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Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy
BACKGROUND: Diabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus (DM). A growing body of research has demonstrated that the inflammatory state plays a critical role in the incidence and development of DN. Pyroptosis is a new way of programmed cell death, w...
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/PMC10360355/ https://www.ncbi.nlm.nih.gov/pubmed/37480090 http://dx.doi.org/10.1186/s12967-023-04350-w |
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author | Yan, Meng Li, Wenwen Wei, Rui Li, Shuwen Liu, Yan Huang, Yuqian Zhang, Yunye Lu, Zihao Lu, Qian |
author_facet | Yan, Meng Li, Wenwen Wei, Rui Li, Shuwen Liu, Yan Huang, Yuqian Zhang, Yunye Lu, Zihao Lu, Qian |
author_sort | Yan, Meng |
collection | PubMed |
description | BACKGROUND: Diabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus (DM). A growing body of research has demonstrated that the inflammatory state plays a critical role in the incidence and development of DN. Pyroptosis is a new way of programmed cell death, which has the particularity of natural immune inflammation. The inhibition of inflammatory cytokine expression and regulation of pathways related to pyroptosis may be a novel strategy for DN treatment. The aim of this study is to identify pyroptosis-related genes and potential drugs for DN. METHODS: DN differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE96804. Dataset GSE30528 and GSE142025 were downloaded to verify pyroptosis-related differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a pyroptosis-related gene predictive model. A consensus clustering analysis was performed to identify pyroptosis-related DN subtypes. Subsequently, Gene Set Variation Analysis (GSVA), Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the differences between DN clusters. A protein–protein interaction (PPI) network was used to select hub genes and DGIdb database was utilized to screen potential therapeutic drugs/compounds targeting hub genes. RESULTS: A total of 24 differentially expressed pyroptosis-related genes were identified in DN. A 16 gene predictive model was conducted via LASSO regression analysis. According to the expression level of these 16 genes, DN cases were divided into two subtypes, and the subtypes are mainly associated with inflammation, activation of immune response and cell metabolism. In addition, we identified 10 hub genes among these subtypes, and predicted 65 potential DN therapeutics that target key genes. CONCLUSION: We identified two pyroptosis-related DN clusters and 65 potential therapeutical agents/compounds for DN, which might shed a light on the treatment of DN. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04350-w. |
format | Online Article Text |
id | pubmed-10360355 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103603552023-07-22 Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy Yan, Meng Li, Wenwen Wei, Rui Li, Shuwen Liu, Yan Huang, Yuqian Zhang, Yunye Lu, Zihao Lu, Qian J Transl Med Research BACKGROUND: Diabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus (DM). A growing body of research has demonstrated that the inflammatory state plays a critical role in the incidence and development of DN. Pyroptosis is a new way of programmed cell death, which has the particularity of natural immune inflammation. The inhibition of inflammatory cytokine expression and regulation of pathways related to pyroptosis may be a novel strategy for DN treatment. The aim of this study is to identify pyroptosis-related genes and potential drugs for DN. METHODS: DN differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE96804. Dataset GSE30528 and GSE142025 were downloaded to verify pyroptosis-related differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a pyroptosis-related gene predictive model. A consensus clustering analysis was performed to identify pyroptosis-related DN subtypes. Subsequently, Gene Set Variation Analysis (GSVA), Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the differences between DN clusters. A protein–protein interaction (PPI) network was used to select hub genes and DGIdb database was utilized to screen potential therapeutic drugs/compounds targeting hub genes. RESULTS: A total of 24 differentially expressed pyroptosis-related genes were identified in DN. A 16 gene predictive model was conducted via LASSO regression analysis. According to the expression level of these 16 genes, DN cases were divided into two subtypes, and the subtypes are mainly associated with inflammation, activation of immune response and cell metabolism. In addition, we identified 10 hub genes among these subtypes, and predicted 65 potential DN therapeutics that target key genes. CONCLUSION: We identified two pyroptosis-related DN clusters and 65 potential therapeutical agents/compounds for DN, which might shed a light on the treatment of DN. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04350-w. BioMed Central 2023-07-21 /pmc/articles/PMC10360355/ /pubmed/37480090 http://dx.doi.org/10.1186/s12967-023-04350-w 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 Yan, Meng Li, Wenwen Wei, Rui Li, Shuwen Liu, Yan Huang, Yuqian Zhang, Yunye Lu, Zihao Lu, Qian Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy |
title | Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy |
title_full | Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy |
title_fullStr | Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy |
title_full_unstemmed | Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy |
title_short | Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy |
title_sort | identification of pyroptosis-related genes and potential drugs in diabetic nephropathy |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10360355/ https://www.ncbi.nlm.nih.gov/pubmed/37480090 http://dx.doi.org/10.1186/s12967-023-04350-w |
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