Evidence map›Paper›PMID 37789254›Full record

ArticleBMC infectious diseases2023

Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network.

Xue Zhou, Ning Wang, Wenjing Liu, Ruixue Chen, Guoyue Yang, Hongzhi Yu

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Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

6 authors.

Xue Zhou *Department of Nephrology, Haihe Hospital, Tianjin University, 890 Jingu Road, Jinnan District, Tianjin, 300350, China. xuezhoudoctor@126.com.ORCID http://orcid.org/0000-0002-3675-9479
Ning Wang *The Third Central Hospital of Tianjin, 83 Jintang Road, Hedong District, Tianjin, 300170, China.ORCID http://orcid.org/0000-0003-3103-4636
Wenjing LiuDepartment of Nephrology, Tianjin Haihe Hospital, Tianjin, 300350, China.
Ruixue ChenTianjin Haihe Hospital, Tianjin, 300350, China.
Guoyue YangThe Third Central Hospital of Tianjin, 83 Jintang Road, Hedong District, Tianjin, 300170, China. 24592453@qq.com.ORCID http://orcid.org/0000-0001-7877-6747
Hongzhi YuTianjin Institute of Respiratory Diseases, Tianjin, 300350, China. 13512205663@163.com.

Funding

Natural Science Foundation of Tianjin City 22JCQNJC00800Tianjin Health Research Project TJWJ2022QN076Tianjin second batch of health industry high-level talent selection and training project TJSJMYXYC-D2-012
6 · The paper itself

Abstract

backgroundThe 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.

methodsUtilizing 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.

resultsWe 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.

conclusionsThis 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.

Indexed as

Acute Kidney InjuryCOVID-19MicroRNAsCluster AnalysisHumansMembrane ProteinsSARS-CoV-2Membrane ProteinsMicroRNAsRARRES1 protein, humanAcute kidney injuryBiomarkerCOVID-19Differentially expressed genesPathogenesis

Identifiers

PMID37789254
PMCPMC10548629

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