Identification of Sex-Specific Candidate Genes Underlying Childhood Asthma Using Integrative Transcriptomic and Mendelian Randomization Approaches

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Methods Blood gene expression datasets from children with asthma and healthy controls (GSE27011 and GSE203482) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between asthma and normal blood samples were identified using limma package, with sex stratification. Functional enrichment analyses were performed to characterize these DEGs. Mendelian Randomization (MR) analysis was further applied to identify sex-specific causal genes. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potential of target genes. Bioinformatics analyses were used to explore the biological functions of candidate genes. Drug prediction and molecular docking analyses were employed to further evaluate the therapeutic potential of the identified drug targets. Results We identified 74 female-specific and 112 male-specific DEGs in pediatric asthma. Functional enrichment analysis indicated that these DEGs were potentially implicated in sex-specific asthma pathogenesis. MR analysis identified 5 causal genes in females (GNG2, HAVCR2, NMUR1, AUTS2, and DTHD1) and 3 in males (CCNE1, KRT73, and NMUR1). Five genes (GNG2, HAVCR2, NMUR1, CCNE1, and KRT73) exhibited consistent differential expression patterns across both MR and transcriptomic analysis, and were thus retained as core candidates. ROC analysis validated their diagnostic potential, with the area under the curve (AUC) values all exceeding 0.7 in the GSE27011 cohort. Immune infiltration analysis further revealed a significant elevation in resting natural killer (NK) cells in female asthma patients, while male asthma samples displayed increased resting NK cells, M0 macrophages, and resting mast cells. Molecular docking analysis showed favorable binding affinity between drugs and proteins with available structural information. Conclusion By integrating transcriptome analysis with MR approaches, we identified GNG2 and NMUR1 as female-specific causal genes in childhood asthma, along with CCNE1, KRT73 as male-specific causal genes, while NMUR1 was shared between sexes. This integrative strategy may facilitate the development of precise biomarkers and therapeutic targets for childhood asthma. child asthma sex transcriptome data Mendelian randomization analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Key Message This study identifies sex-specific causal genes underlying childhood asthma disparities through integrated transcriptomic and Mendelian Randomization (MR) analysis. Key findings include: Female-specific genes: GNG2 and NMUR1 drive asthma risk via immune regulation, While Male-specific genes as CCNE1 and KRT73 associate with airway remodeling. Shared gene: NMUR1 mediates neuro-immune interactions in both sexes. Introduction Asthma is a heterogeneous disease characterized by chronic airway inflammation involving the synergistic participation of multiple inflammatory cells and cytokines, accompanied by symptoms such as wheezing, shortness of breath, coughing and chest tightness [ 1 ] . There are nearly 300 million asthma patients worldwide [ 2 ] ; the overall prevalence rate of childhood asthma worldwide has reached 10.2% [ 3 ] . As the most common chronic respiratory disease in childhood, asthma recurs frequently and is difficult to cure. It harms children's health and burdens families and society economically, with children from disadvantaged backgrounds being particularly affected [ 4 ] . The heterogeneity of asthma is also reflected in sex and age. In adults, asthma is more prevalent in women than in men and is more likely to progress to severe asthma [ 5 ] . In children, asthma prevalence is higher in boys than in girls prior to puberty; this sex disparity reverses during adulthood, with both asthma prevalence and severity displaying a marked upsurge in females [ 6 ] . Boys receive an asthma diagnosis at a younger age compared to girls, and show elevated peripheral blood eosinophil levels and serum periostin [ 7 ] . Boys are more likely to have T2-high asthma, characterized by type 2 inflammation, while girls more frequently present with T2-low asthma, which is non-type 2 and often associated with overweight or obesity [ 8 ] . Previous studies show that sex disparities in asthma are related to distinct genetic factors, immune dynamics, sex hormones, and environment-specific responses [ 9 ] . However, mere recognition of sex-based disparities in asthma is inadequate, and causal inference is essential in the precision medicine era. Moreover, clinically actionable biomarkers for personalized asthma care are still limited. Genetic advancements have deepened our understanding of asthma’s molecular mechanisms and highlighted sex differences in asthma. Genome-wide association studies (GWAS) have identified multiple genetic loci associated with sex interaction effects in childhood asthma. For example, SNP rs1255383 in the 10q11.21 region (near ZNF33B) shows opposing associations with atopic markers between boys and girls [ 10 ] . The GAGC haplotype of the IL-9R confers significant protection against wheezing in boys exposed to inhaled allergens, with no such effect observed in girls [ 11 ] . The heterozygous SNP rs2069727 in IFNG increases asthma risk in boys but decreases it in girls [ 12 ] . Although the aforementioned GWAS have identified numerous sex-related genetic variants, they still have limitations. Firstly, SNPs identified by GWAS often exhibit pleiotropy and are susceptible to confounding by population factors, making it difficult to directly infer sex-specific causal effects. Secondly, traditional GWAS mainly focus on statistical associations between genetic variants and phenotypes, lacking insights into the core underlying molecular mechanisms. Mendelian randomization (MR) leverages genetic variation to infer causality, reducing confounding and reverse causality [ 13 ] . The integrated application of MR and transcriptome analysis validates the causal association between genes and phenotypes and helps bridge the gap from correlation to causality, aiding in the clarification of asthma mechanisms. For instance, researchers have identified 485 blood eQTL-regulated genes related to asthma, 50 of which are causal [ 14 ] . CEP95, RBM6, and ITPKB reduce asthma risk, whereas elevated expression of HOXB-AS1, ETS1, and JAK2 increase risk [ 15 ] . HMOX1 may exert protective effects in asthma through its regulatory role in oxidative stress [ 16 ] . However, existing studies have largely neglected sex-related influences. This study integrates MR and transcriptome analysis to uncover key regulatory genes and mechanisms driving childhood asthma sex disparities, informing sex-specific diagnostics and precision therapies. Materials and Methods 1 Data sources Two pediatric asthma datasets (GSE27011 and GSE203482) were retrieved from the NCBI GEO database [ 17 ] ( http://www.ncbi.nlm.nih.gov/geo/ ) for this analysis. Specifically, GSE27011 comprised 36 asthma patients and 18 normal controls (17 females and 37 males), while GSE203482 included 184 male asthma patients and 137 female asthma patients. For data processing, preprocessed expression data were first downloaded. Probe annotation was then performed using the platform annotation file, followed by the removal of probes that failed to match any Gene Symbol. For probes mapping to the same gene, the mean expression value of these probes was calculated and used as the final expression value for that gene. We obtained GWAS data serving as outcome events and exposures for MR analysis. The IEU OpenGWAS database ( https://gwas.mrcieu.ac.uk ) was accessed for peripheral blood eQTL data related to childhood asthma. The sample size consisted of 194,174 female children with asthma and 167,020 male children with asthma, and the ethnic information was European. Given that all data utilized in this research are openly accessible and freely available for download, no additional ethical approval was required. 2 Identification of differentially expressed genes (DEGs) To identify DEGs between asthma and control groups, differential expression analysis was conducted using the limma R package [ 18 ] , with a significance threshold set at p < 0.05. Specifically, we separately screened DEGs for the following comparisons: female asthma patients vs. female controls (DEG1, derived from GSE27011), male asthma patients vs. male controls (DEG2, derived from GSE27011), and asthma patients vs. controls (DEG3, derived from GSE27011). Additionally, DEG4 (female asthma patients vs. male asthma patients) was derived from GSE203482. To obtain sex-specific DEGs, we intersected DEG1, DEG3, and DEG4 (requiring consistent expression trends between DEG1 and DEG3; trends between female and male asthma patients were not required to align). Similarly, we intersected DEG2, DEG3, and DEG4 (requiring consistent expression trends between DEG2 and DEG3; trends between female and male asthma patients were not required to align). The final intersections yielded female-specific DEGs (female asthma vs. normal) and male-specific DEGs (male asthma vs. normal). 3 Enrichment analysis To investigate the biological pathways and functions potentially implicated in female-DEGs and male-DEGs, we employed the clusterProfiler [ 19 ] R package to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. GO terms are categorized into biological process (BP), cellular component (CC), and molecular function (MF) terms. KEGG pathway analysis annotates the pathways of identified DEGs. Enrichment results with a P value < 0.05 were considered statistically significant. 4 Selection of instrumental variables (IVs) In the MR data processing, we applied the following filtering criteria: (a) SNPs with p -value 10 were retained F = R 2 * (N-2)༏(1-R 2 ), N is the exposure sample size and R is obtained using “get_r_from_bsen” in TwoSampleMR [ 21 ] ; (c) Loci with minor allele frequency (MAF) > 1% were excluded; (d) Linkage disequilibrium loci were pruned using clump_data (kb = 1000, r2 = 0.01) [ 22 ] ; (e) Exposure and outcome loci were harmonized by Harmonize to align allele coding, and palindromic SNPs with moderate allele frequency were removed [ 23 ] . 