Identification of early warning biomarkers for type 4 cardio-renal syndrome based on bioinformatics analysis and secreted proteins

Chronic kidney disease (CKD) can induce chronic heart failure (CHF), a condition referred to as type 4 cardiorenal syndrome (CRS4). The pathophysiological mechanisms remain unclear, and suitable early warning biomarkers for CHF in CKD patients are lacking. A total of 258 CHF key genes and 383 CKD-related secreted proteins were identified through differential expression analysis and WGCNA. PPI analysis revealed 81 genes as potential pathogenic genes related to CRS4. Enrichment analysis of these pathogenic genes highlighted pathways involved in cytokine activity, extracellular matrix remodeling, and immune response. Three machine learning algorithms identified two hub genes (MME and SERPINF1) as potential biomarkers for CHF, and a nomogram model was constructed. ROC analysis demonstrated that the model achieved an AUC greater than 0.80 in both the CHF merged dataset and two external cohorts. Furthermore, immune cell infiltration analysis indicated a correlation between these biomarkers and the infiltration scores of fibroblasts, CD8 T cells, and mast cells in CHF. Finally, our clinical cohort validated the expression patterns of these two biomarkers in serum, with the diagnostic model achieving an AUC of 0.880. CKD may promote the progression of CHF through proteins secreted by the kidneys and blood cells. MME and SERPINF1 may serve as potential biomarkers for CHF in CKD patients.