Multiple myeloma (MM) is characterized by a wide spectrum of genetic changes. Global hypomethylation of repetitive genomic sequences such as long interspersed nuclear element 1 (LINE-1), Alu and satellite alpha (SAT-alpha) sequences has been associated with chromosomal instability in cancer. Methylation status of repetitive elements in MM has never been investigated. In the present study, we used a quantitative bisulfite-polymerase chain reaction pyrosequencing method to evaluate the methylation patterns of LINE-1, Alu and SAT-alpha in 23 human myeloma cell lines (HMCLs) and purified bone marrow plasma cells from 53 newly diagnosed MM patients representative of different molecular subtypes, 7 plasma cell leukemias (PCLs) and 11 healthy controls. MMs showed a decrease of Alu [median: 21.1 %5-methylated cytosine (%5mC)], LINE-1 (70.0%5mC) and SAT-alpha (77.9%5mC) methylation levels compared with controls (25.2, 79.5and 89.5%5mC, respectively). Methylation levels were lower in PCLs and HMCLs compared with MMs (16.7 and 14.8%5mC for Alu, 45.5 and 42.4%5mC for LINE-1 and 33.3 and 43.3%5mC for SAT-alpha, respectively). Notably, LINE-1 and SAT-alpha methylation was significantly lower in the non-hyperdiploid versus hyperdiploid MMs (P = 0.01 and 0.02, respectively), whereas Alu and SAT-alpha methylation was significantly lower in MMs with t(4;14) (P = 0.02 and 0.004, respectively). Finally, we correlated methylation patterns with DNA methyltransferases (DNMTs) messenger RNA levels showing in particular a progressive and significant increase of DNMT1 expression from controls to MMs, PCLs and HMCLs (P < 0.001). Our results indicate that global hypomethylation of repetitive elements is significantly associated with tumor progression in MM and may contribute toward a more extensive stratification of the disease.

Background: The role of microRNAs (miRNAs) in multiple myeloma (MM) has yet to be fully elucidated. To identify miRNAs that are potentially deregulated in MM, we investigated those mapping within transcription units, based on evidence that intronic miRNAs are frequently coexpressed with their host genes. To this end, we monitored host transcript expression values in a panel of 20 human MM cell lines (HMCLs) and focused on transcripts whose expression varied significantly across the dataset. Methods: miRNA expression was quantified by Quantitative Real-Time PCR. Gene expression and genome profiling data were generated on Affymetrix oligonucleotide microarrays. Significant Analysis of Microarrays algorithm was used to investigate differentially expressed transcripts. Conventional statistics were used to test correlations for significance. Public libraries were queried to predict putative miRNA targets. Results: We identified transcripts specific to six miRNA host genes (CCPG1, GULP1, EVL, TACSTD1, MEST, and TNIK) whose average changes in expression varied at least 2-fold from the mean of the examined dataset. We evaluated the expression levels of the corresponding intronic miRNAs and identified a significant correlation between the expression levels of MEST, EVL, and GULP1 and those of the corresponding miRNAs miR-335, miR-342-3p, and miR-561, respectively. Genome-wide profiling of the 20 HMCLs indicated that the increased expression of the three host genes and their corresponding intronic miRNAs was not correlated with local copy number variations. Notably, miRNAs and their host genes were overexpressed in a fraction of primary tumors with respect to normal plasma cells; however, this finding was not correlated with known molecular myeloma groups. The predicted putative miRNA targets and the transcriptional profiles associated with the primary tumors suggest that MEST/miR-335 and EVL/miR-342-3p may play a role in plasma cell homing and/or interactions with the bone marrow microenvironment. Conclusion: Our data support the idea that intronic miRNAs and their host genes are regulated dependently, and may contribute to the understanding of their biological roles in cancer. To our knowledge, this is the first evidence of deregulated miRNA expression in MM, providing insights that may lead to the identification of new biomarkers and altered molecular pathways of the disease.

Distinct lncRNA transcriptional fingerprints characterize progressive stages of multiple myeloma

Purpose: To determine a “gene/molecular fingerprint” of multiple myeloma endothelial cells and identify vascular mechanisms governing the malignant progression from quiescent monoclonal gammopathy of undetermined significance. Experimental Design: Comparative gene expression profiling of multiple myeloma endothelial cells and monoclonal gammopathy of undetermined significance endothelial cells with the Affymetrix U133A Arrays was carried out in patients at diagnosis; expression and function of selective vascular markers was validated by real-time reverse transcriptase-PCR, Western blot, and small interfering RNA analyses. Results: Twenty-two genes were found differentially expressed (14 down-regulated and eight up-regulated) at relatively high stringency in multiple myeloma endothelial cells compared with monoclonal gammopathy of undetermined significance endothelial cells. Functional annotation revealed a role of these genes in the regulation of extracellular matrix formation and bone remodeling, cell adhesion, chemotaxis, angiogenesis, resistance to apoptosis, and cell-cycle regulation. Validation was focused on six genes (DIRAS3, SERPINF1, SRPX, BNIP3, IER3, and SEPW1) not previously found to be functionally correlated to the overangiogenic phenotype of multiple myeloma endothelial cells in active disease. The small interfering RNA knockdown of BNIP3, IER3, and SEPW1 genes affected critical multiple myeloma endothelial cell functions correlated with the overangiogenic phenotype. Conclusions: The distinct endothelial cell gene expression profiles and vascular phenotypes detected in this study may influence remodeling of the bone marrow microenvironment in patients with active multiple myeloma. A better understanding of the linkage between plasma cells and endothelial cells in multiple myeloma could contribute to the molecular classification of the disease and thus pinpoint selective gene targets for more effective antiangiogenic treatments. (Clin Cancer Res 2009;15(17):5369–78)