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  • Comprehensive Analysis of the Systemic Transcriptomic Alternations and Inflammatory Response during the Occurrence and Progress of COVID-19 #MMPMID34457122
  • Mo S; Dai L; Wang Y; Song B; Yang Z; Gu W
  • Oxid Med Cell Longev 2021[]; 2021 (ä): 9998697 PMID34457122show ga
  • The pandemic of the coronavirus disease 2019 (COVID-19) has posed huge threats to healthcare systems and the global economy. However, the host response towards COVID-19 on the molecular and cellular levels still lacks full understanding and effective therapies are in urgent need. Here, we integrate three datasets, GSE152641, GSE161777, and GSE157103. Compared to healthy people, 314 differentially expressed genes were identified, which were mostly involved in neutrophil degranulation and cell division. The protein-protein network was established and two significant subsets were filtered by MCODE: ssGSEA and CIBERSORT, which comprehensively revealed the alternation of immune cell abundance. Weighted gene coexpression network analysis (WGCNA) as well as GO and KEGG analyses unveiled the role of neutrophils and T cells during the progress of the disease. Based on the hospital-free days after 45 days of follow-up and statistical methods such as nonnegative matrix factorization (NMF), submap, and linear correlation analysis, 31 genes were regarded as the signature of the peripheral blood of COVID-19. Various immune cells were identified to be related to the prognosis of the patients. Drugs were predicted for the genes in the signature by DGIdb. Overall, our study comprehensively revealed the relationship between the inflammatory response and the disease course, which provided strategies for the treatment of COVID-19.
  • |*Gene Regulatory Networks[MESH]
  • |*Transcriptome[MESH]
  • |COVID-19/complications/*genetics/*immunology/virology[MESH]
  • |Case-Control Studies[MESH]
  • |Gene Expression Profiling[MESH]
  • |Humans[MESH]
  • |Inflammation/*genetics/*immunology/virology[MESH]
  • |Protein Interaction Maps[MESH]
  • |SARS-CoV-2/*immunology[MESH]

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  • suck abstract from ncbi

    9998697 ä.2021 2021