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Published on: March 4, 2022
Key RNA-binding proteins in renal fibrosis: a comprehensive bioinformatics and machine learning framework for
Jie Chen1,2, Binghan Zhang1, Qixuan Huang1
1Department of Endocrinology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background:
Renal fibrosis is a critical factor in chronic kidney disease progression, with limited diagnostic and therapeutic options. Emerging evidence suggests RNA-binding proteins (RBPs) are pivotal in regulating cellular mechanisms underlying fibrosis.
Methods:
Utilizing an extensive GEO dataset (175 renal fibrosis and 99 normal kidney samples), we identified and validated key RBPs through integrated bioinformatics and machine learning approaches, including lasso and logistic regression models. Differentially expressed genes were analyzed for pathway enrichment using Gene Ontology and KEGG. Single-cell RNA sequencing delineated cell-specific RBP expression, and a murine unilateral ureteral obstruction (UUO) model provided experimental validation.
Results:
A diagnostic model incorporating five RBPs (FKBP11, DCDC2, COL6A3, PLCB4, and GNB5) achieved high accuracy (AUC = 0.899) and robust external validation. These RBPs are implicated in immune-mediated pathways such as cytokine signaling and inflammatory responses. Single-cell analysis highlighted their expression in specific renal cell populations, underscoring functional diversity. Immunofluorescence linked FKBP11 with macrophage infiltration, suggesting its potential as a therapeutic target.
Conclusion:
his study identifies novel RBPs associated with renal fibrosis, advancing the understanding of its pathogenesis and offering actionable biomarkers and therapeutic targets. The integration of bioinformatics and machine learning emphasizes their translational potential in kidney care.