5 Mendel randomization (MR) analysis MR analysis was performed using the TwoSampleMR package [ 24 ] . Five two-sample MR estimation methods were applied, including inverse-variance weighted (IVW), MR-Egger regression, weighted median, simple mode, and weighted mode. To enhance the identification of genuine causal genes, we set the following criteria: genes were required to exhibit significant associations in the IVW method ( P -value < 0.05) with consistent effect directions across all methods. Furthermore, we utilized the MR Steiger test to validate that each IV had stronger variance explanatory power for the exposure than for the outcome [ 25 ] . 6 Heterogeneity and Pleiotropy Testing Following MR analysis, heterogeneity was assessed using MR-Egger regression and IVW. A P -value > 0.05 indicated absence of significant heterogeneity. Potential pleiotropy was further evaluated using MR-Egger regression and MR-PRESSO [ 26 ] . An intercept term close to 0 with a P -value > 0.05 was interpreted as no evidence of horizontal pleiotropy. 7 ROC analysis of candidate genes Genes exhibiting consistent directional trends between transcriptomic expression and MR-derived ORs were retained as candidate causal genes. Subsequently, the ROC curve analysis implemented in the pROC package [ 27 ] was used to evaluate the diagnostic performance of individual genes. The area under the curve (AUC) was used to assess the classification ability of these genes, and those with a p-value 0.7 were selected for subsequent analyses. 8 Gene Set Enrichment Analysis (GSEA) We utilized the R package clusterProfiler [ 19 ] and the MSigDB database [ 28 ] to perform GSEA on male-specific and female-specific DEGs. Pathways were considered significantly enriched when |NES| >1 and adjusted P-value < 0.05. 9 Gene Set Variation Analysis (GSVA) We employed the R package GSVA [ 29 ] and the MSigDB database [ 28 ] to conduct GSVA analysis of sex-specific genes. The alterations in biological processes associated with asthma were found. Diverging bar plots were generated to visualize pathway-specific changes. 10 Immune infiltration analysis To investigate the correlation between core genes and 22 immune cell types, we used the CIBERSORT algorithm [ 30 ] for immune infiltration analysis. Wilcoxon rank sum tests were applied to compare immune infiltration differences between asthma and normal samples within each sex (male and female). Spearman’s method was used to calculate correlation coefficients and P -values between core genes and each immune cell type. We generated a correlation heatmap to visualize these associations. 11 GeneMANIA Analysis We utilized the GeneMANIA database [ 31 ] to construct a GeneMANIA network. The relationship between core genes and the top 20 interacting genes were explored. 12 Correlation between core genes and immune-related genes The relationship between core genes expression and immune-related genes was systematically examined. Immune-related genes were extracted, including immune activation genes, chemokines, chemokine receptors, and MHC genes. The resulting heatmap visualizes the co-expression patterns. 13 Drug target prediction and molecular docking Based on the core genes, potential drug molecules were predicted using the DGIdb database [ 32 ] , and the identified candidate drugs represent commonly used therapeutic agents. Molecular docking analysis of the interactions between the drugs and core genes was performed using AutoDock software [ 33 ] . The crystal structures of the core gene proteins were downloaded from the Protein Data Bank database ( http://www.rcsb.org , PDB). The molecular structures of the small molecules in SDF format were obtained from the PubChem Compound database via the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) [ 34 ] . The molecular docking results were visualized using Discovery Studio and PyMOL software ( http://www.pymol.org ). Results 1 Identification of DEGs In GSE27011: 699 upregulated and 712 downregulated DEGs were identified in female asthma patients versus female controls (DEG1); 771 upregulated and 577 downregulated DEGs were detected in male asthma patients versus male controls (DEG2); 969 upregulated and 765 downregulated DEGs were found in asthma patients versus normal controls (DEG3); in GSE203482, 760 upregulated and 636 downregulated DEGs were identified in female asthma patients versus male asthma patients (DEG4). These intersections ultimately yielded 74 female asthma-specific DEGs (female-DEGs) and 112 male asthma-specific DEGs (male-DEGs) (Fig. 1 A-B, Supplementary Figure S1 ). 2 Functional and pathway enrichment analyses of sex-specific DEGs Female-specific DEGs were significantly enriched in BP such as leukocyte mediated immunity, lymphocyte mediated immunity and natural killer cell mediated immunity, highlighting the genes’ roles in regulating immune defense. In terms of CC, we observed significant enrichment in structures such as cytolytic granule and immunological synapse, suggesting these genes’ subcellular location. The MF enrichment analysis revealed that these genes contributed to MF such as immune receptor activity, emphasizing their importance in immune regulation (Fig. 2 A). For male-specific DEGs, BP analysis revealed significant enrichment of leukocyte-mediated immunity, lymphocyte-mediated immunity, and T cell-mediated cytotoxicity. In the CC category, organelle outer membrane and outer membrane exhibited significant enrichment. In terms of MF, immune receptor activity and growth factor binding were significantly enriched (Fig. 2 B). The KEGG pathway analysis revealed that female-specific DEGs were enriched in natural killer (NK) cell-mediated cytotoxicity, graft-versus-host disease, and antigen processing and presentation (Fig. 2 C), while male-specific DEGs showed similar enrichment in these pathways (Fig. 2 D). Overall, the functional and pathway enrichments of sex-specific DEGs were mainly related to cytokine responses. 3 MR analysis Female-DEGs and male-DEGs were respectively used as exposure factors. All SNPs employed had an F-statistic greater than 10, indicating effective avoidance of bias introduced by weak instrumental variables, and thus were used as genetic instrumental variables for MR analysis. Ultimately, 5 causal genes between female-DEGs and female asthma, and 3 causal genes between male-DEGs and male asthma were identified. The upregulated genes GNG2, HAVCR2, NMUR1, and KRT73 displayed significant positive causal associations with asthma, contributing to an elevated risk of asthma with an odds ratio (OR) > 1. Conversely, the three downregulated genes AUTS2, DTHD1, and CCN1 were associated with a reduced risk of asthma (OR 0.05, Supplementary Table S1 ). Pleiotropy tests (MR-Egger and MR-PRESSO) showed no horizontal pleiotropy ( p > 0.05, Supplementary Table S2). Based on the Steiger test, each genetic instrumental variable exhibited a significantly higher variance explanatory power for the exposure than for the outcome (Supplementary Table S3). 4 ROC analysis of candidate genes After retaining DEGs with consistent expression trends and confirmed causal relationships, we identified 3 female-specific candidate DEGs (GNG2, HAVCR2, NMUR1) and 3 male-specific candidate DEGs (CCNE1, KRT73, NMUR1). Notably, NMUR1 was identified as a common candidate gene in both sexes. ROC analysis revealed favorable predictive performance for each gene, with AUC values exceeding 0.7 in the GSE27011 dataset. Especially for girls, the AUC values for GNG2 and NMUR1 surpassed 0.9, indicating a robust capacity to differentiate asthma patients from healthy controls (Fig. 4 A-B). 5 GSEA We performed GSEA enrichment analysis of the differential genes for male and female samples. The KEGG pathways were mainly enriched in asthma, antigen processing and presentation, and natural killer cell mediated cytotoxicity. In female samples, For GNG2, Natural Killer Cell Mediated Cytotoxicity, Parkinsons Disease and Proteasome were activated. Asthma and Taste Transduction were inhibited. For HAVCR2, Natural Killer Cell Mediated Cytotoxicity, Toll Like Receptor Signaling Pathway and Lysosome were activated. Taste Transduction and Intestinal Immune Network For Iga Production were inhibited. For NMUR1, Parkinsons Disease, Alzheimers Disease and Oxidative Phosphorylation were activated. Hematopoietic Cell Lineage and Taste Transduction were inhibited. In male samples, For CCNE1, Cell Cycle and Spliceosome were activated. Leishmania Infection, Graft Versus Host Disease and Antigen Processing And Presentation were inhibited. For KRT73, Neuroactive Ligand Receptor Interaction and Basal Cell Carcinoma were activated. Spliceosome, Cell Cycle and Mismatch Repair were inhibited. For NMUR1, Natural Killer Cell Mediated Cytotoxicity, Cell Cycle and Valine Leucine And Isoleucine Degradation were activated. Olfactory Transduction, Taste Transduction and Basal Cell Carcinoma were inhibited (Supplementary Figure S2). 6 GSVA Differential expression was assessed between male patients versus controls and female patients versus controls at the gene-set level, retaining biological functions with P < 0.05. For male asthma, five terms were significantly upregulated (most notably Gobp Regulation_of_complement_dependent_cytotoxicity), and five terms were significantly downregulated (Gobp Sensory perception of taste being the most). In female asthma, five terms were significantly upregulated (most notably positive regulation of cd4-positive, alpha-beta T cell differentiation), and five terms were significantly downregulated (Gobp Spliceosomal tri snrnp complex assembly being the most ) (Supplementary Figure S3). 