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已发表论文
糖尿病肾病与动脉粥样硬化共同分子机制的整合生物信息学分析
Authors Zhang J , Wang Z, Cao H, Zhang H, Chen J
Received 5 September 2025
Accepted for publication 6 January 2026
Published 20 January 2026 Volume 2026:19 560821
DOI https://doi.org/10.2147/JIR.S560821
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Quan Zhang
Juan Zhang,1,* Ziwei Wang,2,* Huimin Cao,1,* Hong Zhang,1 Juan Chen1
1Department of Endocrinology, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, 223300, People’s Republic of China; 2Department of General Practice, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, 223300, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Juan Chen, Email chenjuan20160418@163.com Hong Zhang, Email zhh79318@163.com
Background: Increasing evidence suggests that individuals with diabetic nephropathy (DN) are at a significantly higher risk of developing atherosclerosis (AS) compared to the general population. However, the precise mechanisms underlying this association remain unclear. This study aims to explore the shared pathways and potential biomarkers implicated in this complication.
Methods: Microarray data downloaded from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs) in DN and AS. Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expression modules relevant to DN and AS. We conducted functional pathway enrichment analysis on the shared genes. Support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) methods were utilized to identify and validate potential diagnostic markers. Additionally, immunoinfiltration analysis was performed to examine the relationship between the core markers of DN and AS and the expression of immunoinfiltrating cells. Finally, blood samples from patients were collected to assess the diagnostic efficacy of PRCP.
Results: The results of common genes analysis showed that immune and inflammatory response particularly cytokine-cytokine receptor interactions may be a common feature in the pathophysiology of DN and AS. Three potential shared diagnostic markers—ID4, RNF213, and PRCP—were identified and validated using SVM and LASSO. Immunoinfiltration analysis revealed that the expressions of ID4, RNF213, and PRCP were associated with variations in immune cell populations. Differential analysis of peripheral blood microarray data indicated that only PRCP was significantly overexpressed in both AS and DN samples. Moreover, qRT-PCR confirmed the increased expression of PRCP in peripheral blood mononuclear cells (PBMCs) samples from patients.
Conclusion: The co-expression of RNF213, PRCP, and ID4 may contribute to the development of AS and DN. PRCP, particularly, shows promise as a diagnostic marker due to its detectability as a protein in peripheral blood.
Keywords: diabetic nephropathy, atherosclerosis, bioinformatics, molecular mechanism, inflammation, immune
1Department of Endocrinology, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, 223300, People’s Republic of China; 2Department of General Practice, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, 223300, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Juan Chen, Email chenjuan20160418@163.com Hong Zhang, Email zhh79318@163.com
Background: Increasing evidence suggests that individuals with diabetic nephropathy (DN) are at a significantly higher risk of developing atherosclerosis (AS) compared to the general population. However, the precise mechanisms underlying this association remain unclear. This study aims to explore the shared pathways and potential biomarkers implicated in this complication.
Methods: Microarray data downloaded from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs) in DN and AS. Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expression modules relevant to DN and AS. We conducted functional pathway enrichment analysis on the shared genes. Support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) methods were utilized to identify and validate potential diagnostic markers. Additionally, immunoinfiltration analysis was performed to examine the relationship between the core markers of DN and AS and the expression of immunoinfiltrating cells. Finally, blood samples from patients were collected to assess the diagnostic efficacy of PRCP.
Results: The results of common genes analysis showed that immune and inflammatory response particularly cytokine-cytokine receptor interactions may be a common feature in the pathophysiology of DN and AS. Three potential shared diagnostic markers—ID4, RNF213, and PRCP—were identified and validated using SVM and LASSO. Immunoinfiltration analysis revealed that the expressions of ID4, RNF213, and PRCP were associated with variations in immune cell populations. Differential analysis of peripheral blood microarray data indicated that only PRCP was significantly overexpressed in both AS and DN samples. Moreover, qRT-PCR confirmed the increased expression of PRCP in peripheral blood mononuclear cells (PBMCs) samples from patients.
Conclusion: The co-expression of RNF213, PRCP, and ID4 may contribute to the development of AS and DN. PRCP, particularly, shows promise as a diagnostic marker due to its detectability as a protein in peripheral blood.
Keywords: diabetic nephropathy, atherosclerosis, bioinformatics, molecular mechanism, inflammation, immune