Integrative Bioinformatics Analysis Provides Insight into the Molecular Mechanisms of Chronic Kidney Disease
Background/Aims: Chronic kidney disease (CKD) is a worldwide public health problem. Regardless of the underlying primary disease, CKD tends to progress to end-stage kidney disease, resulting in unsatisfactory and costly treatment. Its common pathogenesis, however, remains unclear. The aim of this st...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Karger Publishers
2018-04-01
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| Series: | Kidney & Blood Pressure Research |
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| Online Access: | https://www.karger.com/Article/FullText/488830 |
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| author | Le-Ting Zhou Shen Qiu Lin-Li Lv Zuo-Lin Li Hong Liu Ri-Ning Tang Kun-Ling Ma Bi-Cheng Liu |
| author_facet | Le-Ting Zhou Shen Qiu Lin-Li Lv Zuo-Lin Li Hong Liu Ri-Ning Tang Kun-Ling Ma Bi-Cheng Liu |
| author_sort | Le-Ting Zhou |
| collection | DOAJ |
| description | Background/Aims: Chronic kidney disease (CKD) is a worldwide public health problem. Regardless of the underlying primary disease, CKD tends to progress to end-stage kidney disease, resulting in unsatisfactory and costly treatment. Its common pathogenesis, however, remains unclear. The aim of this study was to provide an unbiased catalog of common gene-expression changes of CKD and reveal the underlying molecular mechanism using an integrative bioinformatics approach. Methods: We systematically collected over 250 Affymetrix microarray datasets from the glomerular and tubulointerstitial compartments of healthy renal tissues and those with various types of established CKD (diabetic kidney disease, hypertensive nephropathy, and glomerular nephropathy). Then, using stringent bioinformatics analysis, shared differentially expressed genes (DEGs) of CKD were obtained. These shared DEGs were further analyzed by the gene ontology (GO) and pathway enrichment analysis. Finally, the protein-protein interaction networks(PINs) were constructed to further refine our results. Results: Our analysis identified 176 and 50 shared DEGs in diseased glomeruli and tubules, respectively, including many transcripts that have not been previously reported to be involved in kidney disease. Enrichment analysis also showed that the glomerular and tubulointerstitial compartments underwent a wide range of unique pathological changes during chronic injury. As revealed by the GO enrichment analysis, shared DEGs in glomeruli were significantly enriched in exosomes. By constructing PINs, we identified several hub genes (e.g. OAS1, JUN, and FOS) and clusters that might play key roles in regulating the development of CKD. Conclusion: Our study not only further reveals the unifying molecular mechanism of CKD pathogenesis but also provides a valuable resource of potential biomarkers and therapeutic targets. |
| first_indexed | 2024-12-11T02:32:14Z |
| format | Article |
| id | doaj.art-7189836e388e42d0a38c9e647d3fc8af |
| institution | Directory Open Access Journal |
| issn | 1420-4096 1423-0143 |
| language | English |
| last_indexed | 2024-12-11T02:32:14Z |
| publishDate | 2018-04-01 |
| publisher | Karger Publishers |
| record_format | Article |
| series | Kidney & Blood Pressure Research |
| spelling | doaj.art-7189836e388e42d0a38c9e647d3fc8af2022-12-22T01:23:48ZengKarger PublishersKidney & Blood Pressure Research1420-40961423-01432018-04-0143256858110.1159/000488830488830Integrative Bioinformatics Analysis Provides Insight into the Molecular Mechanisms of Chronic Kidney DiseaseLe-Ting ZhouShen QiuLin-Li LvZuo-Lin LiHong LiuRi-Ning TangKun-Ling MaBi-Cheng LiuBackground/Aims: Chronic kidney disease (CKD) is a worldwide public health problem. Regardless of the underlying primary disease, CKD tends to progress to end-stage kidney disease, resulting in unsatisfactory and costly treatment. Its common pathogenesis, however, remains unclear. The aim of this study was to provide an unbiased catalog of common gene-expression changes of CKD and reveal the underlying molecular mechanism using an integrative bioinformatics approach. Methods: We systematically collected over 250 Affymetrix microarray datasets from the glomerular and tubulointerstitial compartments of healthy renal tissues and those with various types of established CKD (diabetic kidney disease, hypertensive nephropathy, and glomerular nephropathy). Then, using stringent bioinformatics analysis, shared differentially expressed genes (DEGs) of CKD were obtained. These shared DEGs were further analyzed by the gene ontology (GO) and pathway enrichment analysis. Finally, the protein-protein interaction networks(PINs) were constructed to further refine our results. Results: Our analysis identified 176 and 50 shared DEGs in diseased glomeruli and tubules, respectively, including many transcripts that have not been previously reported to be involved in kidney disease. Enrichment analysis also showed that the glomerular and tubulointerstitial compartments underwent a wide range of unique pathological changes during chronic injury. As revealed by the GO enrichment analysis, shared DEGs in glomeruli were significantly enriched in exosomes. By constructing PINs, we identified several hub genes (e.g. OAS1, JUN, and FOS) and clusters that might play key roles in regulating the development of CKD. Conclusion: Our study not only further reveals the unifying molecular mechanism of CKD pathogenesis but also provides a valuable resource of potential biomarkers and therapeutic targets.https://www.karger.com/Article/FullText/488830MicroarrayProtein– protein interaction networkChronic kidney diseaseBioinformaticsMolecular mechanisms |
| spellingShingle | Le-Ting Zhou Shen Qiu Lin-Li Lv Zuo-Lin Li Hong Liu Ri-Ning Tang Kun-Ling Ma Bi-Cheng Liu Integrative Bioinformatics Analysis Provides Insight into the Molecular Mechanisms of Chronic Kidney Disease Kidney & Blood Pressure Research Microarray Protein– protein interaction network Chronic kidney disease Bioinformatics Molecular mechanisms |
| title | Integrative Bioinformatics Analysis Provides Insight into the Molecular Mechanisms of Chronic Kidney Disease |
| title_full | Integrative Bioinformatics Analysis Provides Insight into the Molecular Mechanisms of Chronic Kidney Disease |
| title_fullStr | Integrative Bioinformatics Analysis Provides Insight into the Molecular Mechanisms of Chronic Kidney Disease |
| title_full_unstemmed | Integrative Bioinformatics Analysis Provides Insight into the Molecular Mechanisms of Chronic Kidney Disease |
| title_short | Integrative Bioinformatics Analysis Provides Insight into the Molecular Mechanisms of Chronic Kidney Disease |
| title_sort | integrative bioinformatics analysis provides insight into the molecular mechanisms of chronic kidney disease |
| topic | Microarray Protein– protein interaction network Chronic kidney disease Bioinformatics Molecular mechanisms |
| url | https://www.karger.com/Article/FullText/488830 |
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