Cargando…
Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network
BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is identified as the cause of coronavirus disease 2019 (COVID-19) pandemic. Acute kidney injury (AKI), one of serious complications of COVID-19 infection, is the leading contributor to renal failure, associating w...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548629/ https://www.ncbi.nlm.nih.gov/pubmed/37789254 http://dx.doi.org/10.1186/s12879-023-08638-6 |
_version_ | 1785115310317109248 |
---|---|
author | Zhou, Xue Wang, Ning Liu, Wenjing Chen, Ruixue Yang, Guoyue Yu, Hongzhi |
author_facet | Zhou, Xue Wang, Ning Liu, Wenjing Chen, Ruixue Yang, Guoyue Yu, Hongzhi |
author_sort | Zhou, Xue |
collection | PubMed |
description | BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is identified as the cause of coronavirus disease 2019 (COVID-19) pandemic. Acute kidney injury (AKI), one of serious complications of COVID-19 infection, is the leading contributor to renal failure, associating with high mortality of the patients. This study aimed to identify the shared gene signatures and construct the gene regulatory network between COVID-19 and AKI, contributing to exploring the potential pathogenesis. METHODS: Utilizing the machine learning approach, the candidate gene signatures were derived from the common differentially expressed genes (DEGs) obtained from COVID-19 and AKI. Subsequently, receiver operating characteristic (ROC), consensus clustering and functional enrichment analyses were performed. Finally, protein-protein interaction (PPI) network, transcription factor (TF)-gene interaction, gene-miRNA interaction, and TF-miRNA coregulatory network were systematically undertaken. RESULTS: We successfully identified the shared 6 candidate gene signatures (RRM2, EGF, TMEM252, RARRES1, COL6A3, CUBN) between COVID-19 and AKI. ROC analysis showed that the model constructed by 6 gene signatures had a high predictive efficacy in COVID-19 (AUC = 0.965) and AKI (AUC = 0.962) cohorts, which had the potential to be the shared diagnostic biomarkers for COVID-19 and AKI. Additionally, the comprehensive gene regulatory networks, including PPI, TF-gene interaction, gene-miRNA interaction, and TF-miRNA coregulatory networks were displayed utilizing NetworkAnalyst platform. CONCLUSIONS: This study successfully identified the shared gene signatures and constructed the comprehensive gene regulatory network between COVID-19 and AKI, which contributed to predicting patients’ prognosis and providing new ideas for developing therapeutic targets for COVID-19 and AKI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08638-6. |
format | Online Article Text |
id | pubmed-10548629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105486292023-10-05 Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network Zhou, Xue Wang, Ning Liu, Wenjing Chen, Ruixue Yang, Guoyue Yu, Hongzhi BMC Infect Dis Research BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is identified as the cause of coronavirus disease 2019 (COVID-19) pandemic. Acute kidney injury (AKI), one of serious complications of COVID-19 infection, is the leading contributor to renal failure, associating with high mortality of the patients. This study aimed to identify the shared gene signatures and construct the gene regulatory network between COVID-19 and AKI, contributing to exploring the potential pathogenesis. METHODS: Utilizing the machine learning approach, the candidate gene signatures were derived from the common differentially expressed genes (DEGs) obtained from COVID-19 and AKI. Subsequently, receiver operating characteristic (ROC), consensus clustering and functional enrichment analyses were performed. Finally, protein-protein interaction (PPI) network, transcription factor (TF)-gene interaction, gene-miRNA interaction, and TF-miRNA coregulatory network were systematically undertaken. RESULTS: We successfully identified the shared 6 candidate gene signatures (RRM2, EGF, TMEM252, RARRES1, COL6A3, CUBN) between COVID-19 and AKI. ROC analysis showed that the model constructed by 6 gene signatures had a high predictive efficacy in COVID-19 (AUC = 0.965) and AKI (AUC = 0.962) cohorts, which had the potential to be the shared diagnostic biomarkers for COVID-19 and AKI. Additionally, the comprehensive gene regulatory networks, including PPI, TF-gene interaction, gene-miRNA interaction, and TF-miRNA coregulatory networks were displayed utilizing NetworkAnalyst platform. CONCLUSIONS: This study successfully identified the shared gene signatures and constructed the comprehensive gene regulatory network between COVID-19 and AKI, which contributed to predicting patients’ prognosis and providing new ideas for developing therapeutic targets for COVID-19 and AKI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08638-6. BioMed Central 2023-10-03 /pmc/articles/PMC10548629/ /pubmed/37789254 http://dx.doi.org/10.1186/s12879-023-08638-6 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 Zhou, Xue Wang, Ning Liu, Wenjing Chen, Ruixue Yang, Guoyue Yu, Hongzhi Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network |
title | Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network |
title_full | Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network |
title_fullStr | Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network |
title_full_unstemmed | Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network |
title_short | Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network |
title_sort | identification of the potential association between sars-cov-2 infection and acute kidney injury based on the shared gene signatures and regulatory network |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548629/ https://www.ncbi.nlm.nih.gov/pubmed/37789254 http://dx.doi.org/10.1186/s12879-023-08638-6 |
work_keys_str_mv | AT zhouxue identificationofthepotentialassociationbetweensarscov2infectionandacutekidneyinjurybasedonthesharedgenesignaturesandregulatorynetwork AT wangning identificationofthepotentialassociationbetweensarscov2infectionandacutekidneyinjurybasedonthesharedgenesignaturesandregulatorynetwork AT liuwenjing identificationofthepotentialassociationbetweensarscov2infectionandacutekidneyinjurybasedonthesharedgenesignaturesandregulatorynetwork AT chenruixue identificationofthepotentialassociationbetweensarscov2infectionandacutekidneyinjurybasedonthesharedgenesignaturesandregulatorynetwork AT yangguoyue identificationofthepotentialassociationbetweensarscov2infectionandacutekidneyinjurybasedonthesharedgenesignaturesandregulatorynetwork AT yuhongzhi identificationofthepotentialassociationbetweensarscov2infectionandacutekidneyinjurybasedonthesharedgenesignaturesandregulatorynetwork |