Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers
Abstract 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) d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BMC Nephrology |
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| Online Access: | https://doi.org/10.1186/s12882-023-03285-0 |
| _version_ | 1797453647705014272 |
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| author | Yu-jia Xi Qiang Guo Ran Zhang Guo-sheng Duan Sheng-xiao Zhang |
| author_facet | Yu-jia Xi Qiang Guo Ran Zhang Guo-sheng Duan Sheng-xiao Zhang |
| author_sort | Yu-jia Xi |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-09T15:25:51Z |
| format | Article |
| id | doaj.art-f22cfc3b11fc4c589e29d26eccc967c4 |
| institution | Directory Open Access Journal |
| issn | 1471-2369 |
| language | English |
| last_indexed | 2024-03-09T15:25:51Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nephrology |
| spelling | doaj.art-f22cfc3b11fc4c589e29d26eccc967c42023-11-26T12:35:29ZengBMCBMC Nephrology1471-23692023-08-0124111310.1186/s12882-023-03285-0Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkersYu-jia Xi0Qiang Guo1Ran Zhang2Guo-sheng Duan3Sheng-xiao Zhang4Department of Urology, Second Hospital of Shanxi Medical UniversityDepartment of Urology, Second Hospital of Shanxi Medical UniversitySchool of Public Health, Shanxi Medical UniversityFifth School of Clinical Medicine, Shanxi Provincial People’s Hospital, Shanxi Medical UniversityKey Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of EducationAbstract 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.https://doi.org/10.1186/s12882-023-03285-0ESRDCellular senescenceImmune regulationGEOBioinformatics |
| spellingShingle | Yu-jia Xi Qiang Guo Ran Zhang Guo-sheng Duan Sheng-xiao Zhang Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers BMC Nephrology ESRD Cellular senescence Immune regulation GEO Bioinformatics |
| 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 | ESRD Cellular senescence Immune regulation GEO Bioinformatics |
| url | https://doi.org/10.1186/s12882-023-03285-0 |
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