Introduction

Asthma, a chronic and complex disease, affects over 300 million individuals globally, with projections suggesting an increase to 400 million by 2025 [1]. Characterized by pervasive airway inflammation and intricate cellular dynamics, asthma presents significant global health challenges, as indicated by the Global Asthma Network (GAN) with prevalence rates of 10.5% among children and 4.4% among adults [2]. The disease manifests through symptoms such as airway hyperresponsiveness, eosinophilic infiltration, reversible airflow obstruction, airway remodeling, mucus hypersecretion, and goblet cell hyperplasia [3]. Despite extensive research, the underlying mechanisms of asthma remain poorly understood, a consequence of its multifactorial nature involving genetic, environmental, infectious, immunological, and dietary components. Current therapeutic approaches primarily target symptom control, often leaving a considerable number of patients undermanaged [4]. This gap underscores the critical need for an in-depth investigation into the genetic and molecular bases of asthma, which could revolutionize our understanding of its onset and progression [5]. Advancements in this field promise to shift from merely managing symptoms to altering the disease trajectory, potentially transforming asthma treatment and significantly enhancing patient care worldwide [6].

PyM plays a pivotal role in the synthesis, degradation, and recycling of critical pyrimidine nucleobases, such as cytosine and uracil, which are crucial to nucleic acid structures. Beyond its foundational role in nucleic acid synthesis, PyM is intricately linked to broader aspects of energy metabolism through its biosynthetic and catabolic pathways, encompassing both de novo and salvage routes [7]. These pathways are particularly vital in rapidly dividing cells [8]. Disruptions in PyM pathways can lead to a range of inherited metabolic and autoimmune inflammatory disorders, including asthma. Recent studies highlight the role of microRNAs, notably miR-146a and miR-155, in promoting asthma by downregulating genes that typically suppress cell proliferation [9]. Furthermore, modulation of these microRNAs through TSHR-mediated pathways reveals insights into the fibroproliferative characteristics of asthma. Research by Madera-Salcedo et al. has shown that inflammation-induced hypermethylation of PPP2R2B (B55ß) leads to resistance to apoptosis in the absence of cytokines [10]. Concurrently, Zhu et al. demonstrated how UBE2T exacerbates the progression of hepatocellular carcinoma by influencing PyM [11]. This research aims to dissect the role of PyMGs in asthma immunotherapy, seeking to uncover new therapeutic opportunities by investigating purinosome formation and the glutamine pyrimidine metabolism pathway [12]. Despite significant progress, the impact of PyM on the immunogenic landscape and its critical role in enhancing the efficacy of immunotherapies for asthma remain poorly understood. This study is committed to a comprehensive assessment of PyMGs and their integration with immunotherapeutic strategies in asthma, potentially leading to transformative clinical advancements.

The Asthma Initiative heralds a transformative leap in biomedical research, integrating comprehensive transcriptomic sequencing with detailed clinical annotations to elucidate the transcriptional and molecular intricacies of asthma [13,14,15]. This project employs cutting-edge bioinformatics to navigate vast datasets, aiming to unveil the complex pathophysiological foundations of the disease. Notably, the role of PyMGs in the asthma schema remains underexplored. Our study addresses this oversight by utilizing asthma-specific datasets from the GEO to investigate the impact and relevance of PyMGs on asthma pathogenesis, as illustrated in Fig. 1. Through this focused analysis, we aim to enhance our understanding of asthma’s molecular basis and pave the way for novel therapeutic strategies.

Fig. 1
figure 1

Framework

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Materials and methods

The methodologies proposed by Zi-Xuan Wu et al. in 2023 were employed in this study [16].

Raw data

GEO was searched for mRNA expression. Series: GSE76262 and GSE147878. Platform: GPL13158 and GPL6480. Strategy for searching (‘Parkinson’ [MeSH] mRNA [All Fields] and normal) AND (‘Homo sapiens’ [Organism] AND ‘Non-coding RNA profiling by array’ [Filter]). Specifically, this investigation harnessed the datasets GSE76262 and GSE147878, underpinned by the GPL13158 and GPL6480 platform. GSE76262 functioned as the training cohort, while GSE147878 constituted the testing group (Table 1). We also identified PyMGs from the MSigDB (Table S1).

