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Tytuł pozycji:

New insights into genetic characteristics between multiple myeloma and COVID-19: An integrative bioinformatics analysis of gene expression omnibus microarray and the cancer genome atlas data.

Tytuł:
New insights into genetic characteristics between multiple myeloma and COVID-19: An integrative bioinformatics analysis of gene expression omnibus microarray and the cancer genome atlas data.
Autorzy:
Wang F; Department of Hematology (Key Department of Jiangsu Medicine), Medical School, Zhongda Hospital, Southeast University, Institute of Hematology Southeast University, Nanjing, China.
Liu R; Department of Quality Management, Medical School, Zhongda Hospital, Southeast University, Institute of Hematology Southeast University, Nanjing, China.
Yang J; Department of Hematology (Key Department of Jiangsu Medicine), Medical School, Zhongda Hospital, Southeast University, Institute of Hematology Southeast University, Nanjing, China.
Chen B; Department of Hematology (Key Department of Jiangsu Medicine), Medical School, Zhongda Hospital, Southeast University, Institute of Hematology Southeast University, Nanjing, China.
Źródło:
International journal of laboratory hematology [Int J Lab Hematol] 2021 Dec; Vol. 43 (6), pp. 1325-1333. Date of Electronic Publication: 2021 Oct 08.
Typ publikacji:
Comparative Study; Journal Article; Validation Study
Język:
English
Imprint Name(s):
Original Publication: Oxford : Blackwell Scientific Publications, c2007-
MeSH Terms:
Gene Expression Profiling*
SARS-CoV-2*
COVID-19/*genetics
Computational Biology/*methods
Multiple Myeloma/*genetics
COVID-19/mortality ; Datasets as Topic ; Electron Transport Complex IV/genetics ; Gene Expression Regulation, Neoplastic ; Gene Expression Regulation, Viral ; Gene Ontology ; Humans ; Kaplan-Meier Estimate ; Microarray Analysis ; Neoplasm Proteins/biosynthesis ; Neoplasm Proteins/genetics ; Nod2 Signaling Adaptor Protein/genetics ; Prognosis ; Proportional Hazards Models ; Protein Interaction Maps/genetics
References:
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Grant Information:
QNRC2016812 Jiangsu Provincial Medical Youth Talent; BK20180372 Natural Science Foundation of Jiangsu Province for Youth
Contributed Indexing:
Keywords: bioinformatics analysis; coronavirus disease 2019; multiple myeloma; overall survival
Substance Nomenclature:
0 (COX6c protein, human)
0 (NOD2 protein, human)
0 (Neoplasm Proteins)
0 (Nod2 Signaling Adaptor Protein)
EC 1.9.3.1 (Electron Transport Complex IV)
Entry Date(s):
Date Created: 20211008 Date Completed: 20211129 Latest Revision: 20211211
Update Code:
20240104
PubMed Central ID:
PMC8652836
DOI:
10.1111/ijlh.13717
PMID:
34623759
Czasopismo naukowe
Background: Multiple myeloma (MM) is a hematological malignancy. Coronavirus disease 2019 (COVID-19) infection correlates with MM features. This study aimed to identify MM prognostic biomarkers with potential association with COVID-19.
Methods: Differentially expressed genes (DEGs) in five MM data sets (GSE47552, GSE16558, GSE13591, GSE6477, and GSE39754) with the same expression trends were screened out. Functional enrichment analysis and the protein-protein interaction network were performed for all DEGs. Prognosis-associated DEGs were screened using the stepwise Cox regression analysis in the cancer genome atlas (TCGA) MMRF-CoMMpass cohort and the GSE24080 data set. Prognosis-associated DEGs associated with COVID-19 infection in the GSE164805 data set were also identified.
Results: A total of 98 DEGs with the same expression trends in five data sets were identified, and 83 DEGs were included in the protein-protein interaction network. Cox regression analysis identified 16 DEGs were associated with MM prognosis in the TCGA cohort, and only the cytochrome c oxidase subunit 6C (COX6C) gene (HR = 1.717, 95% CI 1.231-2.428, p = .002) and the nucleotide-binding oligomerization domain containing 2 (NOD2) gene (HR = 0.882, 95% CI 0.798-0.975, p = .014) were independent factors related to MM prognosis in the GSE24080 data set. Both of them were downregulated in patients with mild COVID-19 infection compared with controls but were upregulated in patients with severe COVID-19 compared with patients with mild illness.
Conclusions: The NOD2 and COX6C genes might be used as prognostic biomarkers in MM. The two genes might be associated with the development of COVID-19 infection.
(© 2021 John Wiley & Sons Ltd.)

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