7 Immune infiltration analysis In female asthma patients, resting NK cells were elevated, whereas in male asthma patients, resting NK cells, M0 macrophages, and resting mast cells were increased. Core genes demonstrated significant correlations with immune cell subsets. For male-DEGs, NMUR1 positively correlated with resting NK cells and monocytes, but negatively with naive B cells and naive CD4 T cells; CCNE1 showed positive correlations with naive B cells and CD8 T cells, but negative correlations with resting NK cells and M0 macrophages; KRT73 positively correlated with resting mast cells and M0 macrophages, but negatively with naive B cells and activated dendritic cells. For female-DEGs, NMUR1 was positively associated with resting NK cells and Tregs, but negatively with activated dendritic cells and naive CD4 T cells; GNG2 positively correlated with resting NK cells and M0 macrophages, but negatively with activated CD4 memory T cells and neutrophils; HAVCR2 showed positive correlations with resting NK cells and Tregs, but negative correlations with activated CD4 memory T cells and naive CD4 T cells (Fig. 5 A-J). 8 GeneMANIA The co-expression network of sex-specific genes was constructed using the GeneMANIA website. In the complex GeneMANIA network, co-expression interactions accounted for 8.01%, physical interactions for 77.64%, and colocalization for 3.63% (Fig. 6 ). 9 Correlation between core gene and immune-related genes Significant differences in the correlation between sex-specific genes and immune-related genes were observed: male-specific genes exhibit stronger associations with immune stimulatory factors, whereas female-specific genes show more prominent correlations with immune inhibitory factors and receptors (Supplementary Figure S4). 10 Drug target prediction and molecular docking In the DSigDB database, common drug molecules were identified for five core genes; Three genes (NMUR1, CCNE1, and HAVCR2) had associated drugs that could be predicted (Supplementary Figure S5). Notably, no known structures were found for the drugs related to HAVCR2. Therefore, molecular docking was performed on the highest-scoring drug-gene pairs, namely CCNE1-CDK INHIBITOR SNS-032 and NMUR1-COMPOUND 5D. The molecular docking results for the featured genes and their corresponding drugs consistently demonstrated low binding energies, with all values being less than zero (Supplementary Figure S6). Discussion We initially applied MR analysis and transcriptome data analysis to identify sex-specific differentially expressed genes in childhood asthma. We found that GNG2, HAVCR2, and NMUR1 were differentially expressed in females, while CCNE1, KRT73, and NMUR1 were differentially expressed in males. Notably, NMUR1 was shared across sexes, suggesting a conserved role in asthma pathogenesis. To elucidate the biological roles of these candidate genes, we conducted enrichment analysis, immune infiltration analysis and GeneMANIA network analysis. We also performed drug prediction and molecular docking for the predicted drugs targeting these genes, further supporting their druggability. These findings highlight potential sex-specific molecular drivers of immune dysregulation, and support sex-tailored therapeutic strategies. Neuromedin U receptor 1 (NMUR1) is a G protein-coupled receptor that primarily mediates neuromedin U (NMU) signaling and serves as a critical mediator in neuro-immune crosstalk [ 35 ] . NMUR1 is highly expressed in mast cells, eosinophils and group 2 innate lymphoid cells (ILC2) [ 36 ] . In asthma patients, sputum NMUR1⁺ ILC2s, which express high levels of IL-5 and IL-13, are more abundant in mild cases and inversely correlated with disease severity and ICS dose. This indicates NMUR1 pathway activity may help classify asthma [ 37 ] . NMUR1 may be a key molecule for the aggravation of asthma induced by viral infection in the mouse model [ 38 ] . Our results reveal that NMUR1 has dual associations, suggesting that while this pathway is universally relevant, its activation thresholds or downstream effects may differ between sexes. Interestingly, COMPOUND 5D which has been recognized as a potent hexapeptide agonist for NMUR1 [ 39 ] , exhibited a favorable docking score in our study, offering insights into binding mechanisms. G-protein gamma subunit 2 (GNG2) is a subunit of the Gβγ dimer and binds to the Gα subunit to form the trimeric G protein [ 40 ] . Trimeric G proteins mediate processes such as cell proliferation, cell differentiation, and angiogenesis [ 41 ] . In the rabbit model of allergic asthma, Gβγ upregulates the activity of PDE4 by activating the c-Src-ERK1/2 pathway, leading to airway hyperresponsiveness and pulmonary inflammation [ 42 ] . In human asthma airway smooth muscle cells, the G protein βγ subunit mediates the Ras/MEK/ERK cascade to upregulate the activity of PDE4 [ 43 ] . Our research further supports the role of Gβγ dimer in asthma. Moreover, the anti-inflammatory effect of the G protein-coupled estrogen receptor (GPER) -specific agonist was shown in the chronic ovalbumin-sensitized asthma model, suggesting that GPER may be a therapeutic target [ 44 ] . This finding may explain its female-specific association and reveal a novel mechanism underlying sex hormone-immune crosstalk in asthma. Hepatitis A Virus Cellular Receptor 2 (HAVCR2), also known as T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), is mainly involved in the regulation of immune responses [ 45 ] . The 574T > G gene polymorphism of TIM-3 is significantly associated with the risk of asthma [ 46 ] . TIM-3 knockout in LPS lung models links it to galectin-3, hinting at asthma treatment potential [ 47 ] . The upregulation of Tim-3 on CD4(+) T cells in patients with allergic asthma is associated with Th1/Th2 imbalance [ 48 ] . However, these studies lack sex stratification. Our study discovered its female-specific association, highlighting the importance of considering sex factors in the research on the immune checkpoint mechanism of asthma. Cyclin E1 (CCNE1) is an important cell cycle regulatory protein, that binds to and activates the expression of cyclin-dependent protein kinase 2, participating in the transition process from the G1 phase to the S phase of the cell cycle and playing a pro-cancer role in various tumors [ 49 ] . Its role in asthma pathogenesis remains unexplored and necessitates further investigation. We prove that CCNE1 targeted by CDK INHIBITOR SNS-032 with a good docking score. CDK INHIBITOR SNS-032, a known CDK inhibitor, exhibits significant anti-tumor activity across multiple cancer models [ 50 ] . Our results support its potential for clinical translation and further elucidate its binding mechanisms. Keratin 73 (KRT73) is a type II keratin protein that is specifically expressed in the inner root sheath cuticle [ 51 ] . We speculate that KRT73 may serve as a novel epithelial dysfunction-associated factor in male asthma susceptibility. Abnormal proliferation and activation of immune cells are considered to be the key to the pathogenesis of asthma [ 52 ] . Our study reveals distinct immune cell profiles in pediatric asthma across different sexes. Resting NK cells are significantly increased, with a more pronounced increase in female than in male patients. Resting NK cells can be potently activated by dendritic cells [ 53 ] . Similar results are observed in the sexual dimorphism of skin immunity, which is mediated by an androgen-ILC2-dendritic cell regulatory axis [ 54 ] . Androgens modulate sex-biased immune gene expression and immune cell populations [ 55 ] . Moreover, differential correlations between target genes and immune cells exist across sex subgroups. These findings highlight the need to investigate sex-specific immune cell dynamics in pediatric asthma to clarify pathogenesis and guide precision therapy. Despite comprehensive exploration of sex-specific childhood asthma molecular mechanisms and identification of potential biomarkers, limitations persist. Firstly, the transcriptome analysis was conducted on a relatively small sample size. Secondly, MR analyses primarily utilized genetic data from European populations (GWAS database), limiting cross-ancestry generalizability. Thirdly, the expression of candidate genes was not measured in animal models or cell models. Future work will involve a larger sample size to validate the candidate genes and underlying mechanism. Multi-lineage GWAS integration will be performed to enhance universality. The functional impact of the candidate genes will be confirmed through rigorous in vitro or in vivo experiments. In conclusion, the integration of MR and transcriptome analysis identified sex-specific causal genes in childhood asthma. These results provide novel insights into the sex-specific pathogenesis of childhood asthma and lay a foundation for future precision medicine approaches. Declarations Author contributions : Siyuan JIa: Conceptualization, Writing–original draft,Writing–review and editing,Formal Analysis,Methodology.Shuyu Chen:Formal Analysis,Methodology, Writing–original draft.Haiyan Zhu:Formal Analysis,Methodology,Funding.Xiaohong Dai:Writing–original draft.Huifang Wang:Writing–original draft,Conceptualization, Methodology.Yu Chen:Conceptualization,Methodology, Writing – original draft. Qianqian Yu:Methodology,Writing–original draft,Data curation.Rongrong Zhang:Conceptualization,Software, Supervision,Validation,Writing – review and editing. Zhaofang Tian:Conceptualization,Writing – review and editing, Funding acquisition,Writing – original draft. Funding The author(s) declare that financial support was received for the research and/or publication of this article. This project was supported by The Affiliated Huaian No. 1 People’sHospital of Nanjing Medical University “Research and Innovation Team”project (YCT202301) ，Jiangsu Provincial Maternal and Child Health Research Project（F202306）. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Generative AI statement The author(s) declare that no Generative AI was used in the creation of this manuscript. Data Availability Statement The data supporting the findings of this study are included in the supplementary materials of this article and are also available from the corresponding author upon reasonable request. References MARTIN J, TOWNSHEND J. Diagnosis and management of asthma in children[J]. BMJ paediatrics open. 2022;6(1):e001277. 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TIM-3 on myeloid cells promotes pulmonary inflammation through increased production of galectin-3[J]. Commun Biology. 2024;7(1):1090. TANG F, WANG F. Upregulation of Tim-3 on CD4(+) T cells is associated with Th1/Th2 imbalance in patients with allergic asthma[J]. Int J Clin Exp Med. 2015;8(3):3809–16. DAI S, LI L, GUO G, et al. CCNE1 stabilizes ANLN by counteracting FZR1-mediated the ubiquitination modification to promotes triple negative breast cancer cell stemness and progression[J]. Cell Death Discovery. 