scholarly journals Integrated Transcriptomic Analysis Reveals a Distinctive Role of YAP1 in Extramedullary Invasion and Therapeutic Sensitivity of Multiple Myeloma

2022 ◽  
Vol 11 ◽  
Author(s):  
Bo Zheng ◽  
Wei Sun ◽  
Ke Yi ◽  
Yajun Zhang ◽  
Liangzhe Wang ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Hematologic Malignancy ◽  
Univariate Analysis ◽  
External Validation ◽  
Transcriptomic Analysis ◽  
Oncogenic Pathways ◽  
Therapeutic Guidelines

Multiple myeloma (MM) is the second most common hematologic malignancy. There are no standard therapeutic guidelines for extramedullary invasion (EM). We performed a retrospective integrated transcriptomic analysis based on GEO, TCGA, and Oncomine datasets with a total of over 2,500 cases enrolled. GSVA analysis was performed on GSE24080. The external validation cohorts include GSE9782, GSE2658, MMRF-COMPASS, and Oncomine. The data of MGUS to relapsed MM were acquired from GSE6477, GSE5900, and Oncomine. The data of EM were acquired from GSE39683 and GSE66291. Single-cell level transcriptome data of MM and EM were acquired from GSE106218. GSVA analysis revealed that 559 cases could be divided into 2 groups based on the expression of oncogenic pathways with prognostic significances. Group 1 with a specific phenotype of YAP1-MYC+ exhibited an unpromising prognosis. The univariate analysis revealed YAP1 as a tumor suppressor in MM. The activity of DNA repair, glycolysis, and oxidative phosphorylation was significantly higher in YAP1-MYC+ MM, which is in concordance with EM myeloma cells based on single-cell analysis. Furthermore, we discovered that YAP1-MYC+ MM patients exhibited an improved response for IMiD treatment. Collectively, YAP1-MYC+MM patients might suffer a worse prognosis and stronger propensity for EM progression.

scholarly journals The multiple myeloma–associated MMSET gene contributes to cellular adhesion, clonogenic growth, and tumorigenicity

Blood ◽  
2008 ◽  
Vol 111 (2) ◽  
pp. 856-864 ◽  
Author(s):  
Josh Lauring ◽  
Abde M. Abukhdeir ◽  
Hiroyuki Konishi ◽  
Joseph P. Garay ◽  
John P. Gustin ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Poor Prognosis ◽  
Target Genes ◽  
Hematologic Malignancy ◽  
Growth Factor Receptor ◽  
Cellular Adhesion ◽  
Tumor Formation ◽  
Set Domain ◽  
Cycle Arrest

Multiple myeloma (MM) is an incurable hematologic malignancy characterized by recurrent chromosomal translocations. Patients with t(4;14)(p16;q32) are the worst prognostic subgroup in MM, although the basis for this poor prognosis is unknown. The t(4;14) is unusual in that it involves 2 potential target genes: fibroblast growth factor receptor 3 (FGFR3) and multiple myeloma SET domain (MMSET). MMSET is universally overexpressed in t(4;14) MM, whereas FGFR3 expression is lost in one-third of cases. Nonetheless, the role of MMSET in t(4;14) MM has remained unclear. Here we demonstrate a role for MMSET in t(4;14) MM cells. Down-regulation of MMSET expression in MM cell lines by RNA interference and by selective disruption of the translocated MMSET allele using gene targeting dramatically reduced colony formation in methylcellulose but had only modest effects in liquid culture. In addition, MMSET knockdown led to cell-cycle arrest of adherent MM cells and reduced the ability of MM cells to adhere to extracellular matrix. Finally, MMSET knockdown and knockout reduced tumor formation by MM xenografts. These results provide the first direct evidence that MMSET plays a significant role in t(4;14) MM and suggest that therapies targeting this gene could impact this particular subset of poor-prognosis patients.

scholarly journals Role of Calcium Signaling Pathway-Related Gene Regulatory Networks in Ischemic Stroke Based on Multiple WGCNA and Single-Cell Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-35
Author(s):  
Weiwei Lin ◽  
Yangxin Wang ◽  
Yisheng Chen ◽  
Qiangwei Wang ◽  
Zhaowen Gu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Calcium Signaling ◽  
Single Cell ◽  
Signaling Pathway ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Kegg Pathways ◽  
Calcium Signaling Pathway ◽  
Core Genes

