ArticleFrontiers in immunology2024
Integrating bioinformatics and machine learning to identify AhR-related gene signatures for prognosis and tumor microenvironment modulation in melanoma.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Transcriptomic and experimental validation identify biomarkers and regulatory mechanisms associated with the aryl hydrocarbon receptor in nasopharyngeal carcinoma.Translational cancer research · 2026Article
- Bioinformatics-driven genome-wide identification of viral miRNAs in high spillover bat coronaviruses and their target genes in human.Archives of microbiology · 2026Article
- Network analysis-guided drug repurposing: IGF1R as a novel melanoma target and therapeutic potential of dapagliflozin.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026Article
- Challenges and opportunities for the next generation of computational tumor models.PLoS biology · 2025Article
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Authors and funding
2 authors.
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Abstract
Background: The Aryl Hydrocarbon Receptor (AhR) pathway significantly influences immune cell regulation, impacting the effectiveness of immunotherapy and patient outcomes in melanoma. However, the specific downstream targets and mechanisms by which AhR influences melanoma remain insufficiently understood. Methods: Melanoma samples from The Cancer Genome Atlas (TCGA) and normal skin tissues from the Genotype-Tissue Expression (GTEx) database were analyzed to identify differentially expressed genes, which were intersected with a curated list of AhR-related pathway genes. Prognostic models were subsequently developed, and feature genes were identified. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis, were employed to explore the biological significance of these genes. The stability of the machine learning models and the relationship between gene expression and immune infiltrating cells were validated using three independent melanoma datasets. A mouse melanoma model was used to validate the dynamic changes of the feature genes during tumor progression. The relationship between the selected genes and drug sensitivity, as well as non-coding RNA interactions, was thoroughly investigated. Results: Our analysis identified a robust prognostic model, with four AhR-related genes (MAP2K1, PRKACB, KLF5, and PIK3R2) emerging as key contributors to melanoma progression. GSEA revealed that these genes are involved in primary immunodeficiency. Immune cell infiltration analysis demonstrated enrichment of CD4 Conclusions: This study presents a comprehensive analysis of AhR-related genes in melanoma, highlighting MAP2K1, PRKACB, KLF5, and PIK3R2 as key prognostic markers and potential therapeutic targets. The integration of bioinformatics and machine learning provides a robust framework for enhancing prognostic evaluation in melanoma patients and offers new avenues for the development of treatments, particularly for those resistant to current immunotherapies.
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