2025;11(1):228. MORALES F. Overview of CDK9 as a target in cancer research[J]. Cell Cycle. 2016;15(4):519–27. PORTER RM, CORDEN L D, LUNNY D P, et al. Keratin K6irs is specific to the inner root sheath of hair follicles in mice and humans[J]. Br J Dermatol. 2001;145(4):558–68. HAMMAD H, LAMBRECHT B N. The basic immunology of asthma[J]. Cell. 2021;184(6):1469–85. FERLAZZO G, TSANG M L, MORETTA L, et al. Human dendritic cells activate resting natural killer (NK) cells and are recognized via the NKp30 receptor by activated NK cells[J]. J Exp Med. 2002;195(3):343–51. CHI L, LIU C, GRIBONIKA I, et al. Sexual dimorphism in skin immunity is mediated by an androgen-ILC2-dendritic cell axis[J]. Volume 384. Science; 2024. p. eadk6200. (New York, N.Y.). 6692. LI F, XING X. Sex differences orchestrated by androgens at single-cell resolution[J]. Nature. 2024;629(8010):193–200. Additional Declarations No competing interests reported. Supplementary Files Supplementary.zip Supplementary Figure S1. Volcano plot of DEGs. (A) asthma patients versus normal controls in GSE27011. (B) female asthma patients versus male asthma patients in GSE203482. (C) male asthma patients versus male controls in GSE27011. (D) female asthma patients versus female controls in GSE27011. Red dots denote upregulated genes, and blue dots represent downregulated genes. Supplementary Figure S2. KEGG signaling pathways significantly enriched by GSEA.(A) GNG2-activating pathway. (B) GNG2-inhibitory pathway. (C) HAVCR2-activating pathway. (D) HAVCR2-inhibitory pathway. (E) female-NMUR1-activating pathway. (F) female-NMUR1-inhibitory pathway. (G) CCNE1-activating pathway. (H) CCNE1-inhibitory pathway. (I) KRT73-activating pathway. (J) KRT73-inhibitory pathway. (K) male-NMUR1-activating pathway. (L) male-NMUR1-inhibitory pathway. Supplementary Figure S3. GO BP significantly enriched by GSVA. Blue bars represent positive t-values, green bars represent negative t-values. The greater the absolute t-value, the more significant the enrichment. (A-male, B-female). Supplementary Figure S4. Heatmap of sex-specific gene expression and immune-related genes. Red represents up-regulation, blue represents down-regulation. Statistical significance is denoted as follows: *p < 0.05 and **p < 0.01. (A-male, B-female). Supplementary Figure S5. Drug target prediction. Orange circular nodes represent genes, and purple square nodes represent drugs. Supplementary Figure S6. Molecular docking. (A) Global view of COMPOUND 5D bound to NMUR1. (B) Local view of the docking interaction between COMPOUND 5D and NMUR1. (C) Global view of SNS-032 bound to CCNE1. (D) Local view of the docking interaction between SNS-032 and CCNE1. Supplementary Table S1 Heterogeneity test for causal sex-specific DEGs Supplementary Table S2Pleiotropy test for causal sex-specific DEGs Supplementary Table S3Steiger test for causal sex-specific DEGs Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7547685","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511009806,"identity":"d6d1960a-996f-41b4-81aa-187b881db4d5","order_by":0,"name":"Yi’an Jia","email":"","orcid":"","institution":"The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi’an","middleName":"","lastName":"Jia","suffix":""},{"id":511009807,"identity":"f49fb5f7-59c7-44ec-8189-af081219bc6f","order_by":1,"name":"Shuyu Chen","email":"","orcid":"","institution":"The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuyu","middleName":"","lastName":"Chen","suffix":""},{"id":511009808,"identity":"ba3eb83f-0698-42fd-8778-235e4e83576d","order_by":2,"name":"Haiyan Zhu","email":"","orcid":"","institution":"The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Zhu","suffix":""},{"id":511009809,"identity":"21d53655-9243-4e78-9054-4ed9b2ab6871","order_by":3,"name":"Xiaohong Dai","email":"","orcid":"","institution":"Huai´an Industrial Park People´s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Dai","suffix":""},{"id":511009810,"identity":"c3b7c0ed-45e2-4b01-9d79-2d349abfa4d2","order_by":4,"name":"Huifang Wang","email":"","orcid":"","institution":"The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huifang","middleName":"","lastName":"Wang","suffix":""},{"id":511009811,"identity":"799afed4-534f-4e58-b29b-fdcb05066587","order_by":5,"name":"Yu Chen","email":"","orcid":"","institution":"The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Chen","suffix":""},{"id":511009812,"identity":"b77126e7-eed8-48f6-b623-9feb24d81aa1","order_by":6,"name":"Qianqian Yu","email":"","orcid":"","institution":"The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Yu","suffix":""},{"id":511009813,"identity":"e51df98e-89be-45fe-af08-2d3518d56cf6","order_by":7,"name":"Rongrong Zhang","email":"","orcid":"","institution":"The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rongrong","middleName":"","lastName":"Zhang","suffix":""},{"id":511009814,"identity":"357784cf-8de0-4659-b5ed-26b8a5d69343","order_by":8,"name":"Zhaofang Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACPgYehsN/Kmzk2NjbDxCnhY2Bh/EBz5k0Yz6eMwlEa2E24G07nDhPwsGASC0SucckJNgOp7dJMCQw/KjYRoyWvDQJA5703DbpxgOMPWduE6Mlx0wiQcI6t03mQAIzYxuxWg4YMKezSSQYEK3F2LAhwTmBBC08bwwfMxxIM2wDBvJBovzCz55jcJjxn428fHv7wQc/KojQwiCQgGAfIEI9yBoi1Y2CUTAKRsEIBgADrTa70PgieAAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhaofang","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2025-09-06 02:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7547685/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7547685/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90895600,"identity":"7678c12b-a4ea-4c5c-9644-628be9dc4c93","added_by":"auto","created_at":"2025-09-09 11:33:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86851,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUpset plot for sex-specific DEGs in childhood asthma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHorizontal bar on left represents the size of each intersection. Matrix represents the intersection relationships among various sets, each row corresponds to a set, and each column represents an intersection. Vertical histogram represents number of set size in each subset. A-female, B-male.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7547685/v1/31fc02cfb8638c88fcb97faf.png"},{"id":90897810,"identity":"e51bd38f-a708-4e12-8c20-cea1cf39e716","added_by":"auto","created_at":"2025-09-09 11:49:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":358862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of sex-specific DEGs in childhood asthma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) GO enrichment analysis of female-DEG. (B) GO enrichment analysis of male-DEG. (C) KEGG pathway enrichment of female-DEG. (D) KEGG pathway enrichment of male-DEG. Nodes represent the number of enriched genes, and colors indicate upregulation and downregulation status.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7547685/v1/ba484d79246c43b206b154d0.png"},{"id":90897365,"identity":"3c4c2919-2f65-488e-ba0d-4fc64bcfd71b","added_by":"auto","created_at":"2025-09-09 11:41:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMendelian randomization results.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Female-specific causal genes. (B) Male-specific causal genes. Columns are annotated as follows: Column 1: Exposure factor; Column 2: Outcome factor; Column 3: Number of SNPs; Column 4: Algorithm; Column 5: OR value and its corresponding confidence interval visualization; Column 6: OR value and its 95% confidence interval (lower and upper bounds); Column 7: P-value. OR (odds ratio) serves as the risk ratio: an OR \u0026gt; 1 indicates a risk factor, whereas an OR \u0026lt; 1 indicates a protective factor.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7547685/v1/685c2aadd99f3f6367bd610b.png"},{"id":90895612,"identity":"65c0bed1-bac0-4815-8801-dab0f8cbbc54","added_by":"auto","created_at":"2025-09-09 11:33:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves for core gene diagnostic efficacy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Female core genes (GNG2, HAVCR2, NMUR1). (B) Male core genes (CCNE1, KRT73, NMUR1). The x-axis represents specificity, the y-axis represents sensitivity, and AUC values are indicated in the legend, illustrating the diagnostic performance of the genes.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7547685/v1/77128e6b4b44b71331892995.png"},{"id":90897814,"identity":"f4331204-5f11-4ec5-8a64-9335d0f49a27","added_by":"auto","created_at":"2025-09-09 11:49:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":548895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunoinfiltration analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Differentially expressed immune cells between male asthma and healthy. (B) Correlation analysis between three male-DEG genes and immune cells. (C) Lollipop plot showing the correlation between male-specific NMUR1 and immune cells. (D) Lollipop plot showing the correlation between CCNE1 and immune cells. (E) Lollipop plot showing the correlation between KRT73 and immune cells. (F) Differentially expressed immune cells between female asthma and healthy. (G)\u003cstrong\u003e \u003c/strong\u003eCorrelation analysis between three female-DEG genes and immune cells. (H) Lollipop plot showing the correlation between female-specific NMUR1 and immune cells. (I) Lollipop plot showing the correlation between GNG2and immune cells. (J) Lollipop plot showing the correlation between HAVCR2and immune cells. Statistical significance is denoted as follows: *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. In the lollipop charts, the x-axis represents the correlation coefficient, with circle size indicating the absolute value of the correlation coefficient and color denoting significance. Darker red reflects smaller \u003cem\u003ep\u003c/em\u003e-values.