Serum cell adhesion molecules (CAMs) NrCAM and P-Cadherin as novel biomarkers in lupus nephritis

Objective This study aimed to identify novel serum biomarkers for lupus nephritis (LN) and evaluate their potential for improving diagnosis, monitoring disease activity, and understanding LN pathogenesis. Methods A cell adhesion molecules (CAMs) antibody microarray was utilized to identify potential biomarkers of LN using serum samples from LN, chronic kidney disease (CKD), and healthy controls (HC). Differential expression analysis and LASSO regression identified CAM biomarkers in a discovery cohort, followed by ELISA validation in an independent, discovery cohort. Biomarker correlations with clinicopathological parameters were assessed, and receiver operating characteristic (ROC) analyses were performed to examine the potential diagnostic utility. Results Of the 17 CAMs analyzed, serum NrCAM and P-Cadherin emerged as the most promising new biomarkers, significantly discriminating LN from HC. Serum NrCAM was found to differentiate between LN and HC ( p < 0.0001) as well as active LN (LNA) from inactive LN (LNI) ( p < 0.01) in the discovery cohort. Serum P-Cadherin, however, differentiated LN from both HC ( p < 0.0001) and CKD ( p < 0.001) and distinguished LNA from LNI ( p < 0.01). In a larger validation cohort, ROC analyses demonstrated strong diagnostic potential for both biomarkers, with AUC values of 0.92 and 0.82 for serum NrCAM and P-Cadherin, respectively. Significant correlations with SLEDAI, rSLEDAI, and histological indices further underscored their potential relevance to LN severity and activity. Interestingly, serum NrCAM and P-Cadherin are complementary in reflecting renal pathology changes including endocapillary hypercellularity, cellular crescents, glomerulosclerosis, tubular atrophy, and interstitial fibrosis. Conclusion Serum NrCAM and P-Cadherin show promise as non-invasive biomarkers for LN, offering potential utility in diagnosis and disease monitoring. Their ability to distinguish between disease states and correlate with clinical parameters highlights their potential to improve patient stratification and guide personalized treatment approaches in LN.