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A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma
Kidney renal clear cell carcinoma (KIRC) is the leading cause of kidney cancer-related deaths. Currently, there are no studies in tumor immunology investigating the use of signatures as a predictor of overall survival in KIRC patients. Our study attempts to establish an immune-related gene risk sign...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603789/ https://www.ncbi.nlm.nih.gov/pubmed/33194402 http://dx.doi.org/10.7717/peerj.10183 |
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author | Zou, Yong Hu, Chuan |
author_facet | Zou, Yong Hu, Chuan |
author_sort | Zou, Yong |
collection | PubMed |
description | Kidney renal clear cell carcinoma (KIRC) is the leading cause of kidney cancer-related deaths. Currently, there are no studies in tumor immunology investigating the use of signatures as a predictor of overall survival in KIRC patients. Our study attempts to establish an immune-related gene risk signature to predict clinical outcomes in KIRC. A total of 528 patients from The Cancer Genome Atlas (TCGA) database were included in our analysis and randomly divided into training (n = 315) and testing sets (n = 213). We collected 1,534 immune-related genes from the Immunology Database and Analysis Portal as candidates to construct our signature. LASSO-COX was used to find gene models with the highest predictive ability. We used survival and Cox analysis to test the model’s independent prognostic ability. Univariate analysis identified 650 immune-related genes with prognostic abilities. After 1,000 iterations, we choose 14 of the most frequent and stable immune-related genes as our signature. We found that the signature was associated with M stage, T stage, and pathological staging. More importantly, the signature can independently predict clinical prognosis in KIRC patients. Gene Set Enrichment Analysis (GSEA) showed an association between our signature and critical metabolism pathways. Our research established a model based upon 14 immune-related genes that predicted the prognosis of KIRC patients based on tumor immune microenvironments. |
format | Online Article Text |
id | pubmed-7603789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76037892020-11-12 A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma Zou, Yong Hu, Chuan PeerJ Bioinformatics Kidney renal clear cell carcinoma (KIRC) is the leading cause of kidney cancer-related deaths. Currently, there are no studies in tumor immunology investigating the use of signatures as a predictor of overall survival in KIRC patients. Our study attempts to establish an immune-related gene risk signature to predict clinical outcomes in KIRC. A total of 528 patients from The Cancer Genome Atlas (TCGA) database were included in our analysis and randomly divided into training (n = 315) and testing sets (n = 213). We collected 1,534 immune-related genes from the Immunology Database and Analysis Portal as candidates to construct our signature. LASSO-COX was used to find gene models with the highest predictive ability. We used survival and Cox analysis to test the model’s independent prognostic ability. Univariate analysis identified 650 immune-related genes with prognostic abilities. After 1,000 iterations, we choose 14 of the most frequent and stable immune-related genes as our signature. We found that the signature was associated with M stage, T stage, and pathological staging. More importantly, the signature can independently predict clinical prognosis in KIRC patients. Gene Set Enrichment Analysis (GSEA) showed an association between our signature and critical metabolism pathways. Our research established a model based upon 14 immune-related genes that predicted the prognosis of KIRC patients based on tumor immune microenvironments. PeerJ Inc. 2020-10-29 /pmc/articles/PMC7603789/ /pubmed/33194402 http://dx.doi.org/10.7717/peerj.10183 Text en ©2020 Zou and Hu https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Zou, Yong Hu, Chuan A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma |
title | A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma |
title_full | A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma |
title_fullStr | A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma |
title_full_unstemmed | A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma |
title_short | A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma |
title_sort | 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603789/ https://www.ncbi.nlm.nih.gov/pubmed/33194402 http://dx.doi.org/10.7717/peerj.10183 |
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