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7547685/v1/ff6bb1455f77f10eb23c0b9b.png"},{"id":90899096,"identity":"de75cca1-f341-48e8-acf4-b9a96c42539b","added_by":"auto","created_at":"2025-09-09 11:57:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":106905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneMANIA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe twenty functionally similar genes were located in the outer circle, and the five hub genes were located in the inner circle. The color of nodes was related to the protein function, while line color represented the type of protein interaction.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7547685/v1/ad2af17f8c1f7d17d1d2adb7.png"},{"id":97665884,"identity":"e0450d22-bd3c-4297-b3d8-9bef1f3780e7","added_by":"auto","created_at":"2025-12-08 09:19:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2362757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7547685/v1/28f976da-2d5b-43ae-a42a-c032d389bb7b.pdf"},{"id":90897386,"identity":"d895bd6f-a82c-4e05-bd32-b45267710988","added_by":"auto","created_at":"2025-09-09 11:41:21","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17799965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. \u003c/strong\u003eVolcano plot of DEGs. (A) asthma patients versus normal controls in GSE27011. (B) female asthma patients versus male asthma patients in GSE203482. (C) male asthma patients versus male controls in GSE27011. (D) female asthma patients versus female controls in GSE27011. Red dots denote upregulated genes, and blue dots represent downregulated genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure S2\u003c/strong\u003e. KEGG signaling pathways significantly enriched by GSEA.(A) GNG2-activating pathway. (B) GNG2-inhibitory pathway. (C) HAVCR2-activating pathway. (D) HAVCR2-inhibitory pathway. (E) female-NMUR1-activating pathway. (F) female-NMUR1-inhibitory pathway. (G) CCNE1-activating pathway. (H) CCNE1-inhibitory pathway. (I) KRT73-activating pathway. (J) KRT73-inhibitory pathway. (K) male-NMUR1-activating pathway. (L) male-NMUR1-inhibitory pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure S3\u003c/strong\u003e. GO BP significantly enriched by GSVA. Blue bars represent positive t-values, green bars represent negative t-values. The greater the absolute t-value, the more significant the enrichment. (A-male, B-female).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure S4\u003c/strong\u003e. Heatmap of sex-specific gene expression and immune-related genes. Red represents up-regulation, blue represents down-regulation. Statistical significance is denoted as follows: *p \u0026lt; 0.05 and **p \u0026lt; 0.01. (A-male, B-female).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure S5\u003c/strong\u003e. Drug target prediction. Orange circular nodes represent genes, and purple square nodes represent drugs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure S6. \u003c/strong\u003eMolecular docking. (A) Global view of COMPOUND 5D bound to NMUR1. (B) Local view of the docking interaction between COMPOUND 5D and NMUR1. (C) Global view of SNS-032 bound to CCNE1. (D) Local view of the docking interaction between SNS-032 and CCNE1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S1 \u003c/strong\u003eHeterogeneity test for causal sex-specific DEGs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S2\u003c/strong\u003ePleiotropy test for causal sex-specific DEGs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S3\u003c/strong\u003eSteiger test for causal sex-specific DEGs\u003c/p\u003e","description":"","filename":"Supplementary.zip","url":"https://assets-eu.researchsquare.com/files/rs-7547685/v1/c42d2c3608b7daa79deee523.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Sex-Specific Candidate Genes Underlying Childhood Asthma Using Integrative Transcriptomic and Mendelian Randomization Approaches","fulltext":[{"header":"Key Message","content":"\u003cp\u003eThis study identifies sex-specific causal genes underlying childhood asthma disparities through integrated transcriptomic and Mendelian Randomization (MR) analysis. Key findings include: Female-specific genes: GNG2 and NMUR1 drive asthma risk via immune regulation, While Male-specific genes as CCNE1 and KRT73 associate with airway remodeling. Shared gene: NMUR1 mediates neuro-immune interactions in both sexes.\u0026nbsp;\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAsthma is a heterogeneous disease characterized by chronic airway inflammation involving the synergistic participation of multiple inflammatory cells and cytokines, accompanied by symptoms such as wheezing, shortness of breath, coughing and chest tightness\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. There are nearly 300\u0026nbsp;million asthma patients worldwide\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e; the overall prevalence rate of childhood asthma worldwide has reached 10.2%\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. As the most common chronic respiratory disease in childhood, asthma recurs frequently and is difficult to cure. It harms children's health and burdens families and society economically, with children from disadvantaged backgrounds being particularly affected\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe heterogeneity of asthma is also reflected in sex and age. In adults, asthma is more prevalent in women than in men and is more likely to progress to severe asthma\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In children, asthma prevalence is higher in boys than in girls prior to puberty; this sex disparity reverses during adulthood, with both asthma prevalence and severity displaying a marked upsurge in females\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Boys receive an asthma diagnosis at a younger age compared to girls, and show elevated peripheral blood eosinophil levels and serum periostin\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Boys are more likely to have T2-high asthma, characterized by type 2 inflammation, while girls more frequently present with T2-low asthma, which is non-type 2 and often associated with overweight or obesity\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Previous studies show that sex disparities in asthma are related to distinct genetic factors, immune dynamics, sex hormones, and environment-specific responses\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, mere recognition of sex-based disparities in asthma is inadequate, and causal inference is essential in the precision medicine era. Moreover, clinically actionable biomarkers for personalized asthma care are still limited.\u003c/p\u003e\u003cp\u003eGenetic advancements have deepened our understanding of asthma\u0026rsquo;s molecular mechanisms and highlighted sex differences in asthma. Genome-wide association studies (GWAS) have identified multiple genetic loci associated with sex interaction effects in childhood asthma. For example, SNP rs1255383 in the 10q11.21 region (near ZNF33B) shows opposing associations with atopic markers between boys and girls\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The GAGC haplotype of the IL-9R confers significant protection against wheezing in boys exposed to inhaled allergens, with no such effect observed in girls\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The heterozygous SNP rs2069727 in IFNG increases asthma risk in boys but decreases it in girls\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Although the aforementioned GWAS have identified numerous sex-related genetic variants, they still have limitations. Firstly, SNPs identified by GWAS often exhibit pleiotropy and are susceptible to confounding by population factors, making it difficult to directly infer sex-specific causal effects. Secondly, traditional GWAS mainly focus on statistical associations between genetic variants and phenotypes, lacking insights into the core underlying molecular mechanisms.\u003c/p\u003e\u003cp\u003eMendelian randomization (MR) leverages genetic variation to infer causality, reducing confounding and reverse causality\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The integrated application of MR and transcriptome analysis validates the causal association between genes and phenotypes and helps bridge the gap from correlation to causality, aiding in the clarification of asthma mechanisms. For instance, researchers have identified 485 blood eQTL-regulated genes related to asthma, 50 of which are causal\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. CEP95, RBM6, and ITPKB reduce asthma risk, whereas elevated expression of HOXB-AS1, ETS1, and JAK2 increase risk\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. HMOX1 may exert protective effects in asthma through its regulatory role in oxidative stress\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, existing studies have largely neglected sex-related influences.\u003c/p\u003e\u003cp\u003eThis study integrates MR and transcriptome analysis to uncover key regulatory genes and mechanisms driving childhood asthma sex disparities, informing sex-specific diagnostics and precision therapies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch3\u003e1 Data sources\u003c/h3\u003e\n\u003cp\u003eTwo pediatric asthma datasets (GSE27011 and GSE203482) were retrieved from the NCBI GEO database\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for this analysis. Specifically, GSE27011 comprised 36 asthma patients and 18 normal controls (17 females and 37 males), while GSE203482 included 184 male asthma patients and 137 female asthma patients. For data processing, preprocessed expression data were first downloaded. Probe annotation was then performed using the platform annotation file, followed by the removal of probes that failed to match any Gene Symbol. For probes mapping to the same gene, the mean expression value of these probes was calculated and used as the final expression value for that gene.\u003c/p\u003e\u003cp\u003eWe obtained GWAS data serving as outcome events and exposures for MR analysis. The IEU OpenGWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was accessed for peripheral blood eQTL data related to childhood asthma. The sample size consisted of 194,174 female children with asthma and 167,020 male children with asthma, and the ethnic information was European. Given that all data utilized in this research are openly accessible and freely available for download, no additional ethical approval was required.