Bioinformatics insights into transcriptomic biomarkers for atopic dermatitis

Year 2025, Volume: 29 Issue: 3, 892 - 902, 04.06.2025

Wirawan Adikusuma Eko Mugiyanto Lalu Muhammad Irham Firdayani Firdayani , Shelvi Listiana Muthia Rahayu Iresha Ayu Masyita Maulida Mazaya Riza Alfian

https://doi.org/10.12991/jrespharm.1693724

Abstract

Atopic dermatitis (AD) is a long-term inflammatory skin condition characterized by a complex interplay of genetic and molecular factors. Understanding the underlying transcriptomic changes can aid in identifying biomarkers for diagnosis and therapeutic targets. This study aimed to discover and characterize transcriptomic biomarkers in AD using bioinformatics tools and techniques. Two pre-existing datasets, GSE6012 and GSE16161, were analyzed using the R limma package to identify differentially expressed genes (DEGs). Gene Ontology (GO) and REACTOME pathway enrichment analyses were conducted using WebGestalt 2019 to explore the biological properties and pathways associated with the identified genes. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, with MCODE and CytoHubba plugins used to identify significant gene clusters and hub genes. The analysis identified 352 DEGs (158 upregulated, 194 downregulated) in GSE6012 and 5451 DEGs (2962 upregulated, 2489 downregulated) in GSE16161, with 226 overlapping genes. GO enrichment analysis revealed significant roles in cell proliferation, epidermis development, and immune response. REACTOME pathway analysis highlighted significant modifications in pathways related to skin structure and immune defense, including cornified envelope formation and antimicrobial peptides. The PPI network analysis identified three primary subclusters and pinpointed APOE and STAT1 as key hub genes. This research offers an understanding of the transcriptomic biomarkers of AD. The identified DEGs, enriched biological functions, pathways, and key hub genes offer valuable information for understanding AD's molecular mechanisms and potential therapeutic targets.

Keywords

Atopic dermatitis , bioinformatics , biomarkers , transcriptomics

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