ArticleRenal failure2025
Key RNA-binding proteins in renal fibrosis: a comprehensive bioinformatics and machine learning framework for diagnostic and therapeutic insights.
Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Non-invasive assessment techniques for renal fibrosis: advances and perspectives.Renal failure · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRenal 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.
methodsUtilizing 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.
resultsA 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.
conclusionhis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.