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Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers
BACKGROUND: Cellular senescence plays an essential role in the development and progression of end-stage renal disease (ESRD). However, the detailed mechanisms phenomenon remains unclear. METHODS: The mRNA expression profiling dataset GSE37171 was taken from the Gene Expression Omnibus (GEO) database...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408218/ https://www.ncbi.nlm.nih.gov/pubmed/37553608 http://dx.doi.org/10.1186/s12882-023-03285-0 |
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author | Xi, Yu-jia Guo, Qiang Zhang, Ran Duan, Guo-sheng Zhang, Sheng-xiao |
author_facet | Xi, Yu-jia Guo, Qiang Zhang, Ran Duan, Guo-sheng Zhang, Sheng-xiao |
author_sort | Xi, Yu-jia |
collection | PubMed |
description | BACKGROUND: Cellular senescence plays an essential role in the development and progression of end-stage renal disease (ESRD). However, the detailed mechanisms phenomenon remains unclear. METHODS: The mRNA expression profiling dataset GSE37171 was taken from the Gene Expression Omnibus (GEO) database. The cell senescence-associated hub genes were selected by applying protein–protein interaction (PPI), followed by correlation analysis, gene interaction analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We next explored the relationships of hub genes with miRNAs, TFs, and diseases. The absolute abundance of eight immune cells and two stromal cells were calculated by MCPcount and the correlation of hub genes with these ten cells was analyzed. Lasso was used to selecting for trait genes. ROC curves and DCA decision curves were used to assess the accuracy and predictive power of the trait genes. RESULTS: A total of 65 cellular senescence signature genes were identified among patients and controls. The PPI network screened out ten hub genes. GO and KEGG indicated that ten hub genes were associated with ESRD progression. Transcription factor gene interactions and common regulatory networks of miRNAs were also identified in the datasets. The hub genes were significantly correlated with immune cells and stromal cells. Then the lasso model was constructed to screen out the five most relevant signature genes (FOS, FOXO3, SIRT1, TP53, SMARCA4). The area under the ROC curve (AUC) showed that these five characteristic genes have good resolving power for the diagnostic model. CONCLUSIONS: Our findings suggested that cellular senescence-associated genes played an important role in the development of ESRD and immune regulation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-023-03285-0. |
format | Online Article Text |
id | pubmed-10408218 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104082182023-08-09 Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers Xi, Yu-jia Guo, Qiang Zhang, Ran Duan, Guo-sheng Zhang, Sheng-xiao BMC Nephrol Research BACKGROUND: Cellular senescence plays an essential role in the development and progression of end-stage renal disease (ESRD). However, the detailed mechanisms phenomenon remains unclear. METHODS: The mRNA expression profiling dataset GSE37171 was taken from the Gene Expression Omnibus (GEO) database. The cell senescence-associated hub genes were selected by applying protein–protein interaction (PPI), followed by correlation analysis, gene interaction analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We next explored the relationships of hub genes with miRNAs, TFs, and diseases. The absolute abundance of eight immune cells and two stromal cells were calculated by MCPcount and the correlation of hub genes with these ten cells was analyzed. Lasso was used to selecting for trait genes. ROC curves and DCA decision curves were used to assess the accuracy and predictive power of the trait genes. RESULTS: A total of 65 cellular senescence signature genes were identified among patients and controls. The PPI network screened out ten hub genes. GO and KEGG indicated that ten hub genes were associated with ESRD progression. Transcription factor gene interactions and common regulatory networks of miRNAs were also identified in the datasets. The hub genes were significantly correlated with immune cells and stromal cells. Then the lasso model was constructed to screen out the five most relevant signature genes (FOS, FOXO3, SIRT1, TP53, SMARCA4). The area under the ROC curve (AUC) showed that these five characteristic genes have good resolving power for the diagnostic model. CONCLUSIONS: Our findings suggested that cellular senescence-associated genes played an important role in the development of ESRD and immune regulation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-023-03285-0. BioMed Central 2023-08-08 /pmc/articles/PMC10408218/ /pubmed/37553608 http://dx.doi.org/10.1186/s12882-023-03285-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xi, Yu-jia Guo, Qiang Zhang, Ran Duan, Guo-sheng Zhang, Sheng-xiao Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers |
title | Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers |
title_full | Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers |
title_fullStr | Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers |
title_full_unstemmed | Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers |
title_short | Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers |
title_sort | identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408218/ https://www.ncbi.nlm.nih.gov/pubmed/37553608 http://dx.doi.org/10.1186/s12882-023-03285-0 |
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