Table 1 The characteristics of model
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Analysis of differentially expressed genes (DEGs)

Our methodology for extracting precise mRNA profiles involved the utilization of Perl scripts to meticulously match and sort transcriptional data from the GSE76262 dataset. Following normalization procedures, we applied stringent criteria for identifying differential expression among PyMGs: FDR < 0.05 and |log2FC|≥ 1. This rigorous approach enabled the isolation of significantly altered PyMGs for further scrutiny. To elucidate the intricate relationships among these genes, Pearson's correlation coefficient was harnessed, leveraging the corrplot package in R for comprehensive correlation analysis. This step was pivotal in highlighting genes with statistically significant associations within the identified modules.

GO and KEGG analysis

To elucidate the biological implications and pathway involvements of the differentially expressed genes (DEGs), we conducted comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Employing the R programming language, we investigated the impact of differentially expressed PyMGs on biological processes (BP), molecular functions (MF), and cellular components (CC). This analysis aimed to delineate the overarching biological themes and molecular pathways influenced by these genes, thereby enhancing our understanding of their roles in disease pathology and identifying potential therapeutic targets. Through this multi-faceted approach, we not only categorized the DEGs but also shed light on the intricate interplay between purine metabolism and its broader biological and clinical significance.

Model construction and analysis of immune cell infiltration

In our pursuit of a predictive model characterized by unparalleled precision and reliability, we utilized the glmnet package for Lasso regression analysis, complemented by cross-validation, to refine and enhance our model. This methodology effectively mitigated overfitting, thereby boosting the model's predictive capability for complex biological datasets. For further validation, we employed the sophisticated SVM-RFE algorithm using the e1071 package, meticulously constructing a machine learning model. Cross-validation was pivotal in assessing the model's error rates and accuracy, ensuring its robustness and dependability. The Random Forest algorithm, renowned for its efficacy in ensemble learning, was integral to our analysis. By generating multiple decision trees and aggregating their predictions, it minimized overfitting risks and enhanced model generalization. The algorithm's distinctive feature—random feature selection and bootstrap sampling—fostered diversity among decision trees, thus improving the model's overall accuracy. Utilizing the randomForest and ggplot2 packages, we concentrated on analyzing differentially expressed genes, identifying key genes crucial for disease classification. In the final phase, we ranked the significance of these feature genes using an integrated approach that synthesized insights from Lasso regression, Random Forest, and SVM models. This provided a nuanced understanding of their roles in disease pathology. Furthermore, the CIBERSORT algorithm enabled us to analyze the immune cell composition, offering deeper insights into the immune landscape associated with the disease. This comprehensive and rigorous analytical approach not only augmented the accuracy of disease classification but also unveiled new avenues for understanding the molecular basis of the disease.

GSEA and GSVA

To elucidate the functional dynamics and pathway alterations across a spectrum of samples, we employed Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). These powerful methodologies enabled the identification of functionally related gene sets and pathway changes, utilizing quantitative scores and visual representations to highlight active biological processes and pathways within various risk stratifications. Using R, we thoroughly investigated the impact of differentially expressed PyMGs on BP, MF, CC, and pathways, providing a granular understanding of their roles in disease mechanisms. This approach allowed us to uncover the intricate biological themes and molecular pathways influenced by these genes, thereby enhancing our comprehension of their involvement in disease pathology.

Drug-gene interactions in asthma

As bioinformatics in asthma research progresses towards pinpointing viable biomarkers, the construction of biological models and the identification of effective markers for disease diagnostics have become increasingly critical. Grasping the clinical relevance of these biomarkers is essential for crafting targeted therapeutic approaches. Anticipating drug reactions based on these pivotal markers is fundamental for the development of future preventative measures and therapeutic regimens for asthma. In this vein, validated biomarkers stand as crucial reference points. Thus, precise prediction of drug-gene interactions holds paramount significance. In this study, the Drug-Gene Interaction database (DGIdb) (https://dgidb.genome.wustl.edu/) was utilized to forecast interactions between the identified hub genes and prospective therapeutic agents, highlighting the potential for targeted intervention strategies in asthma management.

Investigation of shared miRNAs and lncRNAs in asthma

The regulatory landscape of genetics is profoundly influenced by non-coding RNA transcripts, particularly microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). miRNAs modulate gene expression by enhancing or repressing mRNA degradation and translation, while lncRNAs, consisting of over 200 nucleotides, orchestrate a multitude of cellular processes through mechanisms such as chromosomal modifications, transcriptional activation, and interference. Recent studies have illuminated the extensive interplay between miRNAs and lncRNAs, fostering a competitive binding scenario among miRNAs, lncRNAs, and other regulatory entities. This interaction has led to the concept of competitive endogenous RNAs (ceRNAs), where lncRNAs can regulate gene expression by sequestering miRNAs. In light of these findings, our investigation aims to determine whether specific miRNAs and lncRNAs share regulatory mechanisms and developmental pathways in Asthma, potentially unveiling novel avenues for understanding and treating this complex condition.

Establishment of a common mRNA-miRNA-lncRNA network in asthma

To elucidate the interactive landscape among mRNA, miRNA, and lncRNA entities in Asthma, we sourced target gene information from miRTarBase and PrognoScan, databases renowned for providing empirically validated miRNA-lncRNA-target interactions. By intersecting the target genes of common mRNA-miRNA-lncRNA interactions with Asthma-associated genes, we established a regulated network. This network was visualized using Cytoscape software, providing a graphical representation of the molecular interplay critical to Asthma pathophysiology.

Mendelian randomization analysis

To ensure the independence of exposure and outcome variables in our genome-wide association study (GWAS) summary data, we engaged in an association analysis via the TwoSampleMR package in R. Designating NME7 and POLR2B-related expression as the exposure and Asthma as the outcome, we aimed to explore potential causal relationships. The analysis entailed: 1. Instrumental Variables (IVs) Configuration: NME7 and POLR2B-related expressions were screened with a P-value threshold of < 5 × 10^-8 to identify strongly associated exposures. 2. Independence Configuration: Linkage disequilibrium (LD) between SNPs was calculated using the PLINK clustering method, excluding SNPs with LD coefficient r^2 > 0.001 and within 10,000 kb to ensure SNP independence and reduce pleiotropic biases. 3. Statistical Strength Configuration: The robustness of instrumental variables was assessed using the F-statistic (F = β^2/SE^2), with variables having F < 10 deemed inadequate to mitigate confounding effects.

Leveraging GWAS data, SNPs associated with the instrumental variables were identified, and through the “harmonise_data” function within TwoSampleMR, we aligned allelic directions of exposure and outcome, excluding incompatible SNPs. The inverse variance-weighted (IVW) method served as the cornerstone for causal inference, employing the variance of instrumental variables as weights to determine causal dynamics, thereby advancing our understanding of the genetic architecture underlying disease states.

Active components-targets docking

ADCY4 and PNPT1 were docked to verify the accuracy of principal components and prediction targets. The protein configurations of the core targets were obtained from the Uniprot database by using the minimum resolution (Resolution) and the source (Method) as X-ray as the screening condition, and the crystal structure of these protein configurations were obtained from the RCSB PDB database). 2D structures of 6 active components of core targets were obtained from PubChen database, and these 2D structures were minimized by chem3d software. The binding strength and activity of active components and targets were evaluated by SYBYL2.0 software, and the active components of binding TotalScore greater than 3 were selected for sub-docking. Then imported the crystal into the Pymol 2.4 for dehydration, hydrogenation, and separation of ligands; it then imported AutoDockTools 1.5.6 to construct the docking grid box for each target. Docking was completed by Vina 1.1.2 software, and the molecules with the lowest binding energy in the docking conformation were selected to observe the binding effect by matching with the original ligands and intermolecular interactions (such as hydrophobicity, cation-π, anion-π, π-π stacking, hydrogen bonding, etc.). Finally, the Pymol2.4 software was utilized to visualize the molecular docking.

