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Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer

BACKGROUND: Rectal cancer (RC) is one of the most common malignant tumors. Ferroptosis is an iron-dependent form of cell death, which plays an important role in various cancers. However, the correlation between ferroptosis-related genes (FRGs) and prognosis in RC remains unclear. METHODS: Gene expre...

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Autores principales: Shi, Wei-Kun, Liu, Yu-Xin, Qiu, Xiao-Yuan, Zhou, Jing-Ya, Zhou, Jiao-Lin, Lin, Guo-Le
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9682793/
https://www.ncbi.nlm.nih.gov/pubmed/36414988
http://dx.doi.org/10.1186/s12864-022-08996-6
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author Shi, Wei-Kun
Liu, Yu-Xin
Qiu, Xiao-Yuan
Zhou, Jing-Ya
Zhou, Jiao-Lin
Lin, Guo-Le
author_facet Shi, Wei-Kun
Liu, Yu-Xin
Qiu, Xiao-Yuan
Zhou, Jing-Ya
Zhou, Jiao-Lin
Lin, Guo-Le
author_sort Shi, Wei-Kun
collection PubMed
description BACKGROUND: Rectal cancer (RC) is one of the most common malignant tumors. Ferroptosis is an iron-dependent form of cell death, which plays an important role in various cancers. However, the correlation between ferroptosis-related genes (FRGs) and prognosis in RC remains unclear. METHODS: Gene expression data from The Cancer Genome Atlas Rectum adenocarcinoma (TCGA-READ) and GSE87211 were downloaded. Clustering and functional enrichment were evaluated. A FRGs risk score was established based on the univariate Cox analysis and the Least absolute shrinkage and selection operator (LASSO) analysis. K-M analysis and ROC analysis were conducted to determine prognostic values. qRT-PCR was performed to validate levels of mRNA expression. Multivariate Cox analysis was used to build a prognostic prediction model based on the risk score. RESULTS: Based on FRGs, RC patients were grouped into two clusters. In the functional enrichment of differentially expressed genes between the two clusters, immune-related pathways dominated. A novel FRGs signature with 14 genes related to the overall survival (OS) of RC was established. qRT-PCR of the 14 genes identified TP63, ISCU, PLIN4, MAP3K5, OXSR, FANCD2 and ATM were overexpressed in RC tissue; HSPB1, MAPK1, ABCC1, PANX1, MAPK9 and ATG7 were underexpressed; TUBE1 had no difference. The high-risk group had a significantly lower OS than the low-risk group (P < 0.001), and ROC curve analysis confirmed the signature’s predictive capacity. Multivariate analysis demonstrated that the risk score and age were independent prognostic factors. CONCLUSION: A novel FRGs model can be used to predict the prognosis in RC, as well as to guide individual treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08996-6.
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spelling pubmed-96827932022-11-24 Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer Shi, Wei-Kun Liu, Yu-Xin Qiu, Xiao-Yuan Zhou, Jing-Ya Zhou, Jiao-Lin Lin, Guo-Le BMC Genomics Research BACKGROUND: Rectal cancer (RC) is one of the most common malignant tumors. Ferroptosis is an iron-dependent form of cell death, which plays an important role in various cancers. However, the correlation between ferroptosis-related genes (FRGs) and prognosis in RC remains unclear. METHODS: Gene expression data from The Cancer Genome Atlas Rectum adenocarcinoma (TCGA-READ) and GSE87211 were downloaded. Clustering and functional enrichment were evaluated. A FRGs risk score was established based on the univariate Cox analysis and the Least absolute shrinkage and selection operator (LASSO) analysis. K-M analysis and ROC analysis were conducted to determine prognostic values. qRT-PCR was performed to validate levels of mRNA expression. Multivariate Cox analysis was used to build a prognostic prediction model based on the risk score. RESULTS: Based on FRGs, RC patients were grouped into two clusters. In the functional enrichment of differentially expressed genes between the two clusters, immune-related pathways dominated. A novel FRGs signature with 14 genes related to the overall survival (OS) of RC was established. qRT-PCR of the 14 genes identified TP63, ISCU, PLIN4, MAP3K5, OXSR, FANCD2 and ATM were overexpressed in RC tissue; HSPB1, MAPK1, ABCC1, PANX1, MAPK9 and ATG7 were underexpressed; TUBE1 had no difference. The high-risk group had a significantly lower OS than the low-risk group (P < 0.001), and ROC curve analysis confirmed the signature’s predictive capacity. Multivariate analysis demonstrated that the risk score and age were independent prognostic factors. CONCLUSION: A novel FRGs model can be used to predict the prognosis in RC, as well as to guide individual treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08996-6. BioMed Central 2022-11-22 /pmc/articles/PMC9682793/ /pubmed/36414988 http://dx.doi.org/10.1186/s12864-022-08996-6 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
Shi, Wei-Kun
Liu, Yu-Xin
Qiu, Xiao-Yuan
Zhou, Jing-Ya
Zhou, Jiao-Lin
Lin, Guo-Le
Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer
title Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer
title_full Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer
title_fullStr Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer
title_full_unstemmed Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer
title_short Construction and validation of a novel Ferroptosis-related gene signature predictive model in rectal Cancer
title_sort construction and validation of a novel ferroptosis-related gene signature predictive model in rectal cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9682793/
https://www.ncbi.nlm.nih.gov/pubmed/36414988
http://dx.doi.org/10.1186/s12864-022-08996-6
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