Cargando…
A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a commonly occurring tumor. Through a deeper understanding of the immune regulatory mechanisms in the tumor microenvironment, immunotherapy may serve as a potential treatment for cancer patients. This study aimed at identifying the survival-rela...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754506/ https://www.ncbi.nlm.nih.gov/pubmed/35035230 http://dx.doi.org/10.2147/IJGM.S341801 |
_version_ | 1784632284447506432 |
---|---|
author | Li, Jianpeng Cao, Jinlong Li, Pan Deng, Ran Yao, Zhiqiang Ying, Lijun Tian, Junqiang |
author_facet | Li, Jianpeng Cao, Jinlong Li, Pan Deng, Ran Yao, Zhiqiang Ying, Lijun Tian, Junqiang |
author_sort | Li, Jianpeng |
collection | PubMed |
description | BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a commonly occurring tumor. Through a deeper understanding of the immune regulatory mechanisms in the tumor microenvironment, immunotherapy may serve as a potential treatment for cancer patients. This study aimed at identifying the survival-related immune cells and hub genes, which could be potential targets for immunotherapy in ccRCC. METHODS: The gene expression profiles and clinical data of ccRCC patients were extracted from UCSC Xena and Gene Expression Omnibus (GEO) databases. Kaplan–Meier (KM) survival and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were utilized to select the survival-related tumor-infiltrating immune cells. Multivariate Cox regression was utilized to develop a signature based on the tumor-infiltrating immune cells (TIICs). Based on the signature, the risk score was calculated, following which the samples were divided into high-risk and low-risk groups. Differentially expressed genes (DEGs) between the two risk groups were identified. Functional enrichment analysis was performed and cytoHubba plug-in of Cytoscape was used to identify the hub genes. Multiple datasets were utilized to validate these hub genes, including the Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and the Human Protein Atlas (HPA), and the GEO datasets. Finally, a correlation analysis was performed to evaluate the relationship between the hub genes and TIICs. RESULTS: Four immune survival-related cells, including T cell CD4 memory-activated, T cell regulatory (Tregs), eosinophils, and mast cell resting were identified. Nine immune-specific hub genes were identified, which included APOE, CASR, CTLA4, CXCL8, EGF, F2, KNG1, MMP9, and IL6. Furthermore, these hub genes were significantly correlated with clinical traits and closely associated with some TIICs. CONCLUSION: A total of four survival-related immune cell types and nine hub genes were found to be closely associated with ccRCC. These findings may have implications for the development of novel potential immunotherapeutic targets for ccRCC. |
format | Online Article Text |
id | pubmed-8754506 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-87545062022-01-13 A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases Li, Jianpeng Cao, Jinlong Li, Pan Deng, Ran Yao, Zhiqiang Ying, Lijun Tian, Junqiang Int J Gen Med Original Research BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a commonly occurring tumor. Through a deeper understanding of the immune regulatory mechanisms in the tumor microenvironment, immunotherapy may serve as a potential treatment for cancer patients. This study aimed at identifying the survival-related immune cells and hub genes, which could be potential targets for immunotherapy in ccRCC. METHODS: The gene expression profiles and clinical data of ccRCC patients were extracted from UCSC Xena and Gene Expression Omnibus (GEO) databases. Kaplan–Meier (KM) survival and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were utilized to select the survival-related tumor-infiltrating immune cells. Multivariate Cox regression was utilized to develop a signature based on the tumor-infiltrating immune cells (TIICs). Based on the signature, the risk score was calculated, following which the samples were divided into high-risk and low-risk groups. Differentially expressed genes (DEGs) between the two risk groups were identified. Functional enrichment analysis was performed and cytoHubba plug-in of Cytoscape was used to identify the hub genes. Multiple datasets were utilized to validate these hub genes, including the Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and the Human Protein Atlas (HPA), and the GEO datasets. Finally, a correlation analysis was performed to evaluate the relationship between the hub genes and TIICs. RESULTS: Four immune survival-related cells, including T cell CD4 memory-activated, T cell regulatory (Tregs), eosinophils, and mast cell resting were identified. Nine immune-specific hub genes were identified, which included APOE, CASR, CTLA4, CXCL8, EGF, F2, KNG1, MMP9, and IL6. Furthermore, these hub genes were significantly correlated with clinical traits and closely associated with some TIICs. CONCLUSION: A total of four survival-related immune cell types and nine hub genes were found to be closely associated with ccRCC. These findings may have implications for the development of novel potential immunotherapeutic targets for ccRCC. Dove 2022-01-08 /pmc/articles/PMC8754506/ /pubmed/35035230 http://dx.doi.org/10.2147/IJGM.S341801 Text en © 2022 Li et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Li, Jianpeng Cao, Jinlong Li, Pan Deng, Ran Yao, Zhiqiang Ying, Lijun Tian, Junqiang A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases |
title | A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases |
title_full | A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases |
title_fullStr | A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases |
title_full_unstemmed | A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases |
title_short | A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases |
title_sort | bioinformatic analysis of immune-related prognostic genes in clear cell renal cell carcinoma based on tcga and geo databases |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754506/ https://www.ncbi.nlm.nih.gov/pubmed/35035230 http://dx.doi.org/10.2147/IJGM.S341801 |
work_keys_str_mv | AT lijianpeng abioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT caojinlong abioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT lipan abioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT dengran abioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT yaozhiqiang abioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT yinglijun abioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT tianjunqiang abioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT lijianpeng bioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT caojinlong bioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT lipan bioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT dengran bioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT yaozhiqiang bioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT yinglijun bioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases AT tianjunqiang bioinformaticanalysisofimmunerelatedprognosticgenesinclearcellrenalcellcarcinomabasedontcgaandgeodatabases |