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• W3203000983 abstract "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.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.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.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." @default.
• W3203000983 created "2021-10-11" @default.
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• W3203000983 date "2021-10-08" @default.
• W3203000983 modified "2023-10-17" @default.
• W3203000983 title "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" @default.
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• W3203000983 doi "https://doi.org/10.1111/ijlh.13717" @default.
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