Background. This study is aimed at investigating the changes in relevant pathways and the differential expression of related gene expression after ischemic stroke (IS) at the single-cell level using multiple weighted gene coexpression network analysis (WGCNA) and single-cell analysis. Methods. The transcriptome expression datasets of IS samples and single-cell RNA sequencing (scRNA-seq) profiles of cerebrovascular tissues were obtained by searching the Gene Expression Omnibus (GEO) database. First, gene pathway scoring was calculated via gene set variation analysis (GSVA) and was imported into multiple WGCNA to acquire key pathways and pathway-related hub genes. Furthermore, SCENIC was used to identify transcription factors (TFs) regulating these core genes using scRNA-seq data. Finally, the pseudotemporal trajectory analysis was used to analyse the role of these TFs on various cell types under hypoxic and normoxic conditions. Results. The scores of 186 KEGG pathways were obtained via GSVA using microarray expression profiles of 40 specimens. WGCNA of the KEGG pathways revealed the two following pathways: calcium signaling pathway and neuroactive ligand-receptor interaction pathways. Subsequently, WGCNA of the gene expression matrix of the samples revealed the calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) that were identified as core genes via correlation analysis. Furthermore, SCENIC and pseudotemporal analysis revealed JUN, IRF9, ETV5, and PPARA score gene-related TFs. Jun was found to be associated with hypoxia in endothelial cells, whereas Irf9 and Etv5 were identified as astrocyte-specific TFs associated with oxygen concentration in the mouse cerebral cortex. Conclusions. Calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) and TFs (JUN, IRF9, ETV5, and PPARA) were identified to play a key role in IS. This study provides a new perspective and basis for investigating the pathogenesis of IS and developing new therapeutic approaches.

Single cell analysis of multiple myeloma.

2018 ◽  
Vol 36 (15_suppl) ◽  
pp. 8026-8026
Author(s):  
Guy Ledergor ◽  
Assaf Weiner ◽  
Mor Zada ◽  
Yael Chava Cohen ◽  
Moshe E Gatt ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis

Abstract A101: Single-cell analysis reveals the pivotal role of the innate immune compartment in aCTLA-4 antitumor response

2020 ◽  
Author(s):  
Ido Yofe ◽  
Adam Jelinski ◽  
Isabelle Solomon ◽  
Tomer Landsberger ◽  
Marc Robert de Massy ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Innate Immune ◽  
Pivotal Role ◽  
Cell Analysis ◽  
Antitumor Response

Longer Duration of Thalidomide Monotherapy Results in Improved Outcome in Relapsed/Refractory Multiple Myeloma.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 2725-2725 ◽  
Author(s):  
Marie von Lilienfeld-Toal ◽  
Corinna Hahn-Ast ◽  
Francesco Bertolini ◽  
Jelena Bila ◽  
Mathieu Boulin ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Platelet Count ◽  
Prognostic Factors ◽  
Cumulative Dose ◽  
Treatment Duration ◽  
Univariate Analysis ◽  
Duration Of Treatment ◽  
Refractory Multiple Myeloma ◽  
Median Daily Dose

Abstract Although efficacy of thalidomide (thal) monotherapy in relapsed/refractory Multiple Myeloma (MM) is proven, the optimal duration of treatment is unknown. Here, we analysed individual patient data of 852 patients (pts.) with relapsed/refractory MM from 21 centres who received thal monotherapy in a trial or in a compassionate use program. To assess the influence of treatment duration, pts. who discontinued thal because of lack of efficacy, progression or death were excluded, so we analysed only pts. who continued on thal as long as they tolerated it or until the study they were enrolled in was complete. 162 pts. were evaluable; 32 completed the trial, 57 had severe neurotoxicity, 2 severe somnolence, 72 other adverse events leading to discontinuation of thal. The median age was 65 years (interquartile range 58–72), 92 (56%) were male, 96 (59%) had relapsed MM. Sixteen (10%) achieved complete remission (CR), 64 (39%) partial remission (PR), 29 (18%) minimal response, 34 (21%) no change, 9 (6%) had progressive disease, 9 (6%) were unevaluable for response. The median daily dose at 3 months was 200 mg/d (100–400), the cumulative dose after 3 months 23200 mg (9025–34150). Median time to response was 3.6 months (3–7), median treatment duration 5 months (2.3–12). Median event-free survival (EFS) was 16 months (95%CI 12–21), median overall survival (OS) 33 months (95%CI 22–44). In univariate analysis, prognostic factors for EFS/OS were leukocyte/platelet count, haemoglobin, cumulative dose at 3 months, duration of treatment and CR/PR. In multivariate analysis, duration of treatment, CR/PR and platelet count remained independent prognostic factors for EFS/OS. If pts. took thal for ≥ 10 months (n=51), median EFS was 31 (95%CI 14–48) vs 12 (9–14, p<0.001) months and median OS 83 (95%CI 49–116) vs 24 (95%CI 17–31, figure 1, p<0.001) months. We analysed responding pts. (n=80) separately to assess the role of treatment duration in maintenance. Patients in CR/PR who took thal ≥ 10 months (n=41) had a longer EFS (39 (95%CI 19–59) vs 18 (95%CI 9–28, p<0.001) and OS (83 (95%CI 49–116) vs 32 (95%CI 20–44, p<0.001) months). This analysis of patients whose duration of treatment was determined by factors independent of disease demonstrates a clear survival benefit of longer duration of thal treatment for relapsed/refractory MM and supports its value as maintenance therapy. Figure Figure

scholarly journals The Role of AP-1 Transcription Factors in Plasma Cell Biology and Multiple Myeloma Pathophysiology

