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Investigation of Gene Expressions of Myeloma Cells in the Bone Marrow of Multiple Myeloma Patients by Transcriptome Analysis

Authors :
Melda Sarıman
Neslihan Abacı
Sema Sırma Ekmekçi
Aris Çakiris
Ferda Perçin Paçal
Duran Üstek
Mesut Ayer
Mustafa Nuri Yenerel
Sevgi Beşışık
Kıvanç Çefle
Şükrü Palandüz
Şükrü Öztürk
Source :
Balkan Medical Journal, Vol 36, Iss 1, Pp 23-31 (2019)
Publication Year :
2019
Publisher :
Galenos Publishing House, 2019.

Abstract

Background: Multiple myeloma is a plasma cell dyscrasia characterized by transformation of B cells into malignant cells. Although there are data regarding the molecular pathology of multiple myeloma, the molecular mechanisms of the disease have not been fully elucidated. Aims: To investigate the gene expression profiles in bone marrow myeloma cells via RNA-sequencing technology. Study Design: Cell study. Methods: Myeloma cells from four patients with untreated multiple myeloma and B cells from the bone marrow of four healthy donors were sorted using a FACSAria II flow cytometer. The patient pool of myeloma cells and the control pool of B cells were the two comparative groups. A transcriptome analysis was performed and the results were analyzed using bioinformatics tools. Results: In total, 18.806 transcripts (94.4%) were detected in the pooled multiple myeloma patient cells. A total of 992 regions were detected as new exon candidates or alternative splicing regions. In addition, 490 mutations (deletions or insertions), 1.397 single nucleotide variations, 415 fusion transcripts, 132 frameshift mutations, and 983 fusions, which were reported before in the National Center for Biotechnology Information, were detected with unknown functions in patients. A total of 35.268 transcripts were obtained (71%) (25.355 transcripts were defined previously) in the control pool. In this preliminary study, the first 50 genes were analyzed with the MSigDB, Enrichr, and Panther gene set enrichment analysis programs. The molecular functions, cellular components, pathways, and biological processes of the genes were obtained and statistical values were determined using bioinformatics tools and are presented as a supplemental file. Conclusion: EEF1G, ITM2C, FTL, CLPTM1L, and CYBA are identified as possible candidate genes associated with myelomagenesis.

Details

Language :
English
ISSN :
21463123 and 21463131
Volume :
36
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Balkan Medical Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.6e465c95b66b417ab3cfbcd8bf7ca046
Document Type :
article
Full Text :
https://doi.org/10.4274/balkanmedj.2018.0356