Unraveling the matrix stiffness landscape in idiopathic pulmonary fibrosis: GSN and ARG1 as novel diagnostic biomarkers and potential therapeutic targets.

BACKGROUND

Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal interstitial lung disease characterized by excessive extracellular matrix (ECM) deposition and tissue stiffening. Matrix stiffness is a key driver of fibrosis, yet diagnostic biomarkers directly linked to this physical property are lacking. This study aimed to identify robust matrix stiffness-related diagnostic biomarkers and potential therapeutic targets for IPF using an integrated machine learning approach.

METHODS

Gene expression profiles were obtained from the GEO database (Training set: GSE33566; Validation set: GSE93606). Differentially expressed genes (DEGs) were intersected with a matrix stiffness-related gene set. Three machine learning algorithms (SVM-RFE, LASSO, and Naive Bayes) were employed to screen diagnostic feature genes. A diagnostic nomogram was constructed and evaluated. Functional enrichment (GO/KEGG/GSEA), immune infiltration (ssGSEA), and molecular docking analyses were performed to explore biological functions and predict therapeutic drugs.

RESULTS

Eighteen matrix stiffness-related DEGs were identified. Through machine learning screening, GSN and ARG1 were determined as robust key genes, exhibiting high diagnostic accuracy (AUC > 0.7) in both training and validation cohorts. Functional analysis revealed that GSN is involved in actin cytoskeleton regulation, while ARG1 participates in immune response modulation. Both genes showed strong positive correlations with the infiltration of macrophages and neutrophils. Furthermore, molecular docking identified RA-2 as a potential therapeutic agent targeting ARG1 with high binding affinity (-9.2 kcal/mol).

CONCLUSION

We identified GSN and ARG1 as novel matrix stiffness-related diagnostic biomarkers for IPF, linking mechanotransduction to immune microenvironment remodeling. The diagnostic nomogram offers high clinical predictive value, and RA-2 emerged as a putative ARG1-targeting compound with favorable docking energy and warrants further experimental validation as a potential antifibrotic agent.