ArticleFrontiers in oncology2020
Hierarchical Clustering of Cutaneous Melanoma Based on Immunogenomic Profiling.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Characterization of Tumor Microenvironment and Prognosis of Regulatory T cells-Related Subtypes.Current medicinal chemistry · 2026Article
- Heat shock protein family A member 8 is a prognostic marker for bladder cancer: Evidences based on experiments and machine learning.Journal of cellular and molecular medicine · 2023Article
- Glycine Decarboxylase (GLDC) Plays a Crucial Role in Regulating Energy Metabolism, Invasion, Metastasis and Immune Escape for Prostate Cancer.International journal of biological sciences · 2023Article
- An immune-related prognostic signature for thyroid carcinoma to predict survival and response to immune checkpoint inhibitors.Cancer immunology, immunotherapy : CII · 2022Article
- Comprehensive analysis of ECHDC3 as a potential biomarker and therapeutic target for acute myeloid leukemia: Bioinformatic analysis and experimental verification.Frontiers in oncology · 2022Article
- Comprehensive exploration of tumor immune microenvironment feature and therapeutic response in colorectal cancer based on a novel immune-related long non-coding RNA prognostic signature.Frontiers in genetics · 2022Article
- Immunogenomic-Based Analysis of Hierarchical Clustering of Diffuse Large Cell Lymphoma.Journal of immunology research · 2022Article
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Authors and funding
6 authors.
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Abstract
Cutaneous melanoma is an aggressive malignancy with high heterogeneity. Several studies have been performed to identify cutaneous melanoma subtypes based on genomic profiling. However, few classifications based on assessments of immune-associated genes have limited clinical implications for cutaneous melanoma. Using 470 cutaneous melanoma samples from The Cancer Genome Atlas (TCGA), we calculated the enrichment levels of 29 immune-associated gene sets in each sample and hierarchically clustered them into Immunity High (Immunity_H, n=323, 68.7%), Immunity Medium (Immunity_M, n=135, 28.7%), and Immunity Low (Immunity_L, n=12, 2.6%) based on the ssGSEA score. The ESTIMATE algorithm was used to calculate stromal scores (range: -1,800.51-1,901.99), immune scores (range: -1,476.28-3,780.33), estimate scores (range: -2,618.28-5,098.14) and tumor purity (range: 0.216-0.976) and they were significantly correlated with immune subtypes (Kruskal-Wallis test,
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