Evidence map›Paper›PMID 34623759›Full record

ArticleInternational journal of laboratory hematology2021

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.

Fei Wang, Ran Liu, Jie Yang, Baoan Chen

Open access · hybridAbstract readComparative StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of laboratory hematology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.5field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 13 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Fei WangDepartment of Hematology (Key Department of Jiangsu Medicine), Medical School, Zhongda Hospital, Southeast University, Institute of Hematology Southeast University, Nanjing, China.
Ran LiuDepartment of Quality Management, Medical School, Zhongda Hospital, Southeast University, Institute of Hematology Southeast University, Nanjing, China.
Jie YangDepartment of Hematology (Key Department of Jiangsu Medicine), Medical School, Zhongda Hospital, Southeast University, Institute of Hematology Southeast University, Nanjing, China.ORCID https://orcid.org/0000-0001-7179-4651
Baoan ChenDepartment of Hematology (Key Department of Jiangsu Medicine), Medical School, Zhongda Hospital, Southeast University, Institute of Hematology Southeast University, Nanjing, China.ORCID https://orcid.org/0000-0003-3978-7886
Zhongda Hospital Southeast University · CN

Funding

Jiangsu Provincial Medical Youth Talent QNRC2016812Natural Science Foundation of Jiangsu Province for Youth BK20180372
6 · The paper itself

Abstract

backgroundMultiple 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.

methodsDifferentially 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.

resultsA 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.

conclusionsThe 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.

Indexed as

Gene Expression ProfilingSARS-CoV-2Computational BiologyCOVID-19Datasets as TopicElectron Transport Complex IVGene Expression Regulation, NeoplasticGene Expression Regulation, ViralGene OntologyHumansKaplan-Meier EstimateMicroarray AnalysisMultiple MyelomaNeoplasm ProteinsNod2 Signaling Adaptor ProteinPrognosisCOX6c protein, humanElectron Transport Complex IVNeoplasm ProteinsNOD2 protein, humanNod2 Signaling Adaptor Proteinbioinformatics analysiscoronavirus disease 2019multiple myelomaoverall survival

Identifiers

PMID34623759
PMCPMC8652836
OpenAlexW3203000983

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.