| Summary: | 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.
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