Integrative Analysis of Nucleotide Metabolism-Related Genes Reveals a Diagnostic Signature and In Silico Functional Disruption in Hypertrophic Scarring.

Abstract

Hypertrophic scar (HTS) is a fibrotic skin disease characterized by excessive extracellular matrix accumulation and chronic inflammation. This study aimed to investigate cellular heterogeneity, immune alterations, and nucleotide metabolism-related biomarkers in HTS and to explore their potential mechanisms. Single-cell RNA sequencing data were analyzed using Seurat for cell clustering and annotation. Nucleotide metabolism activity was evaluated to identify key cell populations and candidate genes. Bulk transcriptomic datasets from the Gene Expression Omnibus (GEO) were used for differential expression analysis, and machine learning algorithms were applied to develop a diagnostic model. Sixteen cell clusters were identified, with macrophages showing prominent activation of nucleotide metabolism pathways and immune-related signaling. The optimal Enet + svmLinear model achieved strong predictive performance (AUC = 0.934), and SHAP analysis identified NCF1 and GPR34 as key predictive genes. These genes were also associated with immune cell infiltration. In vitro validation using THP-1-derived M2 macrophages showed that NCF1 and GPR34 promoted TGF-β1 secretion and enhanced fibroblast activation, while their knockdown reduced α-SMA and collagen I expression. Collectively, this study reveals the important role of macrophage-associated nucleotide metabolism in HTS and identifies NCF1 and GPR34 as potential biomarkers and therapeutic targets.