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Predicting the clinical outcome of melanoma using an immune-related gene pairs signature

OBJECTIVE: Melanoma is rare but dangerous skin cancer, and it can spread rather quickly in the advanced stages of the tumor. Abundant evidence suggests the relationship between tumor development and progression and the immune system. A robust gene risk model could provide an accurate prediction of c...

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Autores principales: Meng, Liangliang, He, Xiaoxi, Zhang, Xiao, Zhang, Xiaobo, Wei, Yingtian, Wu, Bin, Li, Wei, Li, Jing, Xiao, Yueyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544036/
https://www.ncbi.nlm.nih.gov/pubmed/33031392
http://dx.doi.org/10.1371/journal.pone.0240331
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author Meng, Liangliang
He, Xiaoxi
Zhang, Xiao
Zhang, Xiaobo
Wei, Yingtian
Wu, Bin
Li, Wei
Li, Jing
Xiao, Yueyong
author_facet Meng, Liangliang
He, Xiaoxi
Zhang, Xiao
Zhang, Xiaobo
Wei, Yingtian
Wu, Bin
Li, Wei
Li, Jing
Xiao, Yueyong
author_sort Meng, Liangliang
collection PubMed
description OBJECTIVE: Melanoma is rare but dangerous skin cancer, and it can spread rather quickly in the advanced stages of the tumor. Abundant evidence suggests the relationship between tumor development and progression and the immune system. A robust gene risk model could provide an accurate prediction of clinical outcomes. The present study aimed to explore a robust signature of immune-related gene pairs (IRGPs) for estimating overall survival (OS) in malignant melanoma. METHODS: Clinical and genetic data of skin cutaneous melanoma (SKCM) patients from The Cancer Genome Atlas (TCGA) was performed as a training dataset to identify candidate IRGPs for the prognosis of melanoma. Two independent datasets from the Gene Expression Omnibus (GEO) database (GSE65904) and TCGA dataset (TCGA-UVM) were selected for external validation. Univariate and multivariate Cox regression analyses were then performed to explore the prognostic power of the IRGPs signature and other clinical factors. CIBERSORTx was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues. RESULTS: A signature consisted of 33 IRGPs was established which was significantly associated with patients’ survival in the TCGA-SKCM dataset (P = 2.0×10(−16), Hazard Ratio (HR) = 4.220 (2.909 to 6.122)). We found the IRGPs signature exhibited an independent prognostic factor in all the three independent cohorts in both the univariate and multivariate Cox analysis (P<0.01). The prognostic efficacy of the signature remained unaffected regardless of whether BRAF or NRAS was mutated. As expected, the results were verified in the GSE65904 dataset and the TCGA-UVM dataset. We found an apparent shorter OS in patients of the high-risk group in the GSE65904 dataset (P = 2.1×10(−3); HR = 1.988 (1.309 to 3.020)). The trend in the results of the survival analysis in TCGA-UVM was as we expected, but the result was not statistically significant (P = 0.117, HR = 4.263 (1.407 to 12.91)). CD8 T cells, activated dendritic cells (DCs), regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group in the TCGA-SKCM dataset(P <0.01). CONCLUSION: The results of the present study support the IRGPs signature as a promising marker for prognosis prediction in melanoma.
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spelling pubmed-75440362020-10-19 Predicting the clinical outcome of melanoma using an immune-related gene pairs signature Meng, Liangliang He, Xiaoxi Zhang, Xiao Zhang, Xiaobo Wei, Yingtian Wu, Bin Li, Wei Li, Jing Xiao, Yueyong PLoS One Research Article OBJECTIVE: Melanoma is rare but dangerous skin cancer, and it can spread rather quickly in the advanced stages of the tumor. Abundant evidence suggests the relationship between tumor development and progression and the immune system. A robust gene risk model could provide an accurate prediction of clinical outcomes. The present study aimed to explore a robust signature of immune-related gene pairs (IRGPs) for estimating overall survival (OS) in malignant melanoma. METHODS: Clinical and genetic data of skin cutaneous melanoma (SKCM) patients from The Cancer Genome Atlas (TCGA) was performed as a training dataset to identify candidate IRGPs for the prognosis of melanoma. Two independent datasets from the Gene Expression Omnibus (GEO) database (GSE65904) and TCGA dataset (TCGA-UVM) were selected for external validation. Univariate and multivariate Cox regression analyses were then performed to explore the prognostic power of the IRGPs signature and other clinical factors. CIBERSORTx was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues. RESULTS: A signature consisted of 33 IRGPs was established which was significantly associated with patients’ survival in the TCGA-SKCM dataset (P = 2.0×10(−16), Hazard Ratio (HR) = 4.220 (2.909 to 6.122)). We found the IRGPs signature exhibited an independent prognostic factor in all the three independent cohorts in both the univariate and multivariate Cox analysis (P<0.01). The prognostic efficacy of the signature remained unaffected regardless of whether BRAF or NRAS was mutated. As expected, the results were verified in the GSE65904 dataset and the TCGA-UVM dataset. We found an apparent shorter OS in patients of the high-risk group in the GSE65904 dataset (P = 2.1×10(−3); HR = 1.988 (1.309 to 3.020)). The trend in the results of the survival analysis in TCGA-UVM was as we expected, but the result was not statistically significant (P = 0.117, HR = 4.263 (1.407 to 12.91)). CD8 T cells, activated dendritic cells (DCs), regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group in the TCGA-SKCM dataset(P <0.01). CONCLUSION: The results of the present study support the IRGPs signature as a promising marker for prognosis prediction in melanoma. Public Library of Science 2020-10-08 /pmc/articles/PMC7544036/ /pubmed/33031392 http://dx.doi.org/10.1371/journal.pone.0240331 Text en © 2020 Meng et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Meng, Liangliang
He, Xiaoxi
Zhang, Xiao
Zhang, Xiaobo
Wei, Yingtian
Wu, Bin
Li, Wei
Li, Jing
Xiao, Yueyong
Predicting the clinical outcome of melanoma using an immune-related gene pairs signature
title Predicting the clinical outcome of melanoma using an immune-related gene pairs signature
title_full Predicting the clinical outcome of melanoma using an immune-related gene pairs signature
title_fullStr Predicting the clinical outcome of melanoma using an immune-related gene pairs signature
title_full_unstemmed Predicting the clinical outcome of melanoma using an immune-related gene pairs signature
title_short Predicting the clinical outcome of melanoma using an immune-related gene pairs signature
title_sort predicting the clinical outcome of melanoma using an immune-related gene pairs signature
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544036/
https://www.ncbi.nlm.nih.gov/pubmed/33031392
http://dx.doi.org/10.1371/journal.pone.0240331
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