Dataset Information

Identification of TSGs Frequently Methylated in Renal Cell carcinoma (expression profiles of Renal cell lines following de-methylation)

ABSTRACT: Promoter region hypermethylation and transcriptional silencing is a frequent cause of tumour suppressor gene (TSG) inactivation in many types of human cancers. Functional epigenetic studies, in which gene expression is induced by treatment with demethylating agents, may identify novel genes with tumour-specific methylation. We used high-density gene expression microarrays in a functional epigenetic study of 11 renal cell carcinoma (RCC) cell lines. Twenty eight genes were then selected for analysis of promoter methylation status in cell lines and primary RCC. Eight genes (BNC1, PDLIM4/RIL, REPRIMORPRM, CST6, SFRP1, GREM1, COL14A1 and COL15A1) demonstrated frequent (>30% of RCC tested) tumour-specific promoter region methylation. Hypermethylation was associated with transcriptional silencing. Re-expression of BNC1, CST6, RPRM, and SFRP1 suppressed the growth of RCC cell lines. Whereas, RNAi-knock-down of BNC1, SFRP1 and COL14A1 increased the growth potential of RCC cell lines. Methylation of BNC1 or COL14A1 was associated with a poorer prognosis independent of tumour size, stage or grade. The identification of these epigenetically inactivated candidate RCC tumour suppressor genes can provide insights into renal tumourigenesis and a basis for developing novel therapies and biomarkers for prognosis and detection.

ORGANISM(S): Homo sapiens

PROVIDER: GSE22563 | GEO | 2010/06/25

SECONDARY ACCESSION(S): PRJNA128425

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Recent advancement in cancer research has shown that tumours are highly heterogeneous and multiple phenotypically different cell populations are found in single nodule. Cancer development and tumour growth is driven by specific type of cells - cancer stem cells or tumour initiating cells (CSCs/TICs), which are to be responsible for tumour growth, metastatic spread and drug resistance. This research was designed to verify the presence of tumour initiating cells in renal cancer cell lines. Subsequently, we aimed to characterize phenotype and cell biology of CD105+ cells, defined previously as renal cell carcinoma tumour initiating cells. Main goal of the project was to describe gene expression profile of tumour initiating cells originating from primary tumour and of metastatic origin. Metastatic RCC cell lines (ACHN and Caki-1) demonstrated higher colony forming ability comparing to primary RCC cell lines. We investigated presence of CD105+ cells in RCC cell lines. Furthermore, metastatic RCC cell lines have higher CD105+ cell population and higher expression for stemness genes (Oct-4 and Nanