Developing a Panel of Shared Susceptibility Genes as Diagnostic Biomarkers for chronic obstructive pulmonary disease...

Chronic obstructive pulmonary disease (COPD) and heart failure (HF) are closely intertwined comorbidities that present significant clinical challenges due to the poorly understood pathophysiological mechanisms driving their coexistence. In this study, we systematically identified molecular signatures associated with COPD-HF comorbidity through an integrative bioinformatics analysis of multi-omics datasets. Our findings yielded novel diagnostic biomarkers and elucidated the underlying pathophysiological mechanisms.

The total genes that intersect with the differentially expressed genes (DEGs) of COPD patients and the weighted gene coexpression network (WGCNA) module genes were identified by analyzing DEGs between COPD patients and healthy individuals, as well as two HF datasets. To assess the diagnostic potential, a nomogram based on receiver operating characteristic (ROC) curve analysis was developed. Significantly differentially expressed genes were selected from both COPD and HF groups using the machine learning method known as Least Absolute Shrinkage and Selection Operator (LASSO). Additionally, single sample gene set enrichment analysis (ssGSEA) was employed to investigate the immune systems of HF and COPD patients.

We identified 2002 DEGs between COPD patients and controls, with 36 overlapping WGCNA module genes; furthermore, a total of 201 DEGs were discovered from two HF datasets. Ultimately, the intersection of HF and COPD-related genes revealed four co-susceptibility genes, including SVEP1, MOXD1, SMOC2, and GNB3, were significantly upregulated in both diseases (P < 0.001) and demonstrated high diagnostic accuracy (AUC>0.85). Mechanistically, Machine learning techniques, specifically LASSO analysis, identified five diagnostic genes in COPD and 24 in HF. Patients with chronic COPD and heart failure exhibited significantly elevated expressions of four co-susceptibility genesco-susceptibility genes. Nomograms demonstrated their diagnostic potential in terms of accuracy and performance. Activated CD8 T cells were found to be highly correlated with SVEP1, MOXD1, and SMOC2 in COPD patients, while SVEP1 showed a significant correlation with 26 immune cell types in heart failure patients, as indicated by the ssGSEA analysis. KEGG analysis indicated WNT, VEGF, and SPHINGOLIPID signaling pathways and the co-susceptibility genes were associated in COPD and HF patients.

By utilizing publicly available RNA sequencing data, this study identified a panel of genes that are significantly up-regulated in both COPD and heart failure. Four genes demonstrated high diagnostic value through ROC curve analysis, leading to the development of a nomogram designed to assess each gene's diagnostic potential for patients suffering from COPD and HF.