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Identification of Hypoxia–Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma
Introduction: Kidney renal clear cell carcinoma (KIRC), a kind of malignant disease, is a severe threat to public health. Tracking the information of tumor progression and conducting a related dynamic prognosis model are necessary for KIRC. It is crucial to identify hypoxia–immune-related genes and...
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/PMC8860910/ https://www.ncbi.nlm.nih.gov/pubmed/35211477 http://dx.doi.org/10.3389/fcell.2021.796156 |
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author | Bai, Shuheng Chen, Ling Yan, Yanli Wang, Xuan Jiang, Aimin Li, Rong Kang, Haojing Feng, Zhaode Li, Guangzu Ma, Wen Zhang, Jiangzhou Ren, Juan |
author_facet | Bai, Shuheng Chen, Ling Yan, Yanli Wang, Xuan Jiang, Aimin Li, Rong Kang, Haojing Feng, Zhaode Li, Guangzu Ma, Wen Zhang, Jiangzhou Ren, Juan |
author_sort | Bai, Shuheng |
collection | PubMed |
description | Introduction: Kidney renal clear cell carcinoma (KIRC), a kind of malignant disease, is a severe threat to public health. Tracking the information of tumor progression and conducting a related dynamic prognosis model are necessary for KIRC. It is crucial to identify hypoxia–immune-related genes and construct a prognostic model due to immune interaction and the influence of hypoxia in the prognosis of patients with KIRC. Methods: The hypoxia and immune status of KIRC patients were identified by utilizing t-SNE and ImmuCellAI for gene expression data. COX and Lasso regression were used to identify some hypoxia–immune-related signature genes and further construct a prognostic risk model based on these genes. Internal and external validations were also conducted to construct a prognostic model. Finally, some potentially effective drugs were screened by the CMap dataset. Results: We found that high-hypoxia and low-immune status tend to induce poor overall survival (OS). Six genes, including PLAUR, UCN, PABPC1L, SLC16A12, NFE2L3, and KCNAB1, were identified and involved in our hypoxia–immune-related prognostic risk model. Internal verification showed that the area under the curve (AUC) for the constructed models for 1-, 3-, 4-, and 5-year OS were 0.768, 0.754, 0.775, and 0.792, respectively. For the external verification, the AUC for 1-, 3-, 4-, and 5-year OS were 0.768, 0.739, 0.763, and 0.643 respectively. Furthermore, the decision curve analysis findings demonstrated excellent clinical effectiveness. Finally, we found that four drugs (including vorinostat, fludroxycortide, oxolinic acid, and flutamide) might be effective and efficient in alleviating or reversing the status of severe hypoxia and poor infiltration of immune cells. Conclusion: Our constructed prognostic model, based on hypoxia–immune-related genes, has excellent effectiveness and clinical application value. Moreover, some small-molecule drugs are screened to alleviate severe hypoxia and poor infiltration of immune cells. |
format | Online Article Text |
id | pubmed-8860910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88609102022-02-23 Identification of Hypoxia–Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma Bai, Shuheng Chen, Ling Yan, Yanli Wang, Xuan Jiang, Aimin Li, Rong Kang, Haojing Feng, Zhaode Li, Guangzu Ma, Wen Zhang, Jiangzhou Ren, Juan Front Cell Dev Biol Cell and Developmental Biology Introduction: Kidney renal clear cell carcinoma (KIRC), a kind of malignant disease, is a severe threat to public health. Tracking the information of tumor progression and conducting a related dynamic prognosis model are necessary for KIRC. It is crucial to identify hypoxia–immune-related genes and construct a prognostic model due to immune interaction and the influence of hypoxia in the prognosis of patients with KIRC. Methods: The hypoxia and immune status of KIRC patients were identified by utilizing t-SNE and ImmuCellAI for gene expression data. COX and Lasso regression were used to identify some hypoxia–immune-related signature genes and further construct a prognostic risk model based on these genes. Internal and external validations were also conducted to construct a prognostic model. Finally, some potentially effective drugs were screened by the CMap dataset. Results: We found that high-hypoxia and low-immune status tend to induce poor overall survival (OS). Six genes, including PLAUR, UCN, PABPC1L, SLC16A12, NFE2L3, and KCNAB1, were identified and involved in our hypoxia–immune-related prognostic risk model. Internal verification showed that the area under the curve (AUC) for the constructed models for 1-, 3-, 4-, and 5-year OS were 0.768, 0.754, 0.775, and 0.792, respectively. For the external verification, the AUC for 1-, 3-, 4-, and 5-year OS were 0.768, 0.739, 0.763, and 0.643 respectively. Furthermore, the decision curve analysis findings demonstrated excellent clinical effectiveness. Finally, we found that four drugs (including vorinostat, fludroxycortide, oxolinic acid, and flutamide) might be effective and efficient in alleviating or reversing the status of severe hypoxia and poor infiltration of immune cells. Conclusion: Our constructed prognostic model, based on hypoxia–immune-related genes, has excellent effectiveness and clinical application value. Moreover, some small-molecule drugs are screened to alleviate severe hypoxia and poor infiltration of immune cells. Frontiers Media S.A. 2022-02-08 /pmc/articles/PMC8860910/ /pubmed/35211477 http://dx.doi.org/10.3389/fcell.2021.796156 Text en Copyright © 2022 Bai, Chen, Yan, Wang, Jiang, Li, Kang, Feng, Li, Ma, Zhang and Ren. 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 | Cell and Developmental Biology Bai, Shuheng Chen, Ling Yan, Yanli Wang, Xuan Jiang, Aimin Li, Rong Kang, Haojing Feng, Zhaode Li, Guangzu Ma, Wen Zhang, Jiangzhou Ren, Juan Identification of Hypoxia–Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma |
title | Identification of Hypoxia–Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma |
title_full | Identification of Hypoxia–Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma |
title_fullStr | Identification of Hypoxia–Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma |
title_full_unstemmed | Identification of Hypoxia–Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma |
title_short | Identification of Hypoxia–Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma |
title_sort | identification of hypoxia–immune-related gene signatures and construction of a prognostic model in kidney renal clear cell carcinoma |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860910/ https://www.ncbi.nlm.nih.gov/pubmed/35211477 http://dx.doi.org/10.3389/fcell.2021.796156 |
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