Title: Identification of key abnormally expressed gene modules and pathways in keloid via weighted gene co-expression network analysis

Authors: Jun Chai; Yan Zhang

Addresses: Department of Plastic and Reconstructive Surgery, Suzhou Municipal Hospital, Suzhou, China; Affiliated to: Suzhou Hospital of Nanjing Medical University, Gusu School of Nanjing Medical University, China ' Department of General Surgery, Suzhou Municipal Hospital, Suzhou, China; Affiliated to: Suzhou Hospital of Nanjing Medical University, Gusu School of Nanjing Medical University, China

Abstract: Keloid is a common human skin disease that is related to the environment, heredity, tension, lifestyles, and other factors. However, the complicated molecular mechanisms of keloid formation are still unknown. Thus, this study aimed to identify the key abnormally expressed genes and functionally enriched pathways in keloid. The gene expression profiles of GSE173900 and GSE92566 were downloaded from the GEO database and analysed by 'limma' and 'WGCNA' package in R 4.1.1 to identify the DEGs and key modules associated with keloid. Then, enrichment analysis was performed on the basis of GO and KEGG pathway, and protein-protein interaction network was constructed using the overlapped genes of DEGs. We confirmed the top 5 hub genes and performed immune infiltration analysis and survival curve. CCR5, CYBB, MNDA, PLEK, and PTPRC were identified as the top five hub genes. Immune infiltration analysis showed that M1 macrophages were significantly correlated with keloid formation.

Keywords: keloid; weighted gene co-expression network analysis; WGCNA; immune infiltration analysis; integrated bioinformatics analysis.

DOI: 10.1504/IJDMB.2025.148968

International Journal of Data Mining and Bioinformatics, 2025 Vol.29 No.4, pp.451 - 472

Received: 20 Dec 2023
Accepted: 09 Aug 2024
Published online: 06 Oct 2025 *

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