Hub Genes PRPF19 and PPIB: Molecular Pathways and Potential Biomarkers in COPD

Abstract

Background: Chronic Obstructive Pulmonary Disease (COPD), a complex respiratory disorder, results from genetic and environmental factors. Uncovering its genetic basis is vital for diagnostics and treatment. Robust genetic analysis is essential to establish a causal link.

Methods: Genome-wide DNA methylation analysis was performed using the Illumina Infinium HumanMethylation850 BeadChip in peripheral blood from 8 COPD patients and 8 healthy smoking controls. Differentially methylated genes (DMGs) were cross-analyzed with differentially expressed genes (DEGs) identified from the Gene Expression Omnibus (GEO) dataset GSE38974 (23 COPD, 9 controls). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) networks were utilized to identify COPD-associated hub genes. Mendelian randomization (MR) analysis examined the causal relationship between hub genes and COPD. The expression of selected hub genes was validated through RT-qPCR (80 COPD, 62 controls), immunohistochemistry, and Western blot analyses (10 COPD and 10 controls).

Results: We found 10,593 DMGs and 646 DEGs associated with COPD. These genes were compared with WGCNA module genes, and the Protein-Protein Interaction (PPI) network interaction diagram was drawn, thereby identifying five Hub genes: PPIB, HSPA2, PRPF19, FKBP10 and DOHH. The expression levels of DOHH, FKBP10, PPIB and PRPF19 are higher in COPD, while the expression level of HSPA2 is lower. MR results indicate a potential causal relationship between PRPF19, PPIB and COPD. RT-qPCR, immunohistochemistry and Western blot experiments verified that the expression of PRPF-19 and PPIB was up-regulated in peripheral blood and lung tissue, which was consistent with the results of bioinformatics analysis.

Conclusion: Our findings suggest that PRPF19 and PPIB may serve as promising diagnostic biomarkers in COPD. Further studies are required to fully elucidate their roles in COPD pathogenesis.

Keywords: Mendelian randomization; chronic obstructive pulmonary disease; epigenetic susceptibility; hub genes; protein-protein interaction.

Figure 1

Identification of methylation related DEGs. (A) Heatmap of differentially methylated loci. (B) Volcano plots of DEGs in the GSE38974 datasets and (C) Heatmap of top 10 up-regulated genes and top 10 down-regulated genes (sorted by log₂FC). (D and E) The Venn diagram illustrates the overlap between differentially expressed genes and methylation-related genes. (D) Methylation-related genes that are differentially highly expressed. (E) Methylation-related genes that are differentially lowly expressed.

Figure 2

Identification of hub genes related to methylation in COPD. (A) Venn diagrams to indicate 56 shared genes from methylation-related DEGs and WGCNA network (43 methylation-related highly expressed genes and 13 methylation-related lowly expressed genes in COPD). (B and C) Visualization of the PPI network and top 5 hub genes. (D) Expression correlation analysis of hub genes. Red indicates a positive correlation and blue indicates a negative correlation. (E) Analysis of hub genes in COPD and control. P < 0.05 was considered to be a statistically significant difference, **** indicates statistical significance at p<0.0001.

Figure 3

The correlation between hub genes and immune cells. (A) Evaluation of 28 immune cells infiltration in ssGSEA analysis of COPD samples; (B) Correlation between hub genes and differential immune cell infiltration.

Figure 4

Univariate Mendelian analysis of PRPF19 and PPIB genes in relation to COPD. Scatterplots (A and B) show SNP effects on COPD. Colored lines represent different MR algorithms. Forest plots (C and D) display SNP effects; left of vertical line indicates reduced risk, right suggests increased risk. Funnel plots assess MR randomness (E and F), IVW line symmetry indicates random grouping. Sensitivity analysis (G and H) shows SNP removal’s minimal impact, ensuring MR reliability/stability.

Figure 5

Immunohistochemical validation of hub genes PPIB and PRPF19 expression in lung tissues. (A–D) Representative immunohistochemical staining images of PPIB and PRPF19 proteins in lung tissues from control subjects (A and C) and COPD patients (B and D). Magnification: 400×. (E and F) Quantitative analysis of immunohistochemistry results performed using ImageJ software. Data are presented as average optical density (AOD) values. Expression levels of PPIB (E) and PRPF19 (F) were significantly elevated in COPD patients compared with controls.

Figure 6

Hub genes as Biomarkers for COPD Diagnosis. (A and B) The differential expression levels and ROC curve analysis of PPIB in peripheral blood samples of a COPD group (n = 80) and a control group (n = 62). (C and D) The differential expression levels and ROC curve analysis of PRPF19 in COPD and control group.

Figure 7

PRPF19 and PPIB Expression and Their Role in Apoptosis Regulation in COPD Lung Tissues. (A) Representative Western blot images show increased expression of PRPF19 and PPIB, along with elevated Bcl-2 (anti-apoptotic) and decreased Bax (pro-apoptotic) protein levels in COPD lung tissues compared to controls, suggesting that PRPF19 and PPIB may contribute to apoptosis regulation by promoting cell survival. (B) Quantitative analysis of Western blot results demonstrating significant upregulation of PRPF19, PPIB, and Bcl-2, and downregulation of Bax in COPD samples (P < 0.05). GAPDH was used as the loading control.