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Construction of an EMT‐Related Prognostic Model for Melanoma and Functional Validation of the EMT‐Associated Gene BGN

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The content is shared for informational and educational purposes only: ownership and responsibility remain with the original author and source, and this information is no substitute for medical advice. We thank Pigment Cell & Melanoma Research for their valuable work: you can read the original article on their website.

Pigment Cell &Melanoma Research, Volume 39, Issue 6, November 2026.

Pubblicato: 21/10/2026

Construction of an EMT-Related Prognostic Model for Melanoma and Functional Validation of the EMT-Associated Gene BGN

An epithelial–mesenchymal transition (EMT)-related six-gene signature (LAMC2, IL32, MGP, CAP2, ECM1 and BGN) was constructed using TCGA-SKCM and GTEx data and validated in the GSE65904 cohort. High-risk patients had shorter overall survival, and the risk groups differed in inferred immune infiltration. BGN was upregulated in melanoma tissues and cell lines; BGN knockdown suppressed proliferation, migration, invasion, EMT-related marker changes and xenograft growth, whereas BGN overexpression produced the opposite effects in vitro.

ABSTRACT

Melanoma is a highly aggressive skin tumour that poses considerable challenges in treatment and prognostic assessment. Epithelial–mesenchymal transition (EMT) is a critical process in melanoma progression and contributes to tumour invasion and metastasis. This study aimed to identify EMT-related genes associated with melanoma prognosis and to develop a risk model for patient stratification. Based on previous studies, an EMT-related gene set comprising 200 genes was compiled. Gene expression data from normal skin and skin cutaneous melanoma tissues were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Differential expression analysis was performed to identify differentially expressed genes (DEGs), using an adjusted p value < 0.05 as the significance threshold. Univariable Cox regression analysis was subsequently conducted to identify survival-related genes among the DEGs (p < 0.05), resulting in the selection of six genes: LAMC2, IL32, MGP, CAP2, ECM1 and BGN. A risk score model was then constructed based on these genes. The model showed prognostic stratification ability in both the TCGA cohort and the external validation cohort GSE65904. Patients were classified into high- and low-risk groups, and overall survival was significantly shorter in the high-risk group than in the low-risk group (p < 0.0001). Analysis of the immune microenvironment showed higher levels of immune cell infiltration in the low-risk group. BGN was significantly overexpressed in melanoma tissues and cell lines. In vitro experiments demonstrated that BGN silencing inhibited the proliferation, migration and invasion of A375 cells and reversed EMT-related changes, whereas BGN overexpression enhanced the malignant phenotype of A2058 cells. In vivo experiments further confirmed that BGN knockdown significantly suppressed xenograft tumour growth and altered the expression of EMT-related markers. These findings establish an EMT-related prognostic model for melanoma risk stratification and provide experimental evidence supporting the potential role of BGN in melanoma progression. This study integrates computational analysis with functional validation; however, further validation in independent prospective cohorts is required before clinical application.

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