scholarly journals Gene expression profiling of microRNAs associated with UCA1 in bladder cancer cells

2016 ◽  
Vol 48 (4) ◽  
pp. 1617-1627 ◽  
Author(s):  
XIAOJUAN XIE ◽  
JINGJING PAN ◽  
LIQIANG WEI ◽  
SHOUZHEN WU ◽  
HUILIAN HOU ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Profiling ◽  
Cancer Cells ◽  
Expression Profiling ◽  
Bladder Cancer Cells

scholarly journals Silencing TAK1 alters gene expression signatures in bladder cancer cells

2017 ◽  
Vol 13 (5) ◽  
pp. 2975-2981 ◽  
Author(s):  
Jimin Chen ◽  
Nan Zhang ◽  
Jiaming Wen ◽  
Zhewei Zhang
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Cancer Cells ◽  
Bladder Cancer Cells ◽  
Gene Expression Signatures

scholarly journals Controversial T1G3 bladder cancer is the key to revealing the changes in the biological functions of bladder cancer cells

2020 ◽  
Author(s):  
Shen Pan ◽  
Yunhong Zhan ◽  
Xiaonan Chen ◽  
Bin Wu ◽  
Bitian Liu
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Cancer Cells ◽  
Interaction Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Specific Gene ◽  
Gene Sets ◽  
Bladder Cancer Cells

Abstract Background T1G3 shows a higher chance of recurrence and progression among early bladder cancer types and the available treatment option is controversial. High recurrence and progression are the problems that need to be explored and solved. Changes in the internal signals of bladder cancer cells and differential genes may be the root cause of these problems. Methods GSE120736, GSE19915, GSE19423, GSE32548 and GSE37815 datasets were obtained from Gene Expression Omnibus (GEO ) to identify differentially expressed genes (DEGs). Bladder cancer transcript data from The Cancer Genome Atlas (TCGA) were clustered into different cell-specific gene sets according to weighted gene co-expression network analysis (WGCNA). Multiple sets of databases were used for gene expression comparison, functional enrichment, and protein interaction analysis, including The Human Protein Atlas, Cancer Dependency Map, Metascape, Gene set enrichment analysis, and DisNor. Results DEGs were obtained through GEO data comparison and intersection. After WGCNA was proven to recognise cell-specific gene sets, candidate DEGs were selected and shown to be specifically expressed in cancer cells. Candidate DEGs were related to mitosis and cell cycle. Further, 12 functional candidate markers were identified from the sequencing data of 30 bladder cancer cell lines. These genes were all up-regulated and previously shown to be closely related to bladder cancer progression. Conclusions Twelve functional genes with specific differential expression in bladder cancer cells were identified. WGCNA can identify the relatively specific expression sets of different cells in bladder cancer with greater tumour heterogeneity, which provides new perspectives for future cancer research.

scholarly journals Combining differential expression, chromosomal and pathway analyses for the molecular characterization of renal cell carcinoma

2012 ◽  
Vol 1 (2S) ◽  
Author(s):  
Kyle A. Furge ◽  
Karl Dykema ◽  
David Petillo ◽  
Michael Westphal ◽  
Zhongfa Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Gene Expression Profiling ◽  
Cancer Cells ◽  
Expression Profiling ◽  
Renal Cell ◽  
Molecular Signatures ◽  
Genetic Makeup ◽  
Papillary Rcc

