Exploring the molecular mechanisms of asthma across multiple datasets

Figures & data

Figure 1. The flowchart of the study.

Table 1. The clinical characteristics and treatment history of the cases.

Index	Group	Gender	Age	Diagnosis	Treatment history
NO.1	Asthma	Male	35	Bronchial asthma	Initially diagnosed with mild intermittent asthma, the patient used the short-acting β2 receptor agonist, Salbutamol, only during exacerbations. Over the subsequent two years, the condition progressed to mild persistent asthma, leading to the commencement of low-dose inhaled corticosteroids, Fluticasone.
NO.2	Asthma	Male	60	Bronchial asthma	Initially diagnosed with moderate persistent asthma, the treatment strategy consisted of a combined therapy of Fluticasone and the long-acting β2 receptor agonist Formoterol, supplemented by the short-acting β2 receptor agonist Salbutamol when necessary. A year later, with the condition effectively managed, the same treatment regimen continues.
NO.3	Asthma	Male	35	Bronchial asthma	Initially diagnosed with severe asthma, the patient commenced a regimen of high-dose inhaled corticosteroids, long-acting β2 receptor agonists, and the leukotriene receptor antagonist Montelukast. As the condition was inadequately controlled, the biological drug Omalizumab was later incorporated into the treatment plan.
NO.4	Asthma	Female	25	Bronchial asthma	Initially diagnosed with moderate asthma, the therapeutic strategy was a combined regimen of Fluticasone and long-acting β2 receptor agonists, supplemented with short-acting β2 receptor agonists as needed. Once the condition stabilized, the dosage of Fluticasone was gradually tapered.
NO.5	Asthma	Female	40	Bronchial asthma	Initially diagnosed with mild asthma, the patient only utilized short-acting β2 receptor agonists during flare-ups. With the symptoms persistently mild, there was no alteration in the treatment plan.
NO.6	Control	Male	36	Community-acquired pneumonia	According to the antimicrobial susceptibility test results, Cefuroxime was administered for infection control, complemented by Ambroxol for mucolytic therapy.
NO.7	Control	Male	30	Community-acquired pneumonia	Broad-spectrum antibiotic therapy was initiated, employing Levofloxacin.
NO.8	Control	Male	35	Community-acquired pneumonia	Preliminary treatment involved the administration of Amoxicillin/Clavulanic acid for infection control. With symptoms showing improvement yet persistent cough and fatigue remaining, the therapeutic strategy was adjusted to the macrolide antibiotic, Azithromycin. The patient gradually recuperated following treatment.
NO.9	Control	Male	45	Lung cancer	Chemotherapy was undertaken, principally employing Paclitaxel and Cisplatin. Concurrently, analgesics and antiemetics were administered to mitigate the chemotherapy-induced side effects.
NO.10	Control	Female	55	Pulmonary nodules	No history of underlying illnesses. A small nodule was detected in the right upper lobe during a physical examination, which necessitated a bronchoscopic investigation.

Figure 2. Identification of asthma differential genes: (A,B) Volcano plot and heat map of DEGs, with blue indicating downregulation and pink indicating upregulation. (C,D) Bar chart of enriched GO and KEGG pathways of differential genes.

Figure 3. Machine learning methods identify key genes: (A) Co-expression network of DEGs, where each node represents a module, with larger nodes indicating more genes. (B) Display of feature genes using the Random Forest algorithm. (C) Validation of expression patterns based on the external dataset GSE67940.

Figure 4. The relationship between key genes and immune factors: Pearson correlation heat map of key genes and immune factors, where A–E represent Immunoinhibitor, chemokine, MHC, Immunostimulator, and receptor, respectively.

Figure 5. GSEA enrichment analysis of key genes: (A,B) The specific display of two key gene-enriched KEGG signaling pathways, where the pathways enriched in high expression are shown above the vertical axis, and those enriched in low expression are shown below the vertical axis. (C) miRNA network of key genes based on the mircode database.

Figure 6. Correlation analysis of asthma regulatory genes: (A) Pearson correlation analysis of asthma disease regulatory genes and key genes, where blue represents negative correlation and red represents positive correlation. (B) 2D structural diagrams of four potential therapeutic drugs.

Figure 7. Single-cell atlas of asthma: (A) TSNE plot of cell clustering in asthma, with all cells identified as T cells, NK cells, and B cells. (B) Expression of key genes in single cells. (C) Bubble plot of key gene expression in cells.

Figure 8. The mRNA expression levels of key genes: (A) C1orf64 and (B) C7orf26.

Data availability statement

The data that supports the findings of this study, including any relevant details needed to reproduce the published results, are available from the corresponding author upon reasonable request.