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Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA)
BACKGROUND: We aimed to establish an immune-related gene (IRG) based signature that could provide guidance for clinical bladder cancer (BC) prognostic surveillance. METHODS: Differentially expressed IRGs and transcription factors (TFs) between BCs and normal tissues were extracted from transcriptome...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658175/ https://www.ncbi.nlm.nih.gov/pubmed/33209669 http://dx.doi.org/10.21037/tau-20-696 |
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author | Xing, Qianwei Liu, Shouyong Jiang, Silin Li, Tao Wang, Zengjun Wang, Yi |
author_facet | Xing, Qianwei Liu, Shouyong Jiang, Silin Li, Tao Wang, Zengjun Wang, Yi |
author_sort | Xing, Qianwei |
collection | PubMed |
description | BACKGROUND: We aimed to establish an immune-related gene (IRG) based signature that could provide guidance for clinical bladder cancer (BC) prognostic surveillance. METHODS: Differentially expressed IRGs and transcription factors (TFs) between BCs and normal tissues were extracted from transcriptome data downloaded from the TCGA database. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to identify related pathways based on differently expressed IRGs. Then, univariate Cox regression analysis was performed to investigate IRGs with prognostic values and LASSO penalized Cox regression analysis was utilized to develop the prognostic index (PI) model. RESULTS: A total of 411 BC tissue samples and 19 normal bladder tissues in the TCGA database were enrolled in this study and 259 differentially expressed IRGs were identified. Networks between TFs and IRGs were also provided to seek the upstream regulators of differentially expressed IRGs. By means of univariate Cox regression analysis, 57 IRGs were analyzed with prognostic values and 10 IRGs were finally identified by LASSO penalized Cox regression analysis to construct the PI model. This model could significantly classified BC patients into high-risk group and low-risk group in terms of OS (P=9.923e-07) and its AUC reached 0.711. By means of univariate and multivariate COX regression analysis, this PI was proven to be a valuable independent prognostic factor (HR =1.119, 95% CI =1.066–1.175, P<0.001). CMap database analysis was also utilized to screen out 10 small molecules drugs with the potential for the treatment of BC. CONCLUSIONS: Our study successfully provided a novel PI based on IRGs with the potential to predict the prognosis of BC and screened out 10 small molecules drugs with the potential to treat BC. Besides, networks between TFs and IRGs were also displayed to seek its upstream regulators for future researches. |
format | Online Article Text |
id | pubmed-7658175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-76581752020-11-17 Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA) Xing, Qianwei Liu, Shouyong Jiang, Silin Li, Tao Wang, Zengjun Wang, Yi Transl Androl Urol Original Article BACKGROUND: We aimed to establish an immune-related gene (IRG) based signature that could provide guidance for clinical bladder cancer (BC) prognostic surveillance. METHODS: Differentially expressed IRGs and transcription factors (TFs) between BCs and normal tissues were extracted from transcriptome data downloaded from the TCGA database. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to identify related pathways based on differently expressed IRGs. Then, univariate Cox regression analysis was performed to investigate IRGs with prognostic values and LASSO penalized Cox regression analysis was utilized to develop the prognostic index (PI) model. RESULTS: A total of 411 BC tissue samples and 19 normal bladder tissues in the TCGA database were enrolled in this study and 259 differentially expressed IRGs were identified. Networks between TFs and IRGs were also provided to seek the upstream regulators of differentially expressed IRGs. By means of univariate Cox regression analysis, 57 IRGs were analyzed with prognostic values and 10 IRGs were finally identified by LASSO penalized Cox regression analysis to construct the PI model. This model could significantly classified BC patients into high-risk group and low-risk group in terms of OS (P=9.923e-07) and its AUC reached 0.711. By means of univariate and multivariate COX regression analysis, this PI was proven to be a valuable independent prognostic factor (HR =1.119, 95% CI =1.066–1.175, P<0.001). CMap database analysis was also utilized to screen out 10 small molecules drugs with the potential for the treatment of BC. CONCLUSIONS: Our study successfully provided a novel PI based on IRGs with the potential to predict the prognosis of BC and screened out 10 small molecules drugs with the potential to treat BC. Besides, networks between TFs and IRGs were also displayed to seek its upstream regulators for future researches. AME Publishing Company 2020-10 /pmc/articles/PMC7658175/ /pubmed/33209669 http://dx.doi.org/10.21037/tau-20-696 Text en 2020 Translational Andrology and Urology. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Xing, Qianwei Liu, Shouyong Jiang, Silin Li, Tao Wang, Zengjun Wang, Yi Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA) |
title | Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA) |
title_full | Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA) |
title_fullStr | Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA) |
title_full_unstemmed | Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA) |
title_short | Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA) |
title_sort | prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (blca) |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658175/ https://www.ncbi.nlm.nih.gov/pubmed/33209669 http://dx.doi.org/10.21037/tau-20-696 |
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