Cadherin‐11 (<scp>CDH</scp>11) expression in the peripheral blood of patients with active Multiple Myeloma

Christopoulos, Panagiotis; Sfikakis, Petros P.; Nikolaou, Eftychia; Terpos, Evangelos; Koutsilieris, Michael; Kyrtsonis, Marie‐Christine

doi:10.1111/bjh.14105

Cited by 1 publication

(2 citation statements)

References 10 publications

(11 reference statements)

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“…PPARγ agonists significantly suppress the adhesive interaction between MM and bone marrow stromal cells (Wang, Yang, Zhang, & Farrar, 2007) and increase the cytotoxic effect of other drugs in MM cells (Aouali et al, 2009). (contributing to drug resistance in MM; Belloni et al, 2018) and cadherin 11 (CDH11; involved in MM progression and bone destruction; Christopoulos et al, 2017). Interrelation analysis via assessment of the BPs of downregulated DEGs was consistent with the above results.…”

Section: Survival Analysis Of Degssupporting

confidence: 82%

“…Subsequently, interrelation analysis was performed. Immune system process analysis of downregulated DEGs indicated that B cell differentiation, positive regulation of T cell proliferation, and leukocyte tethering or rolling interact with each other through key genes, such as spleen‐associated tyrosine kinase (SYK; related to MM cell survival and migration; Lorenz et al, ), complement Component 3d receptor 2 (linked to the innate complement‐mediated immune response; Sorman, Zhang, Ding, & Heyman, ), VCAM1 (contributing to drug resistance in MM; Belloni et al, ) and cadherin 11 (CDH11; involved in MM progression and bone destruction; Christopoulos et al, ). Interrelation analysis via assessment of the BPs of downregulated DEGs was consistent with the above results.…”

Section: Discussionmentioning

confidence: 99%

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Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis

Yan

Zheng

et al. 2019

Journal Cellular Physiology

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Multiple myeloma (MM) is a common hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. This study aimed to elucidate key candidate genes and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE6477 and GSE47552 were obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) with p < .05 and [logFC] > 1 were identified. Functional enrichment, protein–protein interaction network construction and survival analyses were then performed. First, 51 upregulated and 78 downregulated DEGs shared between the two GSE datasets were identified. Second, functional enrichment analysis showed that these DEGs are mainly involved in the B cell receptor signaling pathway, hematopoietic cell lineage, and NF‐kappa B pathway. Moreover, interrelation analysis of immune system processes showed enrichment of the downregulated DEGs mainly in B cell differentiation, positive regulation of monocyte chemotaxis and positive regulation of T cell proliferation. Finally, the correlation between DEG expression and survival in MM was evaluated using the PrognoScan database. In conclusion, we identified key candidate genes that affect the outcomes of patients with MM, and these genes might serve as potential therapeutic targets.

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