ArticleFrontiers in immunology2022
Identification of fatty acid metabolism-related molecular subtype biomarkers and their correlation with immune checkpoints in cutaneous melanoma.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 14 citations in OpenAlex.
- The multifaceted roles of the ACSL family in cancer: Metabolic reprogramming, ferroptosis regulation and tumour immune microenvironment remodelling.Clinical and translational medicine · 2026Review
- Cancer immunoediting in the melanoma tumor microenvironment: from immune elimination to therapeutic resistance.Frontiers in immunology · 2026Review
- Tumoral ALOX5 mediated arachidonic acid metabolism regulates immune response in non-small cell lung cancer.Cellular oncology (Dordrecht, Netherlands) · 2025Article
- Selection signature analysis in chickens divergently selected for growth rate reveals novel candidate genes regulating fat deposition.BMC genomics · 2025Article
- CD74 is a potential biomarker predicting the response to immune checkpoint blockade.Cancer cell international · 2024Article
- Obesity enhances the response to neoadjuvant anti-PD1 therapy in oral tongue squamous cell carcinoma.Cancer medicine · 2024Article
- Review
- Leveraging FAM features to predict the prognosis of LGG patients and immunotherapy outcome.American journal of cancer research · 2024Article
- Current knowledge about immunotherapy resistance for melanoma and potential predictive and prognostic biomarkers.Cancer drug resistance (Alhambra, Calif.) · 2024Review
- Article
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10 authors at 2 institutions in 2 countries.
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Abstract
Purpose: Fatty acid metabolism (FAM) affects the immune phenotype in a metabolically dynamic tumor microenvironment (TME), but the use of FAM-related genes (FAMGs) to predict the prognosis and immunotherapy response of cutaneous melanoma (CM) patients has not been investigated. In this study, we aimed to construct FAM molecular subtypes and identify key prognostic biomarkers in CM. Methods: We used a CM dataset in The Cancer Genome Atlas (TCGA) to construct FAM molecular subtypes. We performed Kaplan-Meier (K-M) analysis, gene set enrichment analysis (GSEA), and TME analysis to assess differences in the prognosis and immune phenotype between subtypes. We used weighted gene co-expression network analysis (WGCNA) to identify key biomarkers that regulate tumor metabolism and immunity between the subtypes. We compared overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) between CM patients with high or low biomarker expression. We applied univariable and multivariable Cox analyses to verify the independent prognostic value of the FAM biomarkers. We used GSEA and TME analysis to investigate the immune-related regulation mechanism of the FAM subtype biomarker. We evaluated the immune checkpoint inhibition (ICI) response and chemotherapy sensitivity between CM patients with high or low biomarker expression. We performed real-time fluorescent quantitative PCR (qRT-PCR) and semi-quantitative analysis of the immunohistochemical (IHC) data from the Human Protein Atlas to evaluate the mRNA and protein expression levels of the FAM biomarkers in CM. Results: We identified 2 FAM molecular subtypes (cluster 1 and cluster 2). K-M analysis showed that cluster 2 had better OS and PFS than cluster 1 did. GSEA showed that, compared with cluster 1, cluster 2 had significantly upregulated immune response pathways. The TME analysis indicated that immune cell subpopulations and immune functions were highly enriched in cluster 2 as compared with cluster 1. WGCNA identified 6 hub genes ( Conclusion: Our FAM subtypes verify that different FAM reprogramming affects the function and phenotype of infiltrating immune cells in the CM TME. The FAM molecular subtype biomarkers can be independent predictors of prognosis and immunotherapy response in CM patients.
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