CYBB identified as a key immune hub gene linking lung cancer and atrial fibrillation

Abstract

Background: The proportion of patients with lung cancer complicated by atrial fibrillation (AF) is increasing. Identifying shared molecular targets between these two conditions may provide important prognostic insights for patients with comorbidities.

Methods: The GSE8569 and GSE41177 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed using the limma package in R. Weighted gene co-expression network analysis (WGCNA) was conducted to identify significant gene modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, along with gene set enrichment analysis (GSEA), were used to explore biological functions. Clinical survival data for lung cancer were obtained from The Cancer Genome Atlas (TCGA), and receiver operating characteristic (ROC) analysis was conducted using the R package ROC (version 1.17.0.1).

Results: A total of 598 differentially expressed genes (DEGs) were identified. These DEGs were primarily enriched in cell proliferation, inflammatory responses, non-small cell lung cancer, the p53 signaling pathway, and the cell cycle. Three core genes (CYBB, ITGB2, FCER1G) were identified. Notably, CYBB was downregulated in lung cancer compared to normal tissue. Patients in the low-risk group had significantly better survival outcomes. Heatmap visualization showed that expression of CYBB decreased with increasing risk scores, suggesting a protective role.

Conclusion: CYBB expression may influence lung cancer prognosis and contribute to the pathogenesis of AF. Further research is needed to clarify CYBB's role in patients with both conditions.

Keywords: Atrial fibrillation; CYBB; Lung cancer; Prognosis.

Fig. 1

(A) Differential gene analysis of Lung cancer. (B) Differential gene analysis of Atrial fibrillation. (C) Intersection of differential genes in lung cancer and atrial fibrillation. A total of 598 DEGs. (D-G) Results of GOKEGG enrichment analysis of DEGs. (D) Biological process analysis. (E) Cellular component analysis. (F) Molecular function analysis. (G) Results of KEGG enrichment analysis

Fig. 2

Gene set enrichment analysis (GSEA) results for lung cancer and atrial fibrillation. (A-D) GSEA enrichment analysis for lung cancer. (A) Biological process (BP) analysis, showing significantly enriched pathways related to cell activation, migration, and inflammatory response. (B) Cellular component (CC) analysis, highlighting enriched subcellular structures associated with secretory vesicles and granules. (C) Molecular function (MF) analysis, identifying significant functional categories such as kinase binding and protein receptor interactions. (D) KEGG pathway analysis, displaying enriched signaling pathways including chemokine signaling and apoptosis-related pathways. (E-H) GSEA enrichment analysis for Atrial fibrillation. (E) Biological process (BP) analysis, identifying pathways related to inflammation, cell activation, and lipid metabolism. (F) Cellular component (CC) analysis, highlighting enriched vesicular and granule structures. (G) Molecular function (MF) analysis, showing receptor binding and kinase-related functional enrichment. (H) KEGG pathway analysis, depicting enriched pathways involved in cytokine-cytokine receptor interactions and vascular smooth muscle contraction

Fig. 3

Metascape enrichment analysis. (A) Bar graph of enriched terms across input gene lists, colored by p-values. (B) Network of enriched terms: colored by cluster ID, where nodes that share the same cluster ID are typically close to each other. (C) colored by p-value, where terms containing more genes tend to have a more significant p-value

Fig. 4

WGCNA analysis. (A) Selection of the soft-threshold power (β) in WGCNA. The left panel shows the scale-free topology model fit (R²) as a function of β. A power of β = 10 was selected, as it ensures R² > 0.85 (indicated by the red star). The right panel shows the mean connectivity of genes at different β values, demonstrating that connectivity stabilizes after β = 10. (B) Gene clustering dendrogram with dynamic tree cutting, where different colors represent different modules. (C) Heatmap of module eigengene relationships, showing the correlation among modules

Fig. 5

(A) The heat map of correlation between modules and phenotypes. (B) The scatter map of correlation between GS and MM of related hub genes. (C) The DEGs screened by WGCNA and DEGs was used to obtain venn map

Fig. 6

Construction and analysis of protein-protein interaction (PPI) networks. (A) Construct the PPI network of DEGs using STRING online database and utilize Cytoscape software for analysis. (B) MCC was used to identify the central gene. (C) MNC was used to identify the central gene. (D) Betweenness was used to identify the central gene. (E) Radiality was used to identify the central gene. (F) Stress was used to identify the central gene. (G) Core genes (CYBB、ITGB2、FCER1G) were obtained by merging using Venn diagrams

Fig. 7

Prognostic survival analysis. (A) Forest map. (B,C,D) KM survival curve of core genes in lung cancer. (E) Trend graph of risk score in relation to survival time and survival rate. And expression calorimetric map of core genes in lung cancer survival data. (F) ROC curve of riskscore

Fig. 8

(A-B) Heatmaps of core gene expression in lung cancer and atrial fibrillation datasets. (A) Expression heatmap of CYBB, ITGB2, and FCER1G in lung cancer samples (GSE8569). Data were Z-score normalized for each gene across samples. Hierarchical clustering was performed on genes (rows) using Euclidean distance and complete linkage. The top color bar indicates sample groups (control: light yellow, lung cancer: orange). (B) Expression heatmap of CYBB, ITGB2, and FCER1G in atrial fibrillation samples (GSE41177). Data were Z-score normalized for each gene across samples. Hierarchical clustering was applied to genes (rows) with Euclidean distance and complete linkage. The top color bar indicates sample groups (control: light blue, atrial fibrillation: blue). The color gradient represents relative expression levels, with red indicating higher expression and green indicating lower expression. (C) CTD analysis. (CYBB) are associated with atrial fibrillation, lung disease, lung tumors, hypercholesterolemia, heart failure, heart disease, and inflammation

Fig. 9

Immunoinfiltration analysis. (A) Whole gene expression matrix results in proportion of immune cells. (B) Immune cell expression calorigram in a dataset. (C) Map of co-expression patterns between immune cell components