\u003c/p\u003e\n\u003ch3\u003e2 Identification of differentially expressed genes (DEGs)\u003c/h3\u003e\n\u003cp\u003eTo identify DEGs between asthma and control groups, differential expression analysis was conducted using the limma R package\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, with a significance threshold set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Specifically, we separately screened DEGs for the following comparisons: female asthma patients vs. female controls (DEG1, derived from GSE27011), male asthma patients vs. male controls (DEG2, derived from GSE27011), and asthma patients vs. controls (DEG3, derived from GSE27011). Additionally, DEG4 (female asthma patients vs. male asthma patients) was derived from GSE203482. To obtain sex-specific DEGs, we intersected DEG1, DEG3, and DEG4 (requiring consistent expression trends between DEG1 and DEG3; trends between female and male asthma patients were not required to align). Similarly, we intersected DEG2, DEG3, and DEG4 (requiring consistent expression trends between DEG2 and DEG3; trends between female and male asthma patients were not required to align). The final intersections yielded female-specific DEGs (female asthma vs. normal) and male-specific DEGs (male asthma vs. normal).\u003c/p\u003e\n\u003ch3\u003e3 Enrichment analysis\u003c/h3\u003e\n\u003cp\u003eTo investigate the biological pathways and functions potentially implicated in female-DEGs and male-DEGs, we employed the clusterProfiler \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e R package to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. GO terms are categorized into biological process (BP), cellular component (CC), and molecular function (MF) terms. KEGG pathway analysis annotates the pathways of identified DEGs. Enrichment results with a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\n\u003ch3\u003e4 Selection of instrumental variables (IVs)\u003c/h3\u003e\n\u003cp\u003eIn the MR data processing, we applied the following filtering criteria: (a) SNPs with \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10⁻⁵ were selected as IVs\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e; (b) To mitigate weak instrument bias, variants with an F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 were retained F\u0026thinsp;=\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e*\u003c/sub\u003e(N-2)༏(1-R\u003csup\u003e2\u003c/sup\u003e), N is the exposure sample size and R is obtained using \u0026ldquo;get_r_from_bsen\u0026rdquo; in TwoSampleMR\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e; (c) Loci with minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;1% were excluded; (d) Linkage disequilibrium loci were pruned using clump_data (kb\u0026thinsp;=\u0026thinsp;1000, r2\u0026thinsp;=\u0026thinsp;0.01)\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e; (e) Exposure and outcome loci were harmonized by Harmonize to align allele coding, and palindromic SNPs with moderate allele frequency were removed\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e5 Mendel randomization (MR) analysis\u003c/h3\u003e\n\u003cp\u003eMR analysis was performed using the TwoSampleMR package\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Five two-sample MR estimation methods were applied, including inverse-variance weighted (IVW), MR-Egger regression, weighted median, simple mode, and weighted mode. To enhance the identification of genuine causal genes, we set the following criteria: genes were required to exhibit significant associations in the IVW method (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with consistent effect directions across all methods. Furthermore, we utilized the MR Steiger test to validate that each IV had stronger variance explanatory power for the exposure than for the outcome\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e6 Heterogeneity and Pleiotropy Testing\u003c/h3\u003e\n\u003cp\u003eFollowing MR analysis, heterogeneity was assessed using MR-Egger regression and IVW. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicated absence of significant heterogeneity. Potential pleiotropy was further evaluated using MR-Egger regression and MR-PRESSO\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. An intercept term close to 0 with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 was interpreted as no evidence of horizontal pleiotropy.\u003c/p\u003e\n\u003ch3\u003e7 ROC analysis of candidate genes\u003c/h3\u003e\n\u003cp\u003eGenes exhibiting consistent directional trends between transcriptomic expression and MR-derived ORs were retained as candidate causal genes. Subsequently, the ROC curve analysis implemented in the pROC package\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e was used to evaluate the diagnostic performance of individual genes. The area under the curve (AUC) was used to assess the classification ability of these genes, and those with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 were selected for subsequent analyses.\u003c/p\u003e\n\u003ch3\u003e8 Gene Set Enrichment Analysis (GSEA)\u003c/h3\u003e\n\u003cp\u003eWe utilized the R package clusterProfiler\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e and the MSigDB database\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e to perform GSEA on male-specific and female-specific DEGs. Pathways were considered significantly enriched when |NES| \u0026gt;1 and adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003e9 Gene Set Variation Analysis (GSVA)\u003c/h3\u003e\n\u003cp\u003eWe employed the R package GSVA\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e and the MSigDB database\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e to conduct GSVA analysis of sex-specific genes. The alterations in biological processes associated with asthma were found. Diverging bar plots were generated to visualize pathway-specific changes.\u003c/p\u003e\n\u003ch3\u003e10 Immune infiltration analysis\u003c/h3\u003e\n\u003cp\u003eTo investigate the correlation between core genes and 22 immune cell types, we used the CIBERSORT algorithm\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e for immune infiltration analysis. Wilcoxon rank sum tests were applied to compare immune infiltration differences between asthma and normal samples within each sex (male and female). Spearman\u0026rsquo;s method was used to calculate correlation coefficients and \u003cem\u003eP\u003c/em\u003e-values between core genes and each immune cell type. We generated a correlation heatmap to visualize these associations.\u003c/p\u003e\n\u003ch3\u003e11 GeneMANIA Analysis\u003c/h3\u003e\n\u003cp\u003eWe utilized the GeneMANIA database\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e to construct a GeneMANIA network. The relationship between core genes and the top 20 interacting genes were explored.\u003c/p\u003e\n\u003ch3\u003e12 Correlation between core genes and immune-related genes\u003c/h3\u003e\n\u003cp\u003eThe relationship between core genes expression and immune-related genes was systematically examined. Immune-related genes were extracted, including immune activation genes, chemokines, chemokine receptors, and MHC genes. The resulting heatmap visualizes the co-expression patterns.\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003e13 Drug target prediction and molecular docking\u003c/b\u003e\u003c/div\u003e\u003cp\u003eBased on the core genes, potential drug molecules were predicted using the DGIdb database\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, and the identified candidate drugs represent commonly used therapeutic agents. Molecular docking analysis of the interactions between the drugs and core genes was performed using AutoDock software\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. The crystal structures of the core gene proteins were downloaded from the Protein Data Bank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, PDB). The molecular structures of the small molecules in SDF format were obtained from the PubChem Compound database via the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. The molecular docking results were visualized using Discovery Studio and PyMOL software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pymol.org\u003c/span\u003e\u003cspan address=\"http://www.pymol.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1 Identification of DEGs\u003c/h3\u003e\n\u003cp\u003eIn GSE27011: 699 upregulated and 712 downregulated DEGs were identified in female asthma patients versus female controls (DEG1); 771 upregulated and 577 downregulated DEGs were detected in male asthma patients versus male controls (DEG2); 969 upregulated and 765 downregulated DEGs were found in asthma patients versus normal controls (DEG3); in GSE203482, 760 upregulated and 636 downregulated DEGs were identified in female asthma patients versus male asthma patients (DEG4). These intersections ultimately yielded 74 female asthma-specific DEGs (female-DEGs) and 112 male asthma-specific DEGs (male-DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e2 Functional and pathway enrichment analyses of sex-specific DEGs\u003c/h3\u003e\n\u003cp\u003eFemale-specific DEGs were significantly enriched in BP such as leukocyte mediated immunity, lymphocyte mediated immunity and natural killer cell mediated immunity, highlighting the genes\u0026rsquo; roles in regulating immune defense. In terms of CC, we observed significant enrichment in structures such as cytolytic granule and immunological synapse, suggesting these genes\u0026rsquo; subcellular location. The MF enrichment analysis revealed that these genes contributed to MF such as immune receptor activity, emphasizing their importance in immune regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For male-specific DEGs, BP analysis revealed significant enrichment of leukocyte-mediated immunity, lymphocyte-mediated immunity, and T cell-mediated cytotoxicity. In the CC category, organelle outer membrane and outer membrane exhibited significant enrichment. In terms of MF, immune receptor activity and growth factor binding were significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The