key: cord-0772225-yegal5gn authors: Wang, Fei; Liu, Ran; Yang, Jie; Chen, Baoan 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 date: 2021-10-08 journal: Int J Lab Hematol DOI: 10.1111/ijlh.13717 sha: 117903996955032dbc170a5f3e5c6c9d3269ff39 doc_id: 772225 cord_uid: yegal5gn 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. Multiple myeloma (MM) ranks 24th in the world in 2018, with approximately 160,000 newly diagnosed cases. 1 In hematological malignancies, the incidence of MM ranks second to non-Hodgkin lymphoma. The incidence rate of MM is 2.1 per 100 000 persons globally and is higher (10 per 100,000 persons) in Latin American countries. [2] [3] [4] [5] The survival rate of MM patients has been greatly improved over the past two decades with the introduction of new drugs, but MM remains an incurable malignancy. Also, the 5-year and 10-year overall survival rate of MM is still less than 60% and 40%. 2 Older MM patients have a lower survival rate and an insignificant improvement in survival from new drugs. 2 Prognostic factors can be applied for predicting individualized prognosis, making risk stratification, and treatment recommendations. 6 Factors associated with the prognosis in MM include patients' age, cytogenetics, serum creatinine, platelet count, and gene expression profile. 6 Protein coding (mRNAs) and noncoding RNAs (including miRNAs, lncRNAs, and circRNAs) with prognostic significance in MM have been proven in the past years. 7, 8 Of note, recent evidence shows a correlation between coronavirus disease 2019 (COVID- 19) infection and MM clinical features. [9] [10] [11] A recent study showed that most MM patients (77%) had moderate-severe coronavirus disease 2019 (COVID-19) clinical features, but is lower than 89% in noncancer patients. Most patients who did not survive COVID-19 were male with advanced tumor stages. 11 Most MM patients with a loss of functional immunoglobulins and decreased CD 4+ T-cell count, which are associated with increased infections. 12 Accordingly, MM patients are more susceptible to COVID-19 infection. 9, 10 However, some research studies showed that the most common MM subtypes with COVID-19 infection were IgG and MM patients with COVID-19 showed a longer duration to clinical improvement. 13 These studies show that there might be a potent correlation between COVID-19 infection and MM, which might indicate the hidden and unknown mechanisms of MM. Since pandemic COVID-19 in 2020, more and more evidence shows that there is a correlation between gene differential expression and COVID-19 infection. 14 However, there is limited information on the characteristics of MM patients with COVID-19. Hence, the identification of potent prognostic biomarkers in MM that might have association with pandemic COVID-19 might provide novel insights into the treatment strategy for MM patients hospitalized with COVID-19. Five MM gene expression microarray data sets (including GSE16558, GSE47552, GSE39754, GSE13591, and GSE6477) were selected from The National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo). The inclusion criteria of MM microarray data sets were as follows 9 : inclusive of normal bone marrow plasma cell samples from MM patients (≥40) 12 ; without restrictions on patients' gender, race, treatment response, karyotype, mutation, and pathologic stages. Forty-one, 60, 133, 103, and 170 plasma cell samples (CD138-positive) from MM patients were included in the data sets GSE47552, GSE16558, GSE13591, GSE6477, One COVID-19 gene expression data set (GSE16 4805) was downloaded from the GEO. It was based on the GPL26963 platform (Agilent-085982 Arraystar human lncRNA V5 microarray). GSE16 4805 was composed of 15 peripheral blood mononuclear cells (PBMCs) from severe (respiratory rate ≥ 30 times/min, resting finger oxygen saturation ≤93%, and artery PaO2/FiO2 ≤ 300 mmHg) and mild COVID-19 patients (n = 10, PCR positive) as well as healthy controls (n = 5). Differentially expressed genes in each data set were identified using the online program GEO2R (http://www.ncbi.nlm.nih.gov/ geo/geo2r/) provided by the NCBI. DEGs in each data set were selected using the criteria of p < .05 and |log(fold change, FC)| ≥ 0.5. DEGs common to the five MM data sets were screened out using the Venn tool (http://bioin forma tics.psb.ugent.be/webto ols/Venn/). DEGs with the same expression trends (upregulation, logFC > 0. 5; or downregulation, logFC < -0.5) in five data sets were retained and used for further analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) biological processes associated with DEGs were obtained in the Database for Annotation, Visualization and Integrated Discovery (DAVID; version 6.8; https://david.ncifc rf.gov/). Significant themes were identified using the criteria of p < .05 and input number ≥ 1. