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Data mining of immune-related prognostic genes in metastatic melanoma microenvironment.
- Source :
-
Bioscience reports [Biosci Rep] 2020 Nov 27; Vol. 40 (11). - Publication Year :
- 2020
-
Abstract
- Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein-protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.<br /> (© 2020 The Author(s).)
- Subjects :
- Algorithms
Biomarkers, Tumor metabolism
Databases, Genetic
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Lymphocytes, Tumor-Infiltrating metabolism
Male
Melanoma immunology
Melanoma mortality
Melanoma secondary
Middle Aged
Prognosis
Protein Interaction Maps
Risk Assessment
Risk Factors
Signal Transduction
Skin Neoplasms immunology
Skin Neoplasms mortality
Skin Neoplasms pathology
T-Lymphocytes metabolism
Biomarkers, Tumor genetics
Data Mining
Lymphocytes, Tumor-Infiltrating immunology
Melanoma genetics
Skin Neoplasms genetics
T-Lymphocytes immunology
Transcriptome
Tumor Microenvironment
Subjects
Details
- Language :
- English
- ISSN :
- 1573-4935
- Volume :
- 40
- Issue :
- 11
- Database :
- MEDLINE
- Journal :
- Bioscience reports
- Publication Type :
- Academic Journal
- Accession number :
- 33169786
- Full Text :
- https://doi.org/10.1042/BSR20201704