Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network
Abstract 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, assoc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-023-08638-6 |
| _version_ | 1797577907598524416 |
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| author | Xue Zhou Ning Wang Wenjing Liu Ruixue Chen Guoyue Yang Hongzhi Yu |
| author_facet | Xue Zhou Ning Wang Wenjing Liu Ruixue Chen Guoyue Yang Hongzhi Yu |
| author_sort | Xue Zhou |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-10T22:15:44Z |
| format | Article |
| id | doaj.art-c2f1005ec67842be9bb85a434048e570 |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-03-10T22:15:44Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-c2f1005ec67842be9bb85a434048e5702023-11-19T12:28:52ZengBMCBMC Infectious Diseases1471-23342023-10-0123111110.1186/s12879-023-08638-6Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory networkXue Zhou0Ning Wang1Wenjing Liu2Ruixue Chen3Guoyue Yang4Hongzhi Yu5Department of Nephrology, Haihe Hospital, Tianjin UniversityThe Third Central Hospital of TianjinDepartment of Nephrology, Tianjin Haihe HospitalTianjin Haihe HospitalThe Third Central Hospital of TianjinTianjin Institute of Respiratory DiseasesAbstract 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.https://doi.org/10.1186/s12879-023-08638-6COVID-19Acute kidney injuryDifferentially expressed genesBiomarkerPathogenesis |
| spellingShingle | Xue Zhou Ning Wang Wenjing Liu Ruixue Chen Guoyue Yang Hongzhi Yu Identification of the potential association between SARS-CoV-2 infection and acute kidney injury based on the shared gene signatures and regulatory network BMC Infectious Diseases COVID-19 Acute kidney injury Differentially expressed genes Biomarker Pathogenesis |
| 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 | COVID-19 Acute kidney injury Differentially expressed genes Biomarker Pathogenesis |
| url | https://doi.org/10.1186/s12879-023-08638-6 |
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