Transcription Factors as Early Diagnostic Biomarkersfor Chronic Kidney Disease: A Comprehensive Analysis

Wei Zhou; Qingqing Jia; Shujun Wu; Hanyan Meng; Xinyu Wang; Mingzhu Jiang; Jianhua Mao

doi:10.71321/1pe9g186

Authors

Wei Zhou Department of Nephrology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China.
Qingqing Jia Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
Shujun Wu Renal Division of Northern Jiangsu People's Hospital, Clinical Medicine College of Yangzhou University, Yangzhou, China.
Hanyan Meng Department of Nephrology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children's Regional Medical Center, Hangzhou, China.
Xinyu Wang Department of Pediatrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Mingzhu Jiang Renal Division of Northern Jiangsu People's Hospital, Clinical Medicine College of Yangzhou University, Yangzhou, China.
Jianhua Mao Department of Nephrology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children's Regional Medical Center, Hangzhou, China.

DOI:

https://doi.org/10.71321/1pe9g186

Keywords:

Chronic kidney disease, Transcription factor, Biomarker, Data analysis

Abstract

Background: Chronic kidney disease (CKD) is a global health concern with significant implications for public health and mortality rates, projected to become the fifth leading cause of death by 2040. The search for early diagnostic targets for CKD is imperative. In this study, we concentrated on identifying key transcription factors (TFs) for the early diagnosis of CKD and established a regulatory network between these TFs and their corresponding target genes.

Methods: We conducted microarray data analysis and Gene Set Enrichment Analysis (GSEA) to identify differentially expressed genes (DEGs) and the associated pathways in CKD. We further explored the potential regulatory TFs among DEGs using the TRRUST v2 database and validated the TF-target regulatory relationships through correlation analysis and the JASPAR database. The protein expression of the identified TFs in renal tissues was also assessed.

Results: The analysis identified six TFs, namely HNF4A, CEBPA, CREB1, FOS, HIF1A, and SP1, which demonstrated potential as diagnostic biomarkers for CKD. These TFs showed differentially expressed patterns in CKD and were found to have multiple regulatory relationships with DEGs, indicating their crucial role in the disease process. ROC analysis revealed high predictive efficiency for four of these TFs (CREB1, FOS, HIF1A, and SP1), while the combined predictive efficiency of all TFs was exceptionally high.

Conclusion: Our findings highlight the role of transcription factors in the pathophysiological process of CKD and identify several key TFs with potential for clinical translation as early diagnostic biomarkers for the disease. Further validation and exploration are warranted to leverage the potential clinical utility of these TFs in the early diagnosis and prognosis of CKD.

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