\u003e\n\u003cli\u003eMouro, I., et al., \u003cem\u003eCloning, expression, and chromosomal mapping of a human ATPase II gene, member of the third subfamily of P-type ATPases and orthologous to the presumed bovine and murine aminophospholipid translocase.\u003c/em\u003e Biochem Biophys Res Commun, 1999. \u003cstrong\u003e257\u003c/strong\u003e(2): p. 333-9.\u003c/li\u003e\n\u003cli\u003evan Helvoort, A., et al., \u003cem\u003eMice without phosphatidylcholine transfer protein have no defects in the secretion of phosphatidylcholine into bile or into lung airspaces.\u003c/em\u003e Proc Natl Acad Sci U S A, 1999. \u003cstrong\u003e96\u003c/strong\u003e(20): p. 11501-6.\u003c/li\u003e\n\u003cli\u003eUddin, J., et al., \u003cem\u003eSTARD7 maintains intestinal epithelial mitochondria architecture, barrier integrity, and protection from colitis.\u003c/em\u003e JCI Insight, 2024. \u003cstrong\u003e9\u003c/strong\u003e(22).\u003c/li\u003e\n\u003cli\u003eZhou, Z., J. Zhou, and Y. Du, \u003cem\u003eEstrogen receptor alpha interacts with mitochondrial protein HADHB and affects beta-oxidation activity.\u003c/em\u003e Mol Cell Proteomics, 2012. \u003cstrong\u003e11\u003c/strong\u003e(7): p. M111.011056.\u003c/li\u003e\n\u003cli\u003eNakayama, Y., et al., \u003cem\u003eA long noncoding RNA regulates inflammation resolution by mouse macrophages through fatty acid oxidation activation.\u003c/em\u003e Proc Natl Acad Sci U S A, 2020. \u003cstrong\u003e117\u003c/strong\u003e(25): p. 14365-14375.\u003c/li\u003e\n\u003cli\u003eLv, Q., et al., \u003cem\u003eDidymin switches M1-like toward M2-like macrophage to ameliorate ulcerative colitis via fatty acid oxidation.\u003c/em\u003e Pharmacol Res, 2021. \u003cstrong\u003e169\u003c/strong\u003e: p. 105613.\u003c/li\u003e\n\u003cli\u003ePrakash, Y.S., C.M. Pabelick, and G.C. Sieck, \u003cem\u003eMitochondrial Dysfunction in Airway Disease.\u003c/em\u003e Chest, 2017. \u003cstrong\u003e152\u003c/strong\u003e(3): p. 618-626.\u003c/li\u003e\n\u003cli\u003eZhao, Z. and B.V. Zlokovic, \u003cem\u003eBlood-brain barrier: a dual life of MFSD2A?\u003c/em\u003e Neuron, 2014. \u003cstrong\u003e82\u003c/strong\u003e(4): p. 728-30.\u003c/li\u003e\n\u003cli\u003eUngaro, F., et al., \u003cem\u003eMFSD2A Promotes Endothelial Generation of Inflammation-Resolving Lipid Mediators and Reduces Colitis in Mice.\u003c/em\u003e Gastroenterology, 2017. \u003cstrong\u003e153\u003c/strong\u003e(5): p. 1363-1377.e6.\u003c/li\u003e\n\u003cli\u003eWong, B.H., et al., \u003cem\u003eThe lipid transporter Mfsd2a maintains pulmonary surfactant homeostasis.\u003c/em\u003e J Biol Chem, 2022. \u003cstrong\u003e298\u003c/strong\u003e(3): p. 101709.\u003c/li\u003e\n\u003cli\u003eWang, Z., et al., \u003cem\u003eMfsd2a and Spns2 are essential for sphingosine-1-phosphate transport in the formation and maintenance of the blood-brain barrier.\u003c/em\u003e Sci Adv, 2020. \u003cstrong\u003e6\u003c/strong\u003e(22): p. eaay8627.\u003c/li\u003e\n\u003cli\u003eRoviezzo, F., et al., \u003cem\u003eS1P-induced airway smooth muscle hyperresponsiveness and lung inflammation in vivo: molecular and cellular mechanisms.\u003c/em\u003e Br J Pharmacol, 2015. \u003cstrong\u003e172\u003c/strong\u003e(7): p. 1882-93.\u003c/li\u003e\n\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":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Asthma, Phospholipid Metabolism, Bronchial Epithelial Cells","lastPublishedDoi":"10.21203/rs.3.rs-8669205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8669205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAsthma is a chronic respiratory disease that substantially compromises quality of life. While phospholipid metabolism is implicated in asthma pathophysiology, its specific role remains unclear. This study investigated metabolic alterations related to phospholipid metabolism in bronchial epithelial cells (BECs) of asthma patients compared to healthy individuals, aiming to identify potential biomarkers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed gene expression datasets from the GEO database to identify phospholipid metabolism-associated differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) identified asthma-associated modules. A logistic regression predictive model was constructed and validated using an independent dataset. Validation of key differential genes was performed using established in vivo and in vitro approaches.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSix phospholipid metabolism-related candidate genes exhibited differential expression in asthmatic epithelium. The predictive model demonstrated robust diagnostic performance in BEC samples, with AUC values of 0.76 and 0.83 in the training and validation sets, respectively. In vivo and in vitro validation revealed specific downregulation trends for PCTP and HADHB, and specific upregulation trends for MFSD2A, in asthmatic BECs. This implicates their potential contribution to pathogenesis and utility as diagnostic biomarkers.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study reveals that dysregulation of phospholipid metabolism-related genes (particularly PCTP, HADHB, and MFSD2A) in bronchial epithelial cells is a key characteristic of asthma. These alterations not only contribute to the onset and progression of the disease, but the gene signature model derived from them also demonstrates significant potential as novel diagnostic biomarkers. This provides new avenues for understanding the pathological mechanisms of asthma and developing diagnostic tools.\u003c/p\u003e","manuscriptTitle":"Investigation of differential expression in phospholipid metabolism-related genes in bronchial epithelial cells of asthma patients ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:25:48","doi":"10.21203/rs.3.rs-8669205/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T07:40:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T17:29:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T23:36:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T16:23:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T07:55:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103350237155710532985667155928392562979","date":"2026-03-19T16:04:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248929544643110493616326479998644915935","date":"2026-03-19T15:00:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256727889546148258790502886065134765954","date":"2026-03-19T13:01:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178888346506503457652050328364649566795","date":"2026-03-19T12:44:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T21:04:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330227582041130632932356414101127961929","date":"2026-03-09T21:13:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312982907386243537270933887874729373227","date":"2026-03-06T09:13:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T05:23:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T05:35:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-12T09:29:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2026-02-11T16:28:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50808aa9-1b1f-4d59-b349-af53d0e5b0ed","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T07:40:47+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T07:54:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:25:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8669205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8669205","identity":"rs-8669205","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}