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING; version 11.0; https://strin g-db.org/cgi/input.pl) is a database of known and predicted Protein-protein interactions. Protein interaction pairs with medium confidence (0.4) among DEGs were predicted in the STRING database. The PPI network was constructed using the Cytoscape (version 3.8.0; http://apps.cytos cape.org/), and modules with a high K-score (>5.0) were identified using the plugin MCODE (http://apps.cytos cape.org/apps/mcode). The association of DEGs in the PPI network with the prognosis in MM patients was used to screen the MM prognosis-associated At last, the MM prognosis-associated DEGs which might be differentially expressed in patients with COVID-19 infection were identified using the Venn diagram. The expression profiles of the key MM prognosis-associated DEGs in patients with mild and severe COVID-19 clinical features were compared. Cox regression analysis and Kaplan-Meier survival analysis were performed using the SPSS software (version 22.0; IBM Corporation, Somers). 95% confident interval (CI) and hazard ratio (HR) were calculated. The one-way ANOVA test was used for the comparison in the expression level of genes among sample comparison groups in the GSE16 4805 data set. A statistical significance was considered when p < .05. Total of 1144, 3213, 9475, 2504, and 4428 DEGs were identified from the GSE13591, GSE16558, GSE39754, GSE47552, and GSE6477 data set, respectively. As indicated by the Venn diagram analysis, there were 109 common DEGs among the five data sets (Figure 1 ), including 98 DEGs with the same expression trends were identified and shown in Table S1. Functional enrichment analysis showed that a cluster of upregulated genes encoding ribosomal proteins were associated with biological processes including "GO:0019083: viral transcription," "GO: 0006364:rRNA processing," and "GO:0002181: cytoplasmic translation," and KEGG pathways including "hsa03010: Ribosome" (Table S2) . Also, another cluster of genes were associated with biological processes such as "GO:0006123: mitochondrial electron transport, cytochrome c to oxygen" and "GO:1902600: hydrogen ion transmembrane transport." products) and 601 edges (interactions; Figure 2 ). Two modules, modules 1 and 2, consisted of 27 nodes (340 edges) and 13 nodes (63 edges), respectively. All the genes in modules 1 and 2 were upregulated (Table S1 and Figure 2 ). Functional enrichment analysis showed that DEGs in module 1 were mainly associated with 14 biological processes including "GO:0019083: viral transcription," "GO: 0006364:rRNA processing," and "GO:0002181: cytoplasmic translation," and one KEGG pathway of "hsa03010: Ribosome" (Figure S1A,B) . DEGs in module 2 were associated with eight biological processes related to mitochondrial energy metabolism and seven KEGG pathways related to neurodegenerative diseases ( Figure S1A,B) . Those results showed that genes in modules 1 and 2 had distinct biological functions. Using the TCGA MMRF-CoMMpass cohort (n = 784) and univariate Cox regression analysis, we identified that 30 DEGs of the 83 DEGs in the PPI network were associated with the overall survival time (Table S3) (Table S4) . Also, the Spearman correlation analysis showed that there were significant correlations between the expression levels of the 12 genes (Spearman correlation coefficient, r > .500, p < .05; Table S5 ). Four out of the other 18 DEGs were identified as prognosis-associated DEGs by multivariate Cox regression analysis (Table S4) . Also, 16 genes were considered as potent prognosis-associated DEGs in MM patients in this study. The GSE24080 data set that contained the overall survival data Table 1 ). The expression profiles of the NOD2 (downregulated) and COX6C (upregulated) genes are shown in Figure S2 . The results of F I G U R E 2 Protein-protein interaction network. This network was constructed based on the interactions among the 98 common differentially expressed genes in five data sets. Upregulation and downregulation are indicated by red and green color, respectively. Two modules in the circle lines were identified using MCODE in the Cytoscape prognosis-associated DEGs ( Figure 4A,B) . Also, the NOD2 and COX6C genes were both downregulated in patients with mild COVID-19 infection compared with healthy controls (Figure 4B,C) . However, patients with severe COVID-19 clinical features had higher expression levels of NOD2 and COX6C compared with patients with low NOD2 (p = .0035) and COX6C (p = .0716, insignificance; Figure 4C ) levels, respectively. These results suggested that the NOD2 and COX6C genes might be associated with COVID-19 severity. In summary, our present study showed that patients with MM had a higher expression level of the COX6C gene and genes encoding ribo- infection. Not Applicable. The authors declare that they have no competing interests. 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