ArticleJournal of Cancer2026
Comprehensive Characterization and Prognostic Modeling of Efferocytosis-Related Genes in Cutaneous Melanoma.
Article in Journal of Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
Funding
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Abstract
Background: Cutaneous melanoma (CM) is a highly aggressive skin cancer with poor prognosis in advanced stages. Efferocytosis, the process by which apoptotic cells are cleared by phagocytes, plays a dual role in tumor immunity and progression. However, the comprehensive role of efferocytosis-related genes (EFRGs) in CM remains unclear. Methods: We conducted a multi-omics analysis by integrating transcriptomic data from TCGA, GEO and GTEx databases, identifying differentially expressed genes and performing WGCNA to define EFRG signatures. Based on the expression profiles of differentially expressed EFRGs, we identified molecular subtypes, evaluated immune infiltration using ESTIMATE and ssGSEA. A prognostic EFRG risk scoring model was further constructed and validated using multiple machine learning algorithms. Western blot, CCK8 assay, colony formation assay and Transwell assays were performed to validate the functional role of the screened EFRG. Results: 21 DE-EFRGs with significant prognostic value were identified and three distinct molecular subtypes were subsequently defined. The EFRG-based scoring model effectively stratified patients into high- and low-risk groups with distinct survival outcomes and immune microenvironment characteristics. The low-risk group exhibited a more immune-activated phenotype and greater sensitivity to immunotherapy and chemotherapeutic agents. Conclusions: Our study comprehensively characterizes EFRG patterns in CM and proposes a robust EFRG-based prognostic model.
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