KEGG pathway analysis revealed that female-specific DEGs were enriched in natural killer (NK) cell-mediated cytotoxicity, graft-versus-host disease, and antigen processing and presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), while male-specific DEGs showed similar enrichment in these pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Overall, the functional and pathway enrichments of sex-specific DEGs were mainly related to cytokine responses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e3 MR analysis\u003c/h3\u003e\n\u003cp\u003eFemale-DEGs and male-DEGs were respectively used as exposure factors. All SNPs employed had an F-statistic greater than 10, indicating effective avoidance of bias introduced by weak instrumental variables, and thus were used as genetic instrumental variables for MR analysis. Ultimately, 5 causal genes between female-DEGs and female asthma, and 3 causal genes between male-DEGs and male asthma were identified. The upregulated genes GNG2, HAVCR2, NMUR1, and KRT73 displayed significant positive causal associations with asthma, contributing to an elevated risk of asthma with an odds ratio (OR)\u0026thinsp;\u0026gt;\u0026thinsp;1. Conversely, the three downregulated genes AUTS2, DTHD1, and CCN1 were associated with a reduced risk of asthma (OR\u0026thinsp;\u0026lt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). Heterogeneity tests (MR-Egger and IVW) showed no significant heterogeneity (Q_pval\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Pleiotropy tests (MR-Egger and MR-PRESSO) showed no horizontal pleiotropy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplementary Table S2). Based on the Steiger test, each genetic instrumental variable exhibited a significantly higher variance explanatory power for the exposure than for the outcome (Supplementary Table S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e4 ROC analysis of candidate genes\u003c/h3\u003e\n\u003cp\u003eAfter retaining DEGs with consistent expression trends and confirmed causal relationships, we identified 3 female-specific candidate DEGs (GNG2, HAVCR2, NMUR1) and 3 male-specific candidate DEGs (CCNE1, KRT73, NMUR1). Notably, NMUR1 was identified as a common candidate gene in both sexes. ROC analysis revealed favorable predictive performance for each gene, with AUC values exceeding 0.7 in the GSE27011 dataset. Especially for girls, the AUC values for GNG2 and NMUR1 surpassed 0.9, indicating a robust capacity to differentiate asthma patients from healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e5 GSEA\u003c/h3\u003e\n\u003cp\u003eWe performed GSEA enrichment analysis of the differential genes for male and female samples. The KEGG pathways were mainly enriched in asthma, antigen processing and presentation, and natural killer cell mediated cytotoxicity. In female samples, For GNG2, Natural Killer Cell Mediated Cytotoxicity, Parkinsons Disease and Proteasome were activated. Asthma and Taste Transduction were inhibited. For HAVCR2, Natural Killer Cell Mediated Cytotoxicity, Toll Like Receptor Signaling Pathway and Lysosome were activated. Taste Transduction and Intestinal Immune Network For Iga Production were inhibited. For NMUR1, Parkinsons Disease, Alzheimers Disease and Oxidative Phosphorylation were activated. Hematopoietic Cell Lineage and Taste Transduction were inhibited. In male samples, For CCNE1, Cell Cycle and Spliceosome were activated. Leishmania Infection, Graft Versus Host Disease and Antigen Processing And Presentation were inhibited. For KRT73, Neuroactive Ligand Receptor Interaction and Basal Cell Carcinoma were activated. Spliceosome, Cell Cycle and Mismatch Repair were inhibited. For NMUR1, Natural Killer Cell Mediated Cytotoxicity, Cell Cycle and Valine Leucine And Isoleucine Degradation were activated. Olfactory Transduction, Taste Transduction and Basal Cell Carcinoma were inhibited (Supplementary Figure S2).\u003c/p\u003e\n\u003ch3\u003e6 GSVA\u003c/h3\u003e\n\u003cp\u003eDifferential expression was assessed between male patients versus controls and female patients versus controls at the gene-set level, retaining biological functions with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For male asthma, five terms were significantly upregulated (most notably Gobp Regulation_of_complement_dependent_cytotoxicity), and five terms were significantly downregulated (Gobp Sensory perception of taste being the most). In female asthma, five terms were significantly upregulated (most notably positive regulation of cd4-positive, alpha-beta T cell differentiation), and five terms were significantly downregulated (Gobp Spliceosomal tri snrnp complex assembly being the most ) (Supplementary Figure S3).\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003e7 Immune infiltration analysis\u003c/b\u003e\u003c/div\u003e\u003cp\u003eIn female asthma patients, resting NK cells were elevated, whereas in male asthma patients, resting NK cells, M0 macrophages, and resting mast cells were increased. Core genes demonstrated significant correlations with immune cell subsets. For male-DEGs, NMUR1 positively correlated with resting NK cells and monocytes, but negatively with naive B cells and naive CD4 T cells; CCNE1 showed positive correlations with naive B cells and CD8 T cells, but negative correlations with resting NK cells and M0 macrophages; KRT73 positively correlated with resting mast cells and M0 macrophages, but negatively with naive B cells and activated dendritic cells. For female-DEGs, NMUR1 was positively associated with resting NK cells and Tregs, but negatively with activated dendritic cells and naive CD4 T cells; GNG2 positively correlated with resting NK cells and M0 macrophages, but negatively with activated CD4 memory T cells and neutrophils; HAVCR2 showed positive correlations with resting NK cells and Tregs, but negative correlations with activated CD4 memory T cells and naive CD4 T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-J).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e8 GeneMANIA\u003c/h3\u003e\n\u003cp\u003eThe co-expression network of sex-specific genes was constructed using the GeneMANIA website. In the complex GeneMANIA network, co-expression interactions accounted for 8.01%, physical interactions for 77.64%, and colocalization for 3.63% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e9 Correlation between core gene and immune-related genes\u003c/h3\u003e\n\u003cp\u003eSignificant differences in the correlation between sex-specific genes and immune-related genes were observed: male-specific genes exhibit stronger associations with immune stimulatory factors, whereas female-specific genes show more prominent correlations with immune inhibitory factors and receptors (Supplementary Figure S4).\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003e10 Drug target prediction and molecular docking\u003c/b\u003e\u003c/div\u003e\u003cp\u003eIn the DSigDB database, common drug molecules were identified for five core genes; Three genes (NMUR1, CCNE1, and HAVCR2) had associated drugs that could be predicted (Supplementary Figure S5). Notably, no known structures were found for the drugs related to HAVCR2. Therefore, molecular docking was performed on the highest-scoring drug-gene pairs, namely CCNE1-CDK INHIBITOR SNS-032 and NMUR1-COMPOUND 5D. The molecular docking results for the featured genes and their corresponding drugs consistently demonstrated low binding energies, with all values being less than zero (Supplementary Figure S6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe initially applied MR analysis and transcriptome data analysis to identify sex-specific differentially expressed genes in childhood asthma. We found that GNG2, HAVCR2, and NMUR1 were differentially expressed in females, while CCNE1, KRT73, and NMUR1 were differentially expressed in males. Notably, NMUR1 was shared across sexes, suggesting a conserved role in asthma pathogenesis. To elucidate the biological roles of these candidate genes, we conducted enrichment analysis, immune infiltration analysis and GeneMANIA network analysis. We also performed drug prediction and molecular docking for the predicted drugs targeting these genes, further supporting their druggability. These findings highlight potential sex-specific molecular drivers of immune dysregulation, and support sex-tailored therapeutic strategies.\u003c/p\u003e\u003cp\u003eNeuromedin U receptor 1 (NMUR1) is a G protein-coupled receptor that primarily mediates neuromedin U (NMU) signaling and serves as a critical mediator in neuro-immune crosstalk\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. NMUR1 is highly expressed in mast cells, eosinophils and group 2 innate lymphoid cells (ILC2)\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In asthma patients, sputum NMUR1⁺ ILC2s, which express high levels of IL-5 and IL-13, are more abundant in mild cases and inversely correlated with disease severity and ICS dose. This indicates NMUR1 pathway activity may help classify asthma\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. NMUR1 may be a key molecule for the aggravation of asthma induced by viral infection in the mouse model\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Our results reveal that NMUR1 has dual associations, suggesting that while this pathway is universally relevant, its activation thresholds or downstream effects may differ between sexes. Interestingly, COMPOUND 5D which has been recognized as a potent hexapeptide agonist for NMUR1\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, exhibited a favorable docking score in our study, offering insights into binding mechanisms.