Machine learning and network pharmacology identify keloid biomarkers (AMPH, TNFRSF9) and therapeutic targets (IL6, HAS2) for aloe-derived quercetin

OBJECTIVE: This study aimed to identify diagnostic biomarkers for keloid and explore potential therapeutic agents from traditional Chinese medicine (TCM) by integrating network pharmacology approaches. Specifically, we sought to uncover key molecular targets for Aloe vera and validate their roles in keloid pathogenesis. METHODS: We integrated keloid transcriptome datasets (GSE218007 and GSE237752) by merging GEO data, and identifying differentially expressed genes (DEGs). Functional enrichment analysis (GO, GSEA) and machine learning approaches were applied to select diagnostic biomarkers. Candidate genes were validated via Receiver Operating Characteristic (ROC) curves in training and independent cohorts (GSE44270). PPI networks and Cytohubba algorithms identified hub genes, while TCMSP-screened compounds from Aloe vera were docked with targets using molecular docking. RESULTS: 91 Identified DEGs enriched in fibrosis-related pathways. Machine learning prioritized two diagnostic biomarkers: AMPH and TNFRSF9 (AUC > 0.85 in training/testing). PPI analysis revealed IL6 as a hub gene. Aloe vera-derived quercetin targeted HAS2 and IL6 (both P < 0.05 in validation), with molecular docking confirming stable binding (binding energy