Identification of the Hub Genes and the Signaling Pathways in Human iPSC-Cardiomyocytes Infected by SARS-CoV-2.
Li-Min XieYin-Fei HuangYe-Ling LiuJia-Qi LiangWei DengGeng-Ling LinHuan-Min LuoXu-Guang GuoPublished in: Biochemical genetics (2022)
Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) is an enveloped single-stranded RNA virus that can lead to respiratory symptoms and damage many organs such as heart, kidney, intestine, brain and liver. It has not been clearly documented whether myocardial injury is caused by direct infection of cardiomyocytes, lung injury, or other unknown mechanisms. The gene expression profile of GSE150392 was obtained from the Gene Expression Omnibus (GEO) database. The processing of high-throughput sequencing data and the screening of differentially expressed genes (DEGs) were implemented by R software. The R software was employed to analyze the Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The protein-protein interaction (PPI) network of the DEGs was constructed by the STRING website. The Cytoscape software was applied for the visualization of PPI network and the identification of hub genes. The statistical analysis was performed by the GraphPad Prism software to verify the hub genes. A total of 516 up-regulated genes and 191 down-regulated genes were screened out. The top 1 enrichment items of GO in biological process (BP), Cellular Component (CC), and Molecular Function (MF) were type I interferon signaling pathway, sarcomere, and receptor ligand activity, respectively. The top 10 enrichment pathways, including TNF signaling pathway, were identified by KEGG enrichment analysis. A PPI network was established, consisting of 613 nodes and 3,993 edges. The 12 hub genes were confirmed as statistically significant, which was verified by GSE151879 dataset. In conclusion, the hub genes of human iPSC-cardiomyocytes infected with SARS-CoV-2 were identified through bioinformatics analysis, which may be used as biomarkers for further research.
Keyphrases
- bioinformatics analysis
- sars cov
- signaling pathway
- genome wide
- respiratory syndrome coronavirus
- gene expression
- genome wide identification
- endothelial cells
- transcription factor
- epithelial mesenchymal transition
- heart failure
- small molecule
- rheumatoid arthritis
- early stage
- squamous cell carcinoma
- machine learning
- lymph node
- atrial fibrillation
- depressive symptoms
- multiple sclerosis
- induced pluripotent stem cells
- blood brain barrier
- sleep quality
- electronic health record
- pluripotent stem cells
- high glucose