Using high-throughput gene-expression profiling technology, we can now gaina better understanding of the complex biology that is taking place in cancer cells. This complexity is largely dictated by the abnormal genetic makeup ofthe cancer cells. This abnormal genetic makeup can have profound effectson cellular activities such as cell growth, cell survival and other regulatory processes. Based on the pattern of gene expression, or molecular signatures of the tumours, we can distinguish or subclassify different types of cancers according to their cell of origin, behaviour, and the way they respond to therapeuticagents and radiation. These approaches will lead to better molecularsubclassification of tumours, the basis of personalized medicine. We have, todate, done whole-genome microarray gene-expression profiling on several hundredsof kidney tumours. We adopt a combined bioinformatic approach, based on an integrative analysis of the gene-expression data. These data are used toidentify both cytogenetic abnormalities and molecular pathways that are deregulatedin renal cell carcinoma (RCC). For example, we have identified the deregulationof the VHL-hypoxia pathway in clear-cell RCC, as previously known,and the c-Myc pathway in aggressive papillary RCC. Besides the more commonclear-cell, papillary and chromophobe RCCs, we are currently characterizingthe molecular signatures of rarer forms of renal neoplasia such ascarcinoma of the collecting ducts, mixed epithelial and stromal tumours, chromosomeXp11 translocations associated with papillary RCC, renal medullarycarcinoma, mucinous tubular and spindle-cell carcinoma, and a group of unclassified tumours. Continued development and improvement in the field of molecular profiling will better characterize cancer and provide more accurate diagnosis, prognosis and prediction of drug response.

Association of myeloid suppressor cells with resistance to checkpoint blockade immunotherapy in urothelial carcinoma.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 559-559
Author(s):  
Debasish Sundi ◽  
Megan Christina Duggan ◽  
Himanshu Savardekar ◽  
Hyunwoo Kwon ◽  
Steven Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Profiling ◽  
Immune Cells ◽  
Peripheral Blood ◽  
Expression Profiling ◽  
Suppressor Cells ◽  
Complete Response ◽  
Checkpoint Blockade ◽  
Myeloid Suppressor Cells

559 Background: Myeloid immune cells such as myeloid derived suppressor cells (MDSC) and tumor associated macrophages (TAM) have been hypothesized to cause resistance to immune checkpoint blockade (ICB). This is a pressing clinical problem for patients with bladder cancer. Here we determined if we could identify immune cells associated with resistance to ICB in the BBN963 mouse model, and if we could identify therapeutic strategies to target those same suppressor immune cells from patients with bladder cancer. Methods: BBN963 subcutaneous allografts were established in C57BL6/J mice. Response to anti-PD-L1 ICB was classified as partial or complete response according to RECIST criteria. Immune cell subsets with the tumors was evaluated by gene expression profiling and flow cytometry. Peripheral blood from patients with bladder cancer was collected under an IRB-approved protocol. MDSC were purified by flow sorting (CD11b+ CD33+ HLA-DRlow/neg) and screened for viability (Annexin-V staining) after 24 hours of exposure to a panel potential MDSC inhibitors. Results: 16/22 (72%) subjects met criteria for partial or complete response, while 6/22 (28%) were classified as anti-PD-L1 non-responders. Mice in the control group had a 0/10 (0%) response to isotype control (IgG) treatment. Monocytic MDSC (CD11b+ Ly6C+) were much more frequent among the intratumoral CD45+ cells of non-responding subjects as compared to control mice. Nanostring immune panel gene expression profiling revealed that combination treatment of tumor bearing mice with anti-PD-L1 plus ibrutinib (a putative MDSC inhibitor) decreased mRNA biomarkers of tumor-infiltrating macrophages. In vitro screening of patient-derived peripheral blood mononuclear cells showed that an inhibitor of the bromodomain and extraterminal domain (BET) family BRD4 specifically decreased MDSC viability. Conclusions: Monocytic MDSC appear to be associated with resistance to anti-PD-L1 ICB in a new murine model. Analysis of MDSC from patients with bladder cancer suggests that these myeloid suppressor cells can be specifically targeted.

scholarly journals Gene expression profiling identifies novel key players involved in the cytotoxic effect of Artesunate on pancreatic cancer cells

2009 ◽  
Vol 78 (3) ◽  
pp. 273-283 ◽  
Author(s):  
Mahmoud Youns ◽  
Thomas Efferth ◽  
Jürgen Reichling ◽  
Kurt Fellenberg ◽  
Andrea Bauer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Expression Profiling ◽  
Cancer Cells ◽  
Expression Profiling ◽  
Cytotoxic Effect ◽  
Pancreatic Cancer Cells ◽  
Key Players
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