\u003c/p\u003e\u003cp\u003eG-protein gamma subunit 2 (GNG2) is a subunit of the Gβγ dimer and binds to the Gα subunit to form the trimeric G protein\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Trimeric G proteins mediate processes such as cell proliferation, cell differentiation, and angiogenesis\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. In the rabbit model of allergic asthma, Gβγ upregulates the activity of PDE4 by activating the c-Src-ERK1/2 pathway, leading to airway hyperresponsiveness and pulmonary inflammation\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. In human asthma airway smooth muscle cells, the G protein βγ subunit mediates the Ras/MEK/ERK cascade to upregulate the activity of PDE4\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Our research further supports the role of Gβγ dimer in asthma. Moreover, the anti-inflammatory effect of the G protein-coupled estrogen receptor (GPER) -specific agonist was shown in the chronic ovalbumin-sensitized asthma model, suggesting that GPER may be a therapeutic target\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. This finding may explain its female-specific association and reveal a novel mechanism underlying sex hormone-immune crosstalk in asthma.\u003c/p\u003e\u003cp\u003eHepatitis A Virus Cellular Receptor 2 (HAVCR2), also known as T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), is mainly involved in the regulation of immune responses\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. The 574T\u0026thinsp;\u0026gt;\u0026thinsp;G gene polymorphism of TIM-3 is significantly associated with the risk of asthma\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. TIM-3 knockout in LPS lung models links it to galectin-3, hinting at asthma treatment potential\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. The upregulation of Tim-3 on CD4(+) T cells in patients with allergic asthma is associated with Th1/Th2 imbalance\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. However, these studies lack sex stratification. Our study discovered its female-specific association, highlighting the importance of considering sex factors in the research on the immune checkpoint mechanism of asthma.\u003c/p\u003e\u003cp\u003eCyclin E1 (CCNE1) is an important cell cycle regulatory protein, that binds to and activates the expression of cyclin-dependent protein kinase 2, participating in the transition process from the G1 phase to the S phase of the cell cycle and playing a pro-cancer role in various tumors\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Its role in asthma pathogenesis remains unexplored and necessitates further investigation. We prove that CCNE1 targeted by CDK INHIBITOR SNS-032 with a good docking score. CDK INHIBITOR SNS-032, a known CDK inhibitor, exhibits significant anti-tumor activity across multiple cancer models\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Our results support its potential for clinical translation and further elucidate its binding mechanisms. Keratin 73 (KRT73) is a type II keratin protein that is specifically expressed in the inner root sheath cuticle\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. We speculate that KRT73 may serve as a novel epithelial dysfunction-associated factor in male asthma susceptibility.\u003c/p\u003e\u003cp\u003eAbnormal proliferation and activation of immune cells are considered to be the key to the pathogenesis of asthma\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Our study reveals distinct immune cell profiles in pediatric asthma across different sexes. Resting NK cells are significantly increased, with a more pronounced increase in female than in male patients. Resting NK cells can be potently activated by dendritic cells\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. Similar results are observed in the sexual dimorphism of skin immunity, which is mediated by an androgen-ILC2-dendritic cell regulatory axis\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Androgens modulate sex-biased immune gene expression and immune cell populations\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Moreover, differential correlations between target genes and immune cells exist across sex subgroups. These findings highlight the need to investigate sex-specific immune cell dynamics in pediatric asthma to clarify pathogenesis and guide precision therapy.\u003c/p\u003e\u003cp\u003eDespite comprehensive exploration of sex-specific childhood asthma molecular mechanisms and identification of potential biomarkers, limitations persist. Firstly, the transcriptome analysis was conducted on a relatively small sample size. Secondly, MR analyses primarily utilized genetic data from European populations (GWAS database), limiting cross-ancestry generalizability. Thirdly, the expression of candidate genes was not measured in animal models or cell models. Future work will involve a larger sample size to validate the candidate genes and underlying mechanism. Multi-lineage GWAS integration will be performed to enhance universality. The functional impact of the candidate genes will be confirmed through rigorous in vitro or in vivo experiments.\u003c/p\u003e\u003cp\u003eIn conclusion, the integration of MR and transcriptome analysis identified sex-specific causal genes in childhood asthma. These results provide novel insights into the sex-specific pathogenesis of childhood asthma and lay a foundation for future precision medicine approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;contributions :\u003c/strong\u003eSiyuan JIa: Conceptualization, Writing\u0026ndash;original draft,Writing\u0026ndash;review and editing,Formal\u0026nbsp;Analysis,Methodology.Shuyu Chen:Formal Analysis,Methodology, Writing\u0026ndash;original draft.Haiyan\u0026nbsp;Zhu:Formal\u0026nbsp;Analysis,Methodology,Funding.Xiaohong\u0026nbsp;Dai:Writing\u0026ndash;original\u0026nbsp;draft.Huifang\u0026nbsp;Wang:Writing\u0026ndash;original\u0026nbsp;draft,Conceptualization, Methodology.Yu Chen:Conceptualization,Methodology, Writing \u0026ndash; original draft. Qianqian\u0026nbsp;Yu:Methodology,Writing\u0026ndash;original\u0026nbsp;draft,Data\u0026nbsp;curation.Rongrong Zhang:Conceptualization,Software, Supervision,Validation,Writing \u0026ndash; review and editing. Zhaofang Tian:Conceptualization,Writing \u0026ndash; review and editing, Funding\u0026nbsp;acquisition,Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe author(s) declare that financial support was received for the research and/or publication of this article. This project was supported by The Affiliated Huaian No. 1 People\u0026rsquo;sHospital of Nanjing Medical University \u0026ldquo;Research and Innovation Team\u0026rdquo;project (YCT202301) ，Jiangsu Provincial Maternal and Child Health Research Project（F202306）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof\u0026nbsp;interest\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or\u0026nbsp;financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative\u0026nbsp;AI statement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe author(s) declare that no Generative AI was used in the creation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003eThe data supporting the findings of this study are included in the supplementary materials of this article and are also available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMARTIN J, TOWNSHEND J. 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Nature. 2024;629(8010):193\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"child asthma, sex, transcriptome data, Mendelian randomization analysis","lastPublishedDoi":"10.21203/rs.3.rs-7547685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7547685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aimed to identify key candidate genes underlying sex disparities in childhood asthma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBlood gene expression datasets from children with asthma and healthy controls (GSE27011 and GSE203482) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between asthma and normal blood samples were identified using limma package, with sex stratification. Functional enrichment analyses were performed to characterize these DEGs. Mendelian Randomization (MR) analysis was further applied to identify sex-specific causal genes. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potential of target genes. Bioinformatics analyses were used to explore the biological functions of candidate genes. Drug prediction and molecular docking analyses were employed to further evaluate the therapeutic potential of the identified drug targets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe identified 74 female-specific and 112 male-specific DEGs in pediatric asthma. Functional enrichment analysis indicated that these DEGs were potentially implicated in sex-specific asthma pathogenesis. MR analysis identified 5 causal genes in females (GNG2, HAVCR2, NMUR1, AUTS2, and DTHD1) and 3 in males (CCNE1, KRT73, and NMUR1). Five genes (GNG2, HAVCR2, NMUR1, CCNE1, and KRT73) exhibited consistent differential expression patterns across both MR and transcriptomic analysis, and were thus retained as core candidates. ROC analysis validated their diagnostic potential, with the area under the curve (AUC) values all exceeding 0.7 in the GSE27011 cohort. Immune infiltration analysis further revealed a significant elevation in resting natural killer (NK) cells in female asthma patients, while male asthma samples displayed increased resting NK cells, M0 macrophages, and resting mast cells. Molecular docking analysis showed favorable binding affinity between drugs and proteins with available structural information.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eBy integrating transcriptome analysis with MR approaches, we identified GNG2 and NMUR1 as female-specific causal genes in childhood asthma, along with CCNE1, KRT73 as male-specific causal genes, while NMUR1 was shared between sexes. This integrative strategy may facilitate the development of precise biomarkers and therapeutic targets for childhood asthma.\u003c/p\u003e","manuscriptTitle":"Identification of Sex-Specific Candidate Genes Underlying Childhood Asthma Using Integrative Transcriptomic and Mendelian Randomization Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:33:16","doi":"10.21203/rs.3.rs-7547685/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31df5314-28a6-40bf-a150-1ceee47ae25d","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T21:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 11:33:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7547685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7547685","identity":"rs-7547685","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}