TGM1 as a novel signature gene in psoriasis identified by integrative bioinformatics and experimental validation

Psoriasis is a systemic immune‑mediated skin disease, typically considered to be incurable. Identification of meaningful biomarkers has been a notable challenge in psoriasis prevention and management. The present study aimed to determine the signature genes driving psoriasis and their underlying mechanism. Microarray datasets of psoriasis were obtained from the Gene Expression Omnibus database, and the differentially expressed genes (DEGs) were identified using the 'limma' R package. Gene Set Enrichment Analysis (GSEA) was performed using the 'clusterProfiler' R tool. Functional and pathway enrichment of DEGs were analyzed using a bioinformatics website (Wei Sheng Xin). Furthermore, the present study applied least absolute shrinkage and selection operator regression, random forest and support vector machine‑recursive feature elimination techniques to pinpoint signature genes driving psoriasis. Subsequently, CIBERSORT was used to determine whether psoriasis‑infiltrating immune cells had a strong connection with signature genes. Immunohistochemistry (IHC) was used to demonstrate the expression of TGM1 in human psoriasis samples. Cell transfection was employed to verify the function of TGM1. The top 163 significant DEGs were identified from the GSE30999 dataset, and Kyoto Encyclopedia of Genes and Genomes analysis illustrated that these genes were mostly involved in 'viral protein interaction with cytokine and cytokine receptor', as well as the 'IL‑17 signaling pathway'. The present study screened transglutaminase 1 (TGM1) as a signature gene by combining three machine learning algorithms. Through single‑gene GSEA, the present study further revealed that TGM1 was associated with 'GF‑RTK‑PI3K signaling pathway' and 'cytokine‑JAK‑STAT signaling pathway', providing valuable insights into the underlying mechanism of psoriasis. Additionally, the present study validated TGM1 expression in the GSE53552 and GSE13355 datasets, and demonstrated its elevated expression in lesional psoriatic skin using IHC. Finally, TGM1 overexpression was demonstrated to increase the expression levels of inflammatory factors and keratinocyte differentiation markers, whereas knockdown decreased their expression, especially IL‑1β, S100A8, S100A9 and K1. Together, these findings suggest that TGM1 could be a promising therapeutic target for psoriasis, highlighting its potential application in psoriasis therapy.

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Publication Details

Journal
Molecular Medicine Reports
Published
2025-11-20
DOI
https://doi.org/10.3892/mmr.2025.13755
Primary Topic
Psoriasis: Treatment and Pathogenesis
Type
article
Field-Weighted Citation Impact
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article

TGM1 as a novel signature gene in psoriasis identified by integrative bioinformatics and experimental validation

Qiping Yuan, Lizhi Hu, Qianyu Zhu, Junkai Huang et al.
Molecular Medicine Reports
Psoriasis: Treatment and Pathogenesis
article

TGM1 as a novel signature gene in psoriasis identified by integrative bioinformatics and experimental validation

Qiping Yuan, Lizhi Hu, Qianyu Zhu, Junkai Huang, Jing Luo, Jingyu Zhang, Bingxin Zhang, Pan Guo, Mengke Sun, Hong Cui
article en

Abstract

Psoriasis is a systemic immune‑mediated skin disease, typically considered to be incurable. Identification of meaningful biomarkers has been a notable challenge in psoriasis prevention and management. The present study aimed to determine the signature genes driving psoriasis and their underlying mechanism. Microarray datasets of psoriasis were obtained from the Gene Expression Omnibus database, and the differentially expressed genes (DEGs) were identified using the 'limma' R package. Gene Set Enrichment Analysis (GSEA) was performed using the 'clusterProfiler' R tool. Functional and pathway enrichment of DEGs were analyzed using a bioinformatics website (Wei Sheng Xin). Furthermore, the present study applied least absolute shrinkage and selection operator regression, random forest and support vector machine‑recursive feature elimination techniques to pinpoint signature genes driving psoriasis. Subsequently, CIBERSORT was used to determine whether psoriasis‑infiltrating immune cells had a strong connection with signature genes. Immunohistochemistry (IHC) was used to demonstrate the expression of TGM1 in human psoriasis samples. Cell transfection was employed to verify the function of TGM1. The top 163 significant DEGs were identified from the GSE30999 dataset, and Kyoto Encyclopedia of Genes and Genomes analysis illustrated that these genes were mostly involved in 'viral protein interaction with cytokine and cytokine receptor', as well as the 'IL‑17 signaling pathway'. The present study screened transglutaminase 1 (TGM1) as a signature gene by combining three machine learning algorithms. Through single‑gene GSEA, the present study further revealed that TGM1 was associated with 'GF‑RTK‑PI3K signaling pathway' and 'cytokine‑JAK‑STAT signaling pathway', providing valuable insights into the underlying mechanism of psoriasis. Additionally, the present study validated TGM1 expression in the GSE53552 and GSE13355 datasets, and demonstrated its elevated expression in lesional psoriatic skin using IHC. Finally, TGM1 overexpression was demonstrated to increase the expression levels of inflammatory factors and keratinocyte differentiation markers, whereas knockdown decreased their expression, especially IL‑1β, S100A8, S100A9 and K1. Together, these findings suggest that TGM1 could be a promising therapeutic target for psoriasis, highlighting its potential application in psoriasis therapy.

Molecular Medicine ReportsVol. 33(1)
First Teaching Hospital of Tianjin University of Traditional Chinese Medicine (CN), Tianjin Medical University (CN)
Openalex Percentile: Top 62%
Psoriasis: Treatment and Pathogenesis
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