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Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma
PURPOSE: The present study aimed to establish a hypoxia related genes model to predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data accessed from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database. METHODS: Patients’ data wer...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8818738/ https://www.ncbi.nlm.nih.gov/pubmed/35141153 http://dx.doi.org/10.3389/fonc.2021.806264 |
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author | Ning, Xiang-hui Li, Ning-yang Qi, Yuan-yuan Li, Song-chao Jia, Zhan-kui Yang, Jin-jian |
author_facet | Ning, Xiang-hui Li, Ning-yang Qi, Yuan-yuan Li, Song-chao Jia, Zhan-kui Yang, Jin-jian |
author_sort | Ning, Xiang-hui |
collection | PubMed |
description | PURPOSE: The present study aimed to establish a hypoxia related genes model to predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data accessed from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database. METHODS: Patients’ data were downloaded from the TCGA and ICGC databases, and hypoxia related genes were accessed from the Molecular Signatures Database. The differentially expressed genes were evaluated and then the differential expressions hypoxia genes were screened. The TCGA cohort was randomly divided into a discovery TCGA cohort and a validation TCGA cohort. The discovery TCGA cohort was used for constructing the hypoxia genes risk model through Lasso regression, univariate and multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were used to assess the reliability and sensitivity of our model. Then, we established a nomogram to predict the probable one-, three-, and five-year overall survival rates. Lastly, the Tumor Immune Dysfunction and Exclusion (TIDE) score of patients was calculated. RESULTS: We established a six hypoxia-related gene prognostic model of KIRC patients in the TCGA database and validated in the ICGC database. The patients with high riskscore present poorer prognosis than those with low riskscore in the three TCGA cohorts and ICGC cohort. ROC curves show our six-gene model with a robust predictive capability in these four cohorts. In addition, we constructed a nomogram for KIRC patients in the TCGA database. Finally, the high risk-group had a high TIDE score than the patients with low riskscore. CONCLUSIONS: We established a six hypoxia-related gene risk model for independent prediction of the prognosis of KIRC patients was established and constructed a robust nomogram. The different riskscores might be a biomarker for immunotherapy strategy. |
format | Online Article Text |
id | pubmed-8818738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88187382022-02-08 Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma Ning, Xiang-hui Li, Ning-yang Qi, Yuan-yuan Li, Song-chao Jia, Zhan-kui Yang, Jin-jian Front Oncol Oncology PURPOSE: The present study aimed to establish a hypoxia related genes model to predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data accessed from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database. METHODS: Patients’ data were downloaded from the TCGA and ICGC databases, and hypoxia related genes were accessed from the Molecular Signatures Database. The differentially expressed genes were evaluated and then the differential expressions hypoxia genes were screened. The TCGA cohort was randomly divided into a discovery TCGA cohort and a validation TCGA cohort. The discovery TCGA cohort was used for constructing the hypoxia genes risk model through Lasso regression, univariate and multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were used to assess the reliability and sensitivity of our model. Then, we established a nomogram to predict the probable one-, three-, and five-year overall survival rates. Lastly, the Tumor Immune Dysfunction and Exclusion (TIDE) score of patients was calculated. RESULTS: We established a six hypoxia-related gene prognostic model of KIRC patients in the TCGA database and validated in the ICGC database. The patients with high riskscore present poorer prognosis than those with low riskscore in the three TCGA cohorts and ICGC cohort. ROC curves show our six-gene model with a robust predictive capability in these four cohorts. In addition, we constructed a nomogram for KIRC patients in the TCGA database. Finally, the high risk-group had a high TIDE score than the patients with low riskscore. CONCLUSIONS: We established a six hypoxia-related gene risk model for independent prediction of the prognosis of KIRC patients was established and constructed a robust nomogram. The different riskscores might be a biomarker for immunotherapy strategy. Frontiers Media S.A. 2022-01-24 /pmc/articles/PMC8818738/ /pubmed/35141153 http://dx.doi.org/10.3389/fonc.2021.806264 Text en Copyright © 2022 Ning, Li, Qi, Li, Jia and Yang 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 | Oncology Ning, Xiang-hui Li, Ning-yang Qi, Yuan-yuan Li, Song-chao Jia, Zhan-kui Yang, Jin-jian Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma |
title | Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma |
title_full | Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma |
title_fullStr | Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma |
title_full_unstemmed | Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma |
title_short | Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma |
title_sort | identification of a hypoxia-related gene model for predicting the prognosis and formulating the treatment strategies in kidney renal clear cell carcinoma |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8818738/ https://www.ncbi.nlm.nih.gov/pubmed/35141153 http://dx.doi.org/10.3389/fonc.2021.806264 |
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