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Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma

Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal component...

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Autores principales: Qin, Rujia, Peng, Wen, Wang, Xuemin, Li, Chunyan, Xi, Yan, Zhong, Zhaoming, Sun, Chuanzheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202075/
https://www.ncbi.nlm.nih.gov/pubmed/34136377
http://dx.doi.org/10.3389/fonc.2021.615963
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author Qin, Rujia
Peng, Wen
Wang, Xuemin
Li, Chunyan
Xi, Yan
Zhong, Zhaoming
Sun, Chuanzheng
author_facet Qin, Rujia
Peng, Wen
Wang, Xuemin
Li, Chunyan
Xi, Yan
Zhong, Zhaoming
Sun, Chuanzheng
author_sort Qin, Rujia
collection PubMed
description Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein–protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify “real” hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.
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spelling pubmed-82020752021-06-15 Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma Qin, Rujia Peng, Wen Wang, Xuemin Li, Chunyan Xi, Yan Zhong, Zhaoming Sun, Chuanzheng Front Oncol Oncology Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein–protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify “real” hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM. Frontiers Media S.A. 2021-05-28 /pmc/articles/PMC8202075/ /pubmed/34136377 http://dx.doi.org/10.3389/fonc.2021.615963 Text en Copyright © 2021 Qin, Peng, Wang, Li, Xi, Zhong and Sun https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Qin, Rujia
Peng, Wen
Wang, Xuemin
Li, Chunyan
Xi, Yan
Zhong, Zhaoming
Sun, Chuanzheng
Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma
title Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma
title_full Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma
title_fullStr Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma
title_full_unstemmed Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma
title_short Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma
title_sort identification of genes related to immune infiltration in the tumor microenvironment of cutaneous melanoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202075/
https://www.ncbi.nlm.nih.gov/pubmed/34136377
http://dx.doi.org/10.3389/fonc.2021.615963
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