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Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature
BACKGROUND: Melanoma is a highly heterogeneous and aggressive cutaneous malignancy. Ferroptosis, a new pathway of cell death depending on the intracellar iron, has been shown to be significantly associated with apoptosis of a number of tumors, including melanoma. Nevertheless, the relationship betwe...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8969311/ https://www.ncbi.nlm.nih.gov/pubmed/35354376 http://dx.doi.org/10.1186/s12864-022-08475-y |
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author | Chen, Yuxuan Guo, Linlin Zhou, Zijie An, Ran Wang, Jiecong |
author_facet | Chen, Yuxuan Guo, Linlin Zhou, Zijie An, Ran Wang, Jiecong |
author_sort | Chen, Yuxuan |
collection | PubMed |
description | BACKGROUND: Melanoma is a highly heterogeneous and aggressive cutaneous malignancy. Ferroptosis, a new pathway of cell death depending on the intracellar iron, has been shown to be significantly associated with apoptosis of a number of tumors, including melanoma. Nevertheless, the relationship between ferroptosis-related genes (FRGs) and the melanoma patients’ prognosis needs to be explored. METHODS: Download expression profiles of FRGs and clinical data from The Cancer Genome Atlas (TCGA) database. 70% data were randomly selected from the TCGA database and utilized the univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model to create a prognostic model, and the remaining 30% was used to validate the predictive power of the model. In addition, GSE65904 and GSE22153 date sets as the verification cohort to testify the predictive ability of the signature. RESULTS: We identified nine FRGs relating with melanoma patients’ overall survival (OS) and established a prognostic model based on their expression. During the research, patients were divided into group of high-risk and low-risk according to the results of LASSO regression analysis. Survival time was significantly longer in the low-risk group than that of in the high-risk group (P < 0.001). Enrichment analysis of different risk groups demonstrated that the reasons for the difference were related to immune-related pathways, and the degree of immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group. CONCLUSIONS: The FRG prognostic model we established can predict the prognosis of melanoma patients and may further guide subsequent treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08475-y. |
format | Online Article Text |
id | pubmed-8969311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89693112022-04-01 Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature Chen, Yuxuan Guo, Linlin Zhou, Zijie An, Ran Wang, Jiecong BMC Genomics Research BACKGROUND: Melanoma is a highly heterogeneous and aggressive cutaneous malignancy. Ferroptosis, a new pathway of cell death depending on the intracellar iron, has been shown to be significantly associated with apoptosis of a number of tumors, including melanoma. Nevertheless, the relationship between ferroptosis-related genes (FRGs) and the melanoma patients’ prognosis needs to be explored. METHODS: Download expression profiles of FRGs and clinical data from The Cancer Genome Atlas (TCGA) database. 70% data were randomly selected from the TCGA database and utilized the univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model to create a prognostic model, and the remaining 30% was used to validate the predictive power of the model. In addition, GSE65904 and GSE22153 date sets as the verification cohort to testify the predictive ability of the signature. RESULTS: We identified nine FRGs relating with melanoma patients’ overall survival (OS) and established a prognostic model based on their expression. During the research, patients were divided into group of high-risk and low-risk according to the results of LASSO regression analysis. Survival time was significantly longer in the low-risk group than that of in the high-risk group (P < 0.001). Enrichment analysis of different risk groups demonstrated that the reasons for the difference were related to immune-related pathways, and the degree of immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group. CONCLUSIONS: The FRG prognostic model we established can predict the prognosis of melanoma patients and may further guide subsequent treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08475-y. BioMed Central 2022-03-30 /pmc/articles/PMC8969311/ /pubmed/35354376 http://dx.doi.org/10.1186/s12864-022-08475-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Chen, Yuxuan Guo, Linlin Zhou, Zijie An, Ran Wang, Jiecong Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature |
title | Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature |
title_full | Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature |
title_fullStr | Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature |
title_full_unstemmed | Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature |
title_short | Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature |
title_sort | identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8969311/ https://www.ncbi.nlm.nih.gov/pubmed/35354376 http://dx.doi.org/10.1186/s12864-022-08475-y |
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