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

Abstract

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 <−7 kcal/mol). IL6 emerged as both a key network hub and a therapeutic target, linking keloid and TCM mechanisms. Conclusion AMPH and TNFRSF9 are promising diagnostic biomarkers for keloid, while quercetin from Aloe vera targets HAS2 and IL6, offering therapeutic potential. The dual role of IL6 underscores its centrality in keloid pathogenesis, connecting bioinformatics predictions with TCM pharmacology. This study provides a foundation for clinical prediction and targeted treatment strategies.