GLP-1 and diabetic nephropathy share key molecular targets

Glucagon-like peptide-1 (GLP-1) receptor agonists provide renoprotective benefits in diabetes, yet the molecular mechanisms linking GLP-1 signaling to diabetic nephropathy remain poorly defined. This study aimed to identify shared molecular targets between GLP-1 activity and diabetic kidney disease by integrating protein targets of GLP-1 from UniProt with disease-associated genes from GeneCards. The overlapping gene set was analyzed using STRING and Cytoscape with MCODE clustering, followed by Gene Ontology and KEGG enrichment through the clusterProfiler package. Molecular docking with HADDOCK was employed to validate structural interactions between GLP-1 and central network proteins. We identified 17 shared genes, including STAT3, EP300, MAPK1, and INSR, which formed a densely connected cluster enriched in pathways related to insulin response, hypoxia adaptation, apoptosis, and glucose metabolism. Docking analysis demonstrated direct and favorable binding of GLP-1 to STAT3, PIK3R1, and..., , Title:

# Data from: GLP-1 and diabetic nephropathy share key molecular targets

Authors

Wanderson Gabriel Gomes de Melo¹

Regina Lúcia dos Santos Silva¹

Ianahanna Duarte Santos Soares¹

Bruno de Sousa Barbosa¹

Felipe Cardoso de Brito²

Napoleão Martins Argôlo Neto¹

Dayseanny de Oliveira Bezerra²

Affiliations
¹ Universidade Federal do Piauí (UFPI), Teresina, Brazil
² Instituto Federal do Piauí (IFPI), Teresina, Brazil

Associated Manuscript

Journal: *Canadian Journal of Physiology and Pharmacology*
Manuscript ID: cjpp-2025-0146
Status: Accepted for publication

## Description of Files

csv_and_xlsx/ – Raw and processed data tables (CSV/XLSX) used for network and enrichment analyses.

tsv_and_txt/ – Gene lists, metadata, and formatted text exports from STRING and clusterProfiler outputs.

figures/ – Figures generated for visualization of PPI networks, cluster topology, and enrichment results.

others/ – Supplementary analysis scripts (.R, .py) and notes supporting the dataset.

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