Affiliation:
1. The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
2. Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, Hubei Province, China
3. Hubei Clinical Research Center for Infectious Diseases, Wuhan 430023, Hubei Province, China
4. Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan 430023, Hubei Province, China
5. Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology and Wuhan Jinyintan Hospital, Chinese Academy of Sciences, Wuhan 430023, Hubei Province, China
Abstract
ABSTRACT
Objective:
The Coronavirus Disease 2019 (COVID-19) is a recently-emerging infectious disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), posing a significant threat to public health around the world. In patients with COVID-19, acute kidney injury (AKI) is a common complication associated with poor prognoses. We analyzed co-expressed genes to explore relationships between SARS-CoV2 infection and AKI, and revealed potential biomarkers and therapeutic targets of the COVID-19-associated AKI (COVID-19-AKI).
Methods:
We utilized the GSE147507 and GSE139061 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) in SARS-CoV-2 infection and AKI, respectively. This was followed by analyzing protein-protein interaction networks, Gene Ontology, and pathway enrichment to uncover the relationship between DEGs. DEGs in common (co-DEGs), as well as corresponding interactive transcription factors (TFs) and microRNAs, were identified from the above results, followed by drug molecules uncovered for managing COVID-19-AKI. Aims: To reveal potential biomarkers and therapeutic targets for COVID-19-AKI by bioinformatic approach.
Results:
We discovered 345 DEGs in the lung and 310 DEGs AKI samples from COVID-19 patients, respectively. IFIT1, ISG15, MX1, IFIT3, and IFIT2 were involved in SARS-CoV-2 pulmonary infection, while hub genes such as RPL23, EIF4A1, RPS8, RPL13, and UPF2 were associated with AKI. We further derived co-DEGs including ERRFI1, KLK10, NR4A1, PODXL, RASGEF1C, RNU11, SNORA12, SNORA74B, and VTRNA1-1 coupled with their predicted transcription factors, including BACH2, HNF4A, MYC, and microRNAs containing miR-637, miR-542-3p, and miR-224. These targets may correlate with COVID-19-AKI, for which candidate drugs were identified.
Conclusions:
ERRFI1, KLK10, NR4A1, PODXL, RASGEF1C, RNU11, SNORA12, SNORA74B, and VTRNA1-1 may be associated with COVID-19-AKI and serve as novel markers.
Subject
General Engineering,Electrical and Electronic Engineering,Building and Construction