Identification of Shared Biomarkers in Chronic Kidney Disease and Diabetic Nephropathy Using Single-Cell Sequencing - Ma JS; Yang J; Wang WC; Quan YX; Liao XN; Bai YH; Jiang HY (Diabetes, Metabolic Syndrome and Obesity)
Jin-Sha Ma, Jiao Yang, Wen-Chao Wang, Yi-Xiao Quan, Xing-Na Liao, Yi-Hua Bai, Hong-Ying Jiang Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, People’s Republic of ChinaCorrespondence: Yi-Hua Bai, Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, No. 374 Dianmian Road, Wuhua District, Kunming, 650101, People’s Republic of China, Tel +86 13658897696, Fax +86 0871 63402482, Email baiyihua@kmmu.edu.cn Hong-Ying Jiang, Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, No. 374 Dianmian Road, Wuhua District, Kunming, 650101, People’s Republic of China, Tel +86 13033371998, Email 1627248965@qq.comBackground: Chronic kidney disease (CKD) and diabetic nephropathy (DN) represent significant renal health challenges, with overlapping pathogenic mechanisms. This study evaluated shared biomarkers in CKD and DN through single-cell sequencing, aiming to identify potential diagnostic and therapeutic targets and provide new insights into their common pathogenesis.Methods: In this study, single-cell RNA sequencing was performed on nine columns of human blood samples, including three control cases, three CKD cases, and three DN cases. Following sequencing, single-cell analysis was conducted to identify different cell types. Differential expression analysis was then performed to compare the disease samples (CKD and DN) with control samples, resulting in the identification of differentially expressed genes (DEGs). The intersection of DEGs between the disease samples and the control samples was extracted, and a Protein-Protein Interaction (PPI) network was constructed using these intersecting genes, with biomarkers identified through the STRING database. Additionally, Gene Set Enrichment Analysis and GeneMANIA were applied to explore the potential mechanisms underlying these biomarkers.Results: Findings revealed elevated IRF7 expression within dendritic cells (DC), while MX1 showed specifically elevated expression in both DN and CKD samples. MX1 and IRF7 exhibited notable high expression in DC. Four biomarkers were all enriched in the Oxidative Phosphorylation pathway in CKD, and in DN, they were all enriched in the FcγR Mediated Phagocytosis pathway. STAT1 and ISG15 were widely expressed across macrophages, monocytes, NK cells, and NK T cells. In conclusion, the four biomarkers were expressed differently in the disease and control groups of different immune cells.Conclusion: Our study successfully identified MX1, IRF7, STAT1, and ISG15 as shared biomarkers in CKD and DN, revealing their distinct expression patterns and potential roles in disease mechanisms.Keywords: chronic kidney disease, diabetic nephropathy, gene set enrichment analysis, pseudotime, single-cell RNA-sequencing