Results

Identification of DEGs and principal component analysis

Among the 31 examined PyMGs, several exhibited significant differences in expression levels. Furthermore, gene clustering analysis revealed distinct clusters in the treatment and control groups. Notable PyMGs in the treatment group included DHODH, POLE, UCK2, ENTPD1, NT5E, UPB1, ENPP1, TXNRD1, while control group PyMGs comprised PNPT1, POLR3F, PRIM2, POLR2C, POLR2G, POLR1D, POLR2B (Fig. 2a). Correlation analysis was conducted among these PyMGs, and a correlation matrix was generated for visualization (Fig. 2b) (Table S2).

Fig. 2
figure 2

Principal Component Analysis. a Analysis of difference. b Analysis of correlation

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Enrichment analysis of PyMGs

GO enrichment analysis identified 290 core target genes, encompassing BP, MF, and CC. The MF category primarily involved nucleoside binding (GO: 0001882), catalytic activity, acting on RNA (GO: 0140098), ribonucleoside binding (GO: 0032549). The CC category was mainly associated with transferase complex, transferring phosphorus-containing groups (GO: 0061695), nuclear chromosome (GO: 0000228), RNA polymerase complex (GO: 0030880). The BP category included RNA splicing (GO: 0008380), anatomical structure homeostasis (GO: 0060249), purine-containing compound metabolic process (GO: 0072521). KEGG enrichment analysis revealed that the upregulated genes were primarily involved in Pyrimidine metabolism (hsa00240), Huntington's disease (hsa05016), Purine metabolism (hsa00230) (Fig. 3 and Table S3a, b).

Fig. 3
figure 3

For PyMGs, GO, and KEGG analyses were performed. a The GO circle illustrates the scatter map of the selected gene’s logFC. b The KEGG barplot and bubble illustrates the scatter map of the logFC of the indicated gene

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Model construction

In our study, we meticulously established a gene signature by employing LASSO and Cox regression analysis, judiciously selecting the optimal value, as depicted in Fig. 4a, b. To validate the precision and reliability of our model, we constructed a machine learning model using SVM-RFE. This model demonstrated exceptional accuracy, achieving a score of 0.863, and maintained a minimal error rate of 0.137, as shown in Fig. 4c, d. Some key genes were screened by random forest tree, including NME7, POLR3C, ENTPD4, ENPP1, UCKL1, etc. (Fig. 4e, f). The intersection of the 11 PyMGs identified by LASSO, RF and SVM revealed strong concordance (Fig. 4g). Upon evaluating the model in relation to the 11 hub genes, we observed notably high accuracy rates for each gene: DHODH (AUC = 0.680), UMPS (AUC = 0.823), NME7 (AUC = 0.776), NME1 (AUC = 0.767), POLR2B (AUC = 0.672), POLR3B (AUC = 0.775), POLR1C (AUC = 0.637), POLE (AUC = 0.699), ENPP3 (AUC = 0.679), RRM2B (AUC = 0.744), TK2 (AUC = 0.720) (Fig. 4f). Remarkably, an AUC of 0.934 (95% CI 0.887−0.973) was achieved in dataset GSE76262, underscoring the high accuracy and robustness of our prediction model (Fig. 4g) (Table 1 and S4). In evaluating the performance of our study, particularly with regard to the AUC, a detailed analysis of Fig. 4 demonstrates an impressive AUC value of 0.934, underscoring the high accuracy of our model. Addressing concerns about the lower AUC values observed for certain genes, it is essential to consider the influence of individual genetic variations on these outcomes. Despite these variations, it is important to note that the aggregated AUC values for these genes consistently approximate a significant benchmark of 0.7. This collective result substantiates the overall credibility, precision, and robustness of our predictive model, affirming its utility in both clinical and research contexts.

Fig. 4
figure 4

The development of the PyMGs signature. a Regression of the 11 Asthma-related genes using LASSO. b Cross-validation is used in the LASSO regression to fine-tune parameter selection. c, d Accuracy and error of this model. e, f: RF. g Venn. h AUC of 10 hub genes. i AUC of train group

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Gene set enrichment analysis

In this study, the AUC of each gene in the test group, the Rank ranking of each gene, and the results of the test group validation were observed. We found that NME7 and POLR2B may be the most relevant genes. Through literature evaluation and analysis of hub gene sensitivity within the model, it was determined that NME7 and POLR2B may be the most relevant genes to Asthma. In terms of GO analysis, NME7 was found to be associated with CC ribosome, MF cytokine receptor activity, MF structural constituent of ribosome. On the other hand, POLR2B was primarily involved in the CC mitochondrial protein containing complex, CC organellar ribosome, CC ribosomal subunit (Fig. 5a). In KEGG analysis, NME7 was mainly associated with cytokine cytokine receptor interaction, neuroactive ligand receptor interaction, oxidative phosphorylation, while POLR2B was involved in neuroactive ligand receptor interaction, oxidative phosphorylation, parkinsons disease (Fig. 5b) (Table S5).

