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Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this s...

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Autores principales: Shen, Xin, Shang, Lifeng, Han, Junwei, Zhang, Yi, Niu, Wenkai, Liu, Haiwang, Shi, Hai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845246/
https://www.ncbi.nlm.nih.gov/pubmed/36685954
http://dx.doi.org/10.3389/fgene.2022.1095867
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author Shen, Xin
Shang, Lifeng
Han, Junwei
Zhang, Yi
Niu, Wenkai
Liu, Haiwang
Shi, Hai
author_facet Shen, Xin
Shang, Lifeng
Han, Junwei
Zhang, Yi
Niu, Wenkai
Liu, Haiwang
Shi, Hai
author_sort Shen, Xin
collection PubMed
description Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this study, data on patients with Skin cutaneous melanoma were downloaded from the GEO, TCGA, and GTEx databases. Genes associated with the immune pathway were screened from published papers and lncRNAs associated with them were identified. We performed immune microenvironment and functional enrichment analyses. The analysis was followed by applying univariate/multivariate Cox regression algorithms to finally identify three lncRNAs associated with the immune pathway for the construction of prognostic prediction models (CXCL10, RXRG, and SCG2). This stepwise downscaling method, which finally screens out prognostic factors and key genes and then uses them to build a risk model, has excellent predictive power. According to analyses of the model’s reliability, it was able to differentiate the prognostic value and continued existence of Skin cutaneous melanoma patient populations more effectively. This study is an analysis of the immune pathway that leads lncRNAs in Skin cutaneous melanoma in an effort to open up new treatment avenues for Skin cutaneous melanoma.
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spelling pubmed-98452462023-01-19 Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma Shen, Xin Shang, Lifeng Han, Junwei Zhang, Yi Niu, Wenkai Liu, Haiwang Shi, Hai Front Genet Genetics Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this study, data on patients with Skin cutaneous melanoma were downloaded from the GEO, TCGA, and GTEx databases. Genes associated with the immune pathway were screened from published papers and lncRNAs associated with them were identified. We performed immune microenvironment and functional enrichment analyses. The analysis was followed by applying univariate/multivariate Cox regression algorithms to finally identify three lncRNAs associated with the immune pathway for the construction of prognostic prediction models (CXCL10, RXRG, and SCG2). This stepwise downscaling method, which finally screens out prognostic factors and key genes and then uses them to build a risk model, has excellent predictive power. According to analyses of the model’s reliability, it was able to differentiate the prognostic value and continued existence of Skin cutaneous melanoma patient populations more effectively. This study is an analysis of the immune pathway that leads lncRNAs in Skin cutaneous melanoma in an effort to open up new treatment avenues for Skin cutaneous melanoma. Frontiers Media S.A. 2023-01-04 /pmc/articles/PMC9845246/ /pubmed/36685954 http://dx.doi.org/10.3389/fgene.2022.1095867 Text en Copyright © 2023 Shen, Shang, Han, Zhang, Niu, Liu and Shi. 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 Genetics
Shen, Xin
Shang, Lifeng
Han, Junwei
Zhang, Yi
Niu, Wenkai
Liu, Haiwang
Shi, Hai
Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma
title Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma
title_full Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma
title_fullStr Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma
title_full_unstemmed Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma
title_short Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma
title_sort immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845246/
https://www.ncbi.nlm.nih.gov/pubmed/36685954
http://dx.doi.org/10.3389/fgene.2022.1095867
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