Age-Related Co-Expression of BCOR and BCORL1 mRNA in Acute Myeloid Leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is an aggressive hematological malignancy caused by a variety of genetic abnormalities and epigenetic dysregulation. The incidence of AML is strongly related to age, with the highest incidence rates being in older adults. The loss of function mutations in BCOR and BCORL1 genes have been identified in AML. BCL6 corepressor (BCOR) and BCL6 corepressor like 1 (BCORL1) are important epigenetic regu-lators as a member of Polycomb repressive complex 1 (PRC1.1), involved in histone modification processes. METHODS: We analyzed the BCOR and BCORL1 mRNA expression in 74 adult and 22 pediatric patients with AML by Real-Time quantitative PCR in this study. RESULTS: Our results indicated that both BCOR and BCORL1 mRNA expressions decrease with age (p = 0.009 and p = 0.008, respectively) and there is a positive correlation between BCOR and BCORL1 mRNA expression (p < 0.001). BCOR and BCORL1 mRNA expressions were not significantly different in both adult and pediatric patients with AML compared to control (p > 0.05). CONCLUSIONS: Our findings indicate that expression of BCOR and BCORL1 mRNA are down-regulated with age. The increase in AML incidence with age suggests that age-associated BCOR and BCORL1 down-regulation might potentially contribute to age-related epigenetic alterations and form a predisposing condition for the development of elderly AML.

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

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.