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2326
Author(s):  
Fengjuan Fan ◽  
Klaus Podar
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
Transcription Factors ◽  
Cell Differentiation ◽  
Plasma Cell ◽  
Cell Biology ◽  
Hematologic Malignancy ◽  
Family Members ◽  
Plasma Cell Differentiation

Multiple myeloma (MM) is an incurable hematologic malignancy characterized by the clonal expansion of malignant plasma cells within the bone marrow. Activator Protein-1 (AP-1) transcription factors (TFs), comprised of the JUN, FOS, ATF and MAF multigene families, are implicated in a plethora of physiologic processes and tumorigenesis including plasma cell differentiation and MM pathogenesis. Depending on the genetic background, the tumor stage, and cues of the tumor microenvironment, specific dimeric AP-1 complexes are formed. For example, AP-1 complexes containing Fra-1, Fra-2 and B-ATF play central roles in the transcriptional control of B cell development and plasma cell differentiation, while dysregulation of AP-1 family members c-Maf, c-Jun, and JunB is associated with MM cell proliferation, survival, drug resistance, bone marrow angiogenesis, and bone disease. The present review article summarizes our up-to-date knowledge on the role of AP-1 family members in plasma cell differentiation and MM pathophysiology. Moreover, it discusses novel, rationally derived approaches to therapeutically target AP-1 TFs, including protein-protein and protein-DNA binding inhibitors, epigenetic modifiers and natural products.

scholarly journals The Expression and Prognostic Value of SUMO1-Activating Enzyme Subunit 1 and Its Potential Mechanism in Triple-Negative Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Qingshui Wang ◽  
Wenting Zhong ◽  
Lin Deng ◽  
Qili Lin ◽  
Youyu Lin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Cycle ◽  
Protein Expression ◽  
Single Cell ◽  
Prognostic Model ◽  
Triple Negative ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Mrna And Protein Expression

Background: Triple-negative breast cancer (TNBC) is the most invasive and metastatic subtype of breast cancer. SUMO1-activating enzyme subunit 1 (SAE1), an E1-activating enzyme, is indispensable for protein SUMOylation. SAE1 has been found to be a relevant biomarker for progression and prognosis in several tumor types. However, the role of SAE1 in TNBC remains to be elucidated.Methods: In the research, the mRNA expression of SAE1 was analyzed via the cancer genome atlas (TCGA) and gene expression omnibus (GEO) database. Cistrome DB Toolkit was used to predict which transcription factors (TFs) are most likely to increase SAE1 expression in TNBC. The correlation between the expression of SAE1 and the methylation of SAE1 or quantity of tumor-infiltrating immune cells was further invested. Single-cell analysis, using CancerSEA, was performed to query which functional states are associated with SAE1 in different cancers in breast cancer at the single-cell level. Next, weighted gene coexpression network (WGCNA) was applied to reveal the highly correlated genes and coexpression networks of SAE1 in TNBC patients, and a prognostic model containing SAE1 and correlated genes was constructed. Finally, we also examined SAE1 protein expression of 207 TNBC tissues using immunohistochemical (IHC) staining.Results: The mRNA and protein expression of SAE1 were increased in TNBC tissues compared with adjacent normal tissues, and the protein expression of SAE1 was significantly associated with overall survival (OS) and disease-free survival (DFS). Correlation analyses revealed that SAE1 expression was positively correlated with forkhead box M1 (FOXM1) TFs and negatively correlated with SAE1 methylation site (cg14042711) level. WGCNA indicated that the genes coexpressed with SAE1 belonged to the green module containing 1,176 genes. Through pathway enrichment analysis of the module, 1,176 genes were found enriched in cell cycle and DNA repair. Single-cell analysis indicated that SAE1 and its coexpression genes were associated with cell cycle, DNA damage, DNA repair, and cell proliferation. Using the LASSO COX regression, a prognostic model including SAE1 and polo-like kinase 1 (PLK1) was built to accurately predict the likelihood of DFS in TNBC patients.Conclusion: In conclusion, we comprehensively analyzed the mRNA and protein expression, prognosis, and interaction genes of SAE1 in TNBC and constructed a prognostic model including SAE1 and PLK1. These results might be important for better understanding of the role of SAE1 in TNBC. In addition, DNA methyltransferase and TFs inhibitor treatments targeting SAE1 might improve the survival of TNBC patients.

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