BMC medical genomics · 2025
Identification of hub genes contributing to acute eczema using transcriptome sequencing and machine-learning approaches.
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Zhao Yingying, Wang Wenxiu, Li Xinying, Ye Xiaona, Han Xingjun
Аннотация
BACKGROUND: Acute eczema (AE) is a multifactorial inflammatory skin disease with complex immune dysregulation. The identification of key pathogenic genes may provide novel biomarkers and therapeutic targets.
METHODS: An AE rat model was induced using DNCB. Transcriptome sequencing was performed on skin lesions, and differentially expressed genes (DEGs) were identified via DESeq2. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network construction, functional enrichment, and "Friends" analysis were applied to screen hub genes. Multiple machine learning algorithms, including LASSO, support vector machine (SVM), and random forest (RF), were integrated for candidate selection. Quantitative real-time PCR (qRT-PCR), Western blotting, and immunohistochemistry were used for validation.
RESULTS: A total of 1717 DEGs (1190 upregulated, 527 downregulated) were identified. WGCNA and PPI analysis yielded 36 hub genes enriched in immune and inflammatory pathways, particularly Th1/Th2/Th17 differentiation, JAK-STAT, and PI3K-Akt signaling. Cross-validation by machine learning highlighted Icam1 as a top candidate. Experimental assays confirmed significant upregulation of ICAM-1 at mRNA and protein levels in AE lesions compared with controls.
CONCLUSION: Icam1 is a key gene potentially driving inflammatory infiltration in AE and may serve as a diagnostic target. This integrative bioinformatics-experimental approach provides a robust framework for discovering pathogenic genes in inflammatory skin diseases.
METHODS: An AE rat model was induced using DNCB. Transcriptome sequencing was performed on skin lesions, and differentially expressed genes (DEGs) were identified via DESeq2. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network construction, functional enrichment, and "Friends" analysis were applied to screen hub genes. Multiple machine learning algorithms, including LASSO, support vector machine (SVM), and random forest (RF), were integrated for candidate selection. Quantitative real-time PCR (qRT-PCR), Western blotting, and immunohistochemistry were used for validation.
RESULTS: A total of 1717 DEGs (1190 upregulated, 527 downregulated) were identified. WGCNA and PPI analysis yielded 36 hub genes enriched in immune and inflammatory pathways, particularly Th1/Th2/Th17 differentiation, JAK-STAT, and PI3K-Akt signaling. Cross-validation by machine learning highlighted Icam1 as a top candidate. Experimental assays confirmed significant upregulation of ICAM-1 at mRNA and protein levels in AE lesions compared with controls.
CONCLUSION: Icam1 is a key gene potentially driving inflammatory infiltration in AE and may serve as a diagnostic target. This integrative bioinformatics-experimental approach provides a robust framework for discovering pathogenic genes in inflammatory skin diseases.