Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data

Abstract Multiple myeloma (MM) is a plasma cell malignancy with diverse clinical phenotypes and molecular heterogeneity not completely understood. Differentially expressed genes (DEGs) and miRNAs (DEMs) in MM may influence disease pathogenesis, clinical presentation / drug sensitivities. But these s...

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Autores principales:	Amit Katiyar, Gurvinder Kaur, Lata Rani, Lingaraja Jena, Harpreet Singh, Lalit Kumar, Atul Sharma, Punit Kaur, Ritu Gupta
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c336c17869354085b7e7978cdfe6aff4
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spelling	oai:doaj.org-article:c336c17869354085b7e7978cdfe6aff42021-12-02T14:42:01ZGenome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data10.1038/s41598-021-90424-y2045-2322https://doaj.org/article/c336c17869354085b7e7978cdfe6aff42021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90424-yhttps://doaj.org/toc/2045-2322Abstract Multiple myeloma (MM) is a plasma cell malignancy with diverse clinical phenotypes and molecular heterogeneity not completely understood. Differentially expressed genes (DEGs) and miRNAs (DEMs) in MM may influence disease pathogenesis, clinical presentation / drug sensitivities. But these signatures overlap meagrely plausibly due to complexity of myeloma genome, diversity in primary cells studied, molecular technologies/ analytical tools utilized. This warrants further investigations since DEGs/DEMs can impact clinical outcomes and guide personalized therapy. We have conducted genome-wide meta-analysis of DEGs/DEMs in MM versus Normal Plasma Cells (NPCs) and derived unified putative signatures for MM. 100 DEMs and 1,362 DEGs were found deranged between MM and NPCs. Signatures of 37 DEMs (‘Union 37’) and 154 DEGs (‘Union 154’) were deduced that shared 17 DEMs and 22 DEGs with published prognostic signatures, respectively. Two miRs (miR-16–2-3p, 30d-2-3p) correlated with survival outcomes. PPI analysis identified 5 topmost functionally connected hub genes (UBC, ITGA4, HSP90AB1, VCAM1, VCP). Transcription factor regulatory networks were determined for five seed DEGs with ≥ 4 biomarker applications (CDKN1A, CDKN2A, MMP9, IGF1, MKI67) and three topmost up/ down regulated DEMs (miR-23b, 195, let7b/ miR-20a, 155, 92a). Further studies are warranted to establish and translate prognostic potential of these signatures for MM.Amit KatiyarGurvinder KaurLata RaniLingaraja JenaHarpreet SinghLalit KumarAtul SharmaPunit KaurRitu GuptaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Amit Katiyar Gurvinder Kaur Lata Rani Lingaraja Jena Harpreet Singh Lalit Kumar Atul Sharma Punit Kaur Ritu Gupta Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data
description	Abstract Multiple myeloma (MM) is a plasma cell malignancy with diverse clinical phenotypes and molecular heterogeneity not completely understood. Differentially expressed genes (DEGs) and miRNAs (DEMs) in MM may influence disease pathogenesis, clinical presentation / drug sensitivities. But these signatures overlap meagrely plausibly due to complexity of myeloma genome, diversity in primary cells studied, molecular technologies/ analytical tools utilized. This warrants further investigations since DEGs/DEMs can impact clinical outcomes and guide personalized therapy. We have conducted genome-wide meta-analysis of DEGs/DEMs in MM versus Normal Plasma Cells (NPCs) and derived unified putative signatures for MM. 100 DEMs and 1,362 DEGs were found deranged between MM and NPCs. Signatures of 37 DEMs (‘Union 37’) and 154 DEGs (‘Union 154’) were deduced that shared 17 DEMs and 22 DEGs with published prognostic signatures, respectively. Two miRs (miR-16–2-3p, 30d-2-3p) correlated with survival outcomes. PPI analysis identified 5 topmost functionally connected hub genes (UBC, ITGA4, HSP90AB1, VCAM1, VCP). Transcription factor regulatory networks were determined for five seed DEGs with ≥ 4 biomarker applications (CDKN1A, CDKN2A, MMP9, IGF1, MKI67) and three topmost up/ down regulated DEMs (miR-23b, 195, let7b/ miR-20a, 155, 92a). Further studies are warranted to establish and translate prognostic potential of these signatures for MM.
format	article
author	Amit Katiyar Gurvinder Kaur Lata Rani Lingaraja Jena Harpreet Singh Lalit Kumar Atul Sharma Punit Kaur Ritu Gupta
author_facet	Amit Katiyar Gurvinder Kaur Lata Rani Lingaraja Jena Harpreet Singh Lalit Kumar Atul Sharma Punit Kaur Ritu Gupta
author_sort	Amit Katiyar
title	Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data
title_short	Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data
title_full	Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data
title_fullStr	Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data
title_full_unstemmed	Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data
title_sort	genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mrna and mirna expression data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c336c17869354085b7e7978cdfe6aff4
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Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data

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