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Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury

BACKGROUND: Sepsis and septic shock are life-threatening diseases with high mortality rate in intensive care unit (ICU). Acute kidney injury (AKI) is a common complication of sepsis, and its occurrence is a poor prognostic sign to septic patients. We analyzed co-differentially expressed genes (co-DE...

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Autores principales: Tang, Yun, Yang, Xiaobo, Shu, Huaqing, Yu, Yuan, Pan, Shangwen, Xu, Jiqian, Shang, You
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8052759/
https://www.ncbi.nlm.nih.gov/pubmed/33863396
http://dx.doi.org/10.1186/s41065-021-00176-y
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author Tang, Yun
Yang, Xiaobo
Shu, Huaqing
Yu, Yuan
Pan, Shangwen
Xu, Jiqian
Shang, You
author_facet Tang, Yun
Yang, Xiaobo
Shu, Huaqing
Yu, Yuan
Pan, Shangwen
Xu, Jiqian
Shang, You
author_sort Tang, Yun
collection PubMed
description BACKGROUND: Sepsis and septic shock are life-threatening diseases with high mortality rate in intensive care unit (ICU). Acute kidney injury (AKI) is a common complication of sepsis, and its occurrence is a poor prognostic sign to septic patients. We analyzed co-differentially expressed genes (co-DEGs) to explore relationships between septic shock and AKI and reveal potential biomarkers and therapeutic targets of septic-shock-associated AKI (SSAKI). METHODS: Two gene expression datasets (GSE30718 and GSE57065) were downloaded from the Gene Expression Omnibus (GEO). The GSE57065 dataset included 28 septic shock patients and 25 healthy volunteers and blood samples were collected within 0.5, 24 and 48 h after shock. Specimens of GSE30718 were collected from 26 patients with AKI and 11 control patents. AKI-DEGs and septic-shock-DEGs were identified using the two datasets. Subsequently, Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs. We also evaluated co-DEGs and corresponding predicted miRNAs involved in septic shock and AKI. RESULTS: We identified 62 DEGs in AKI specimens and 888, 870, and 717 DEGs in septic shock blood samples within 0.5, 24 and 48 h, respectively. The hub genes of EGF and OLFM4 may be involved in AKI and QPCT, CKAP4, PRKCQ, PLAC8, PRC1, BCL9L, ATP11B, KLHL2, LDLRAP1, NDUFAF1, IFIT2, CSF1R, HGF, NRN1, GZMB, and STAT4 may be associated with septic shock. Besides, co-DEGs of VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 coupled with corresponding predicted miRNAs, especially miR-29b-3p, miR-152-3p, and miR-223-3p may be regarded as promising targets for the diagnosis and treatment of SSAKI in the future. CONCLUSIONS: Septic shock and AKI are related and VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 genes are significantly associated with novel biomarkers involved in the occurrence and development of SSAKI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41065-021-00176-y.
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spelling pubmed-80527592021-04-19 Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury Tang, Yun Yang, Xiaobo Shu, Huaqing Yu, Yuan Pan, Shangwen Xu, Jiqian Shang, You Hereditas Research BACKGROUND: Sepsis and septic shock are life-threatening diseases with high mortality rate in intensive care unit (ICU). Acute kidney injury (AKI) is a common complication of sepsis, and its occurrence is a poor prognostic sign to septic patients. We analyzed co-differentially expressed genes (co-DEGs) to explore relationships between septic shock and AKI and reveal potential biomarkers and therapeutic targets of septic-shock-associated AKI (SSAKI). METHODS: Two gene expression datasets (GSE30718 and GSE57065) were downloaded from the Gene Expression Omnibus (GEO). The GSE57065 dataset included 28 septic shock patients and 25 healthy volunteers and blood samples were collected within 0.5, 24 and 48 h after shock. Specimens of GSE30718 were collected from 26 patients with AKI and 11 control patents. AKI-DEGs and septic-shock-DEGs were identified using the two datasets. Subsequently, Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs. We also evaluated co-DEGs and corresponding predicted miRNAs involved in septic shock and AKI. RESULTS: We identified 62 DEGs in AKI specimens and 888, 870, and 717 DEGs in septic shock blood samples within 0.5, 24 and 48 h, respectively. The hub genes of EGF and OLFM4 may be involved in AKI and QPCT, CKAP4, PRKCQ, PLAC8, PRC1, BCL9L, ATP11B, KLHL2, LDLRAP1, NDUFAF1, IFIT2, CSF1R, HGF, NRN1, GZMB, and STAT4 may be associated with septic shock. Besides, co-DEGs of VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 coupled with corresponding predicted miRNAs, especially miR-29b-3p, miR-152-3p, and miR-223-3p may be regarded as promising targets for the diagnosis and treatment of SSAKI in the future. CONCLUSIONS: Septic shock and AKI are related and VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 genes are significantly associated with novel biomarkers involved in the occurrence and development of SSAKI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41065-021-00176-y. BioMed Central 2021-04-16 /pmc/articles/PMC8052759/ /pubmed/33863396 http://dx.doi.org/10.1186/s41065-021-00176-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Tang, Yun
Yang, Xiaobo
Shu, Huaqing
Yu, Yuan
Pan, Shangwen
Xu, Jiqian
Shang, You
Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury
title Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury
title_full Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury
title_fullStr Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury
title_full_unstemmed Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury
title_short Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury
title_sort bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8052759/
https://www.ncbi.nlm.nih.gov/pubmed/33863396
http://dx.doi.org/10.1186/s41065-021-00176-y
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