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A new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma
This study aimed to explore the potential role of autophagy-related genes in kidney renal clear cell carcinoma (KIRC) and develop a new prognostic-related risk model. In our research, we used multiple bioinformatics methods to perform a pan-cancer analysis of the CNV, SNV, mRNA expression, and overa...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806698/ https://www.ncbi.nlm.nih.gov/pubmed/34636718 http://dx.doi.org/10.1080/21655979.2021.1976050 |
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author | Wu, Guangzhen Xu, Yingkun Zhang, Huayu Ruan, Zihao Zhang, Peizhi Wang, Zicheng Gao, Han Che, Xiangyu Xia, Qinghua Chen, Feng |
author_facet | Wu, Guangzhen Xu, Yingkun Zhang, Huayu Ruan, Zihao Zhang, Peizhi Wang, Zicheng Gao, Han Che, Xiangyu Xia, Qinghua Chen, Feng |
author_sort | Wu, Guangzhen |
collection | PubMed |
description | This study aimed to explore the potential role of autophagy-related genes in kidney renal clear cell carcinoma (KIRC) and develop a new prognostic-related risk model. In our research, we used multiple bioinformatics methods to perform a pan-cancer analysis of the CNV, SNV, mRNA expression, and overall survival of autophagy-related genes, and displayed the results in the form of heat maps. We then performed cluster analysis and LASSO regression analysis on these autophagy-related genes in KIRC. In the cluster analysis, we successfully divided patients with KIRC into five clusters and found that there was a clear correlation between the classification and two clinicopathological features: tumor, and stage. In LASSO regression analysis, we used 13 genes to create a new prognostic-related risk model in KIRC. The model showed that the survival rate of patients with KIRC in the high-risk group was significantly lower than that in the low-risk group, and that there was a correlation between this grouping and the patients’ metastasis, tumor, stage, grade, and fustat. The results of the ROC curve suggested that this model has good prediction accuracy. The results of multivariate Cox analysis show that the risk score of this model can be used as an independent risk factor for patients with KIRC. In summary, we believe that this research provides valuable data supporting future clinical treatment and scientific research. |
format | Online Article Text |
id | pubmed-8806698 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-88066982022-02-02 A new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma Wu, Guangzhen Xu, Yingkun Zhang, Huayu Ruan, Zihao Zhang, Peizhi Wang, Zicheng Gao, Han Che, Xiangyu Xia, Qinghua Chen, Feng Bioengineered Research Paper This study aimed to explore the potential role of autophagy-related genes in kidney renal clear cell carcinoma (KIRC) and develop a new prognostic-related risk model. In our research, we used multiple bioinformatics methods to perform a pan-cancer analysis of the CNV, SNV, mRNA expression, and overall survival of autophagy-related genes, and displayed the results in the form of heat maps. We then performed cluster analysis and LASSO regression analysis on these autophagy-related genes in KIRC. In the cluster analysis, we successfully divided patients with KIRC into five clusters and found that there was a clear correlation between the classification and two clinicopathological features: tumor, and stage. In LASSO regression analysis, we used 13 genes to create a new prognostic-related risk model in KIRC. The model showed that the survival rate of patients with KIRC in the high-risk group was significantly lower than that in the low-risk group, and that there was a correlation between this grouping and the patients’ metastasis, tumor, stage, grade, and fustat. The results of the ROC curve suggested that this model has good prediction accuracy. The results of multivariate Cox analysis show that the risk score of this model can be used as an independent risk factor for patients with KIRC. In summary, we believe that this research provides valuable data supporting future clinical treatment and scientific research. Taylor & Francis 2021-10-12 /pmc/articles/PMC8806698/ /pubmed/34636718 http://dx.doi.org/10.1080/21655979.2021.1976050 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://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/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Paper Wu, Guangzhen Xu, Yingkun Zhang, Huayu Ruan, Zihao Zhang, Peizhi Wang, Zicheng Gao, Han Che, Xiangyu Xia, Qinghua Chen, Feng A new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma |
title | A new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma |
title_full | A new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma |
title_fullStr | A new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma |
title_full_unstemmed | A new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma |
title_short | A new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma |
title_sort | new prognostic risk model based on autophagy-related genes in kidney renal clear cell carcinoma |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806698/ https://www.ncbi.nlm.nih.gov/pubmed/34636718 http://dx.doi.org/10.1080/21655979.2021.1976050 |
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