Fig. 5
figure 5

GSEA of Analysis in NME7 and POLR2B. a GO. b KEGG

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Analysis of immune cells

In this investigation, we explore the nuanced role of the immune microenvironment in the onset and progression of asthma, a complex condition shaped by the dynamics of immune cell interactions. Our extensive analysis was designed to unravel the expression patterns and interrelations of these cells, utilizing violin plots to vividly delineate the differential expression profiles of immune cells in control versus asthma-impacted tissues. These graphical representations distinctly showcased an upregulation of activated mast cells, eosinophils, and both resting and activated dendritic cells in the control samples. Conversely, tissues afflicted by asthma exhibited heightened levels of CD8 T cells, follicular helper T cells, and both M0 and M2 macrophages, illuminating the altered or engaged immune response mechanisms in asthma. This endeavor aimed to elucidate the potential genetic interplays with, or influences by, the immune microenvironment in asthma, striving to enrich our comprehension of the disease's pathophysiological underpinnings. The results, showcased in Fig. 6a, reveal a complex symbiosis between immune cells and genetic factors, broadening our understanding of the intricate immune-genetic interplay in asthma. In addition, we added NME7 and POLR2B to the immune infiltration analysis of their respective genes alone.

Fig. 6
figure 6

Expression of Immune cells. a Expression of immune cells in different clusters. b NME7. c POLR2B

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GSVA

In the GO analysis, NME7 was primarily associated with CC septin cytoskeleton, BP negative regulation of myoblast proliferation, BP axonemal central apparatus assembly, MF olfactory receptor binding, CC iga immunoglobulin complex. POLR2B was mainly involved in the MF udp xylosyltransferase activity, BP regulation of mirna catabolic process, BP positive regulation of mirna catabolic process, CC fancm mhf complex, MF glycine n acyltransferase activity (Fig. 7a). In terms of KEGG analysis, NME7 was mainly associated with glycosaminoglycan degradation, taurine and hypotaurine metabolism, renin angiotensin system, glycosphingolipid biosynthesis lacto and neolacto series, asthma, sulfur metabolism. POLR2B was involved in glycosaminoglycan biosynthesis heparan sulfate, linoleic acid metabolism, arachidonic acid metabolism, taste transduction, retinol metabolism, nitrogen metabolism (Fig. 7b).

Fig. 7
figure 7

GSVA of Analysis in NME7 and POLR2B. a GO. b KEGG

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Drug-gene interactions

Some drugs were predicted to interact with the eleven hub genes, including leflunomide, teriflunomide, vidofludimus, chembl1164954, leucovorin (Table S6) (Fig. 8). The investigation unveiled eleven PyMGs—DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2—significantly associated with asthma. According to rank, NME7 and POLR2B were selected for molecular docking. To verify the credibility of our results. We performed molecular docking of NME7 and POLR2B with CHEMBL1164954, LEFLUNOMIDE, TERIFLUNOMIDE, ZALCITABINE (Table 2 and Fig. 9).

Fig. 8
figure 8

Drug-gene interactions. Red circles are up-regulated genes, green hexagons are down-regulated genes, and blue squares are associated drugs

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Table 2 Molecular docking results of UMPS and DHODH
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Fig. 9
figure 9

Molecular docking results. a UMPS. b DHODH

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Identification of common RNAs and construction of miRNA-lncRNA shared genes network

A total of 212 miRNAs and 241 lncRNAs associated with Asthma were identified from three databases (Table S7a, b). Table S7 shows the matching of these genes against the corresponding miRNA database. These databases include miRanda [17], miRDB [18], and TargetScan [19]. When the corresponding database matched the relevant miRNA, the score was marked as 1. It can be seen that when all three databases can be matched, it is 3 points. The miRNA was matched by spongeScan database [20] to obtain the corresponding lncRNA data. The miRNA-lncRNA-gene network was constructed by intersecting these non-coding RNAs with the shared genes obtained through Lasso regression and SVM-RFE. The network consisted of 190 lncRNAs, 182 miRNAs, and some common genes, including the 11 hub genes (NME7, TK2, UMPS, POLE, ENPP3, POLR1C, DHODH, RRM2B, POLR3B, NME1, POLR2B) (Fig. 10).

Fig. 10
figure 10

miRNAs-LncRNAs shared Genes Network. Red circles are mrnas, blue quadrangles are miRNAs, and green triangles are lncRNAs

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Validation of hub genes

To enhance the confidence and prediction accuracy of the model, GSE147878 dataset was used for validation. The GSE147878 analysis further confirming their potential relevance to Asthma (Fig. 11).

Fig. 11
figure 11

eleven hub genes were validated

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Model verification

The Boxplots depicted the residual expression patterns of these genes in Asthma (Fig. 12a). There are some differences in the proportions of the four different modes (Fig. 12b, c). The PyMGs' diagnostic capacity in distinguishing Asthma from control samples revealed a satisfactory diagnostic value, with an AUC of RF: 0.967; SVM: 0.919; XGB: 0.943; GLM: 0.933 (Fig. 12c). An AUC of 1.000 (95% CI 1.000–1.000) in GSE147878 (Fig. 12d).

Fig. 12
figure 12

Model verification. a Residual expression patterns. b, c Model expression patterns (d) AUC of model. e AUC of test group

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Mendelian randomization analysis

In our exploration of the intrinsic connection between NME7 and POLR2B, and asthma forest plots were meticulously employed to visually articulate the associations. For NME7, the all SNPs (rs73078636, rs10178845, rs11071559, rs2460555, rs7734635, rs5743618, etc.) conspicuously positioned itself to the right of the confidence interval, indicating a positive association. (Fig. 13a). In the case of POLR2B, all SNPs (rs10178845, rs11071559, rs2460555, rs7734635, rs5743618, rs61957178, rs4795399, etc.) were all situated to the right of the confidence interva, suggesting a similar trend of association with asthmal (Fig. 13b). Further dissecting the heterogeneity inherent in our analysis, the funnel plot tailored to asthma revealed a deviation from the expected symmetrical distribution, albeit maintaining a general symmetry. This nuanced observation was further scrutinized through sensitivity analysis, employing a “leave-one-out” approach. Remarkably, the omission of any individual SNP from the analysis had a negligible effect on the results of the Inverse Variance Weighted (IVW) analysis, indicating that the remaining SNPs consistently mirrored the outcomes of the aggregate dataset. Substantiating the validity of our findings, the MR-Egger regression analysis was invoked, providing a solid foundation that bolsters both the robustness and authenticity of our results and the methodologies applied. This Mendelian randomization analysis unequivocally confirms the intimate association of NME7 and POLR2B with asthma. Hence, it delineates a potential pathway to modulate the incidence, evolution, and progression of asthma by intervening in the functions of NME7 and POLR2B, presenting a promising avenue for therapeutic intervention and a deeper understanding of the disease mechanism.

Fig. 13
figure 13

Mendelian Randomization Analysis. a NME7. b POLR2B

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Discussions

Asthma, a chronic inflammatory disorder of the airways, presents a significant global health challenge, with the Global Asthma Network (GAN) study reporting prevalence rates of 10.5% in children and 4.4% in adults [21]. Characterized by airway inflammation, hyperresponsiveness, mucus hypersecretion, and remodeling, the pathophysiology of asthma is complex, resulting from a multifaceted interplay of genetic and environmental factors [22]. Despite the availability of treatments primarily aimed at symptomatic relief, a substantial proportion of patients remain inadequately managed, highlighting the urgent need for advanced therapeutic interventions [23]. In this context, the identification of asthma biomarkers through comprehensive bioinformatic analysis is a critical endeavor. This approach aims to unravel the complex mechanisms underlying asthma and uncover novel therapeutic targets, potentially shifting asthma management towards early detection and precise disease characterization [24]. PyM is essential for the synthesis, breakdown, and utilization of key pyrimidine nucleobases, such as cytosine and uracil, which are integral to nucleic acid structures and energy metabolism [8]. Dysregulation of PyM pathways is implicated in various inherited metabolic disorders, including autoimmune inflammatory diseases [9]. Recent studies have highlighted the role of microRNAs, such as miR-146a and miR-155, in modulating cell proliferation in asthma by targeting genes that inhibit cell growth, and the TSHR-mediated regulation of these microRNAs in asthma's fibroproliferative pathology [10]. Additionally, research has identified dysregulation in PPP2R2B (B55ß) through inflammation-driven hypermethylation, affecting apoptosis resistance, and the role of UBE2T in exacerbating hepatocellular carcinoma by influencing PyM [12]. This study focuses on PyMGs and their relevance in asthma immunotherapy, exploring new therapeutic avenues through the investigation of purinosome formation and the glutamine pyrimidine metabolism pathway [25]. Despite advancements, the impact of PyM on the immunogenic landscape and its role in modulating immunotherapy efficacy in asthma remains to be fully elucidated. Our research aims to conduct a thorough analysis of PyMGs and their interaction with immunotherapeutic strategies in asthma, paving the way for groundbreaking clinical progress.

In our comprehensive study, we identified a network of 31 DEGs intricately associated with PyMGs in asthma. Utilizing a rigorous analytical framework that combines DEG analysis, Lasso regression, and SVM-RFE, we identified eleven critical PyMGs: DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, and TK2. These genes have demonstrated significant diagnostic relevance in asthma, a finding further validated by external datasets, underscoring their essential role in the disease's pathogenesis. However, our research also revealed a significant gap in understanding the interaction between these genes and specific transcription factors, especially within the context of purine metabolism. Notably, NME7 and POLR2B emerged as key mediators in the link between asthma and PyMGs. Further investigation into their biological functions indicated their involvement in various immune-related processes, including RNA splicing, anatomical structure homeostasis, and the metabolic processing of purine-containing compounds. This finding suggests that PyMGs may regulate a broad spectrum of biological pathways, particularly those related to immune responses, thereby potentially influencing the pathophysiological progression of asthma. Our results propose that these genes are crucial in understanding asthma’s progression and could unveil new avenues for therapeutic targeting, presenting promising opportunities for future research and the development of treatment strategies. This underscores the paramount importance of PyMGs within the molecular landscape of asthma, indicating a novel direction in the quest to understand and mitigate this complex disease.

The investigation into PyM, a cornerstone of cellular energy balance and proliferation, has revealed its extensive implications across various diseases and metabolic disorders. In the intricate landscape of asthma pathogenesis, recent research has highlighted the critical roles of NME7 and POLR2B in shaping the molecular framework of this disease. Asthma, characterized by chronic airway inflammation, hyperresponsiveness, and reversible airflow obstruction, arises from a complex interplay of genetic, environmental, and immunological factors [26]. Identifying NME7 and POLR2B as key players in this intricate narrative adds a new dimension to our understanding of asthma's origins and potential therapeutic avenues. NME7, associated with intracellular signaling and cellular differentiation pathways, has been significantly linked to asthma [27]. Its role in modulating signal transduction pathways underscores its impact on the inflammatory cascade and airway smooth muscle cell dynamics, which are integral to asthma's pathophysiology [28]. However, the precise mechanisms by which NME7 influences asthma require further elucidation. The gene's association with asthma suggests that any deviation in NME7's expression or functionality could exacerbate the inflammatory environment, leading to increased asthma symptoms and airway structural changes [29]. Similarly, POLR2B, a vital component of the RNA polymerase II complex essential for mRNA synthesis, has been identified as a gene of interest in asthma research. Its fundamental role in gene expression regulation positions POLR2B as a key mediator in asthma by controlling genes involved in immune responses and airway inflammation [30]. Changes in POLR2B expression or function could alter the transcriptional profile of asthma-related genes, influencing disease severity and the effectiveness of therapeutic interventions. Furthermore, analyses of the GSE147878 dataset have highlighted PyM-related markers as emerging prognostic indicators in asthma, representing a dynamic frontier in genomic exploration. These insights pave the way for innovative therapeutic strategies in asthma management, heralding the advent of personalized medicine in contemporary healthcare. This body of research not only deepens our understanding of asthma's molecular etiology but also underscores the potential of targeted genetic and molecular interventions in revolutionizing asthma treatment paradigms.

Asthma, a chronic disorder, is characterized by persistent airway inflammation and increased reactivity, resulting from a complex interplay between host immune mechanisms and environmental factors within the pulmonary environment. The pathogenesis of asthma is marked by an imbalance between the innate and adaptive immune systems in the airways [31]. Environmental triggers—such as allergens, pollutants, and respiratory pathogens—induce airway epithelial cells to initiate an immune response, characterized by the release of pro-inflammatory cytokines and chemokines. This response recruits various innate immune cells, including neutrophils, macrophages, dendritic cells, and innate lymphoid cells, leading to acute inflammation [32]. Simultaneously, dysregulation of adaptive immune responses, particularly within T-helper (Th) cell subsets, plays a crucial role in sustaining chronic inflammation and structural changes characteristic of asthma [33]. A shift towards Th2-dominated responses, marked by the secretion of interleukins (IL)-4, IL-5, and IL-13, promotes eosinophilic inflammation, airway hyperresponsiveness, and excessive mucus production [34]. Additionally, recent studies have highlighted the significant roles of aberrant Th17 and regulatory T cell (Treg) functions in modulating airway inflammation and remodeling, adding complexity to our understanding of asthma’s immunology. Our analysis, utilizing violin plot visualizations, has identified distinct immune cell expression profiles. The control group exhibited higher levels of activated mast cells, eosinophils, and both resting and activated dendritic cells. In contrast, the treatment group showed increased levels of CD8 T cells, follicular helper T cells, and both M0 and M2 macrophages. These findings provide deeper insights into the immune landscape of asthma. These observations underscore the importance of elucidating immune pathways to advance innovative therapeutic strategies. Immunomodulatory interventions, aimed at reducing inflammation and correcting immune dysregulation, present a promising avenue for novel asthma treatments. This research heralds a new era in targeted therapeutic interventions, poised to reshape the future of asthma therapy by emphasizing the critical role of immune pathways in understanding and managing the disease.

The merging of asthma research with metabolic studies marks a groundbreaking phase in medical science, energized by the adoption of advanced bioinformatics techniques. This shift has significantly broadened our understanding of the molecular complexity and diverse pathological expressions of asthma [35,36,37]. The scientific community's collaborative efforts have been crucial in deciphering the molecular foundations and clinical spectrum of this disease. Our research represents a pivotal advancement in this burgeoning field, highlighting the critical role of PyMGs within the asthma paradigm. Utilizing extensive datasets from the GEO, specifically GSE76262 and GSE147878, we have applied a comprehensive suite of analytical tools, including GO, KEGG, and GSEA. These tools have enabled the development of a sophisticated predictive model that illuminates the intricate role of PyMGs in asthma's etiology. Our work establishes a foundational theoretical framework while also opening new pathways for investigating the metabolic imbalances at the heart of asthma and developing targeted therapeutic approaches. However, the necessity for further empirical research to confirm the mechanisms proposed in our findings is paramount. Such validation, achievable through rigorous in vivo and in vitro testing, is essential to deepen our understanding of asthma. These exacting scientific endeavors are vital for progressing towards effective treatment solutions, shaping the future of asthma research and therapeutic development.

Conclusions

Within the complex landscape of asthma, PyMGs play a central role, orchestrating a broad array of biological interactions, pathways, signaling cascades, and regulatory mechanisms. At the core of asthma's molecular framework, PyMGs underlie the synthesis of key biomolecules such as DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, and TK2, which are crucial in essential physiological processes and metabolic regulation related to asthma. Notably, NME7 and POLR2B are distinguished by their significant regulatory effects on metabolic pathways, profoundly influencing asthma's pathophysiological terrain. Our investigations into PyMGs highlight their vital role in the metabolic imbalances characteristic of asthma, proposing the targeting of these pathways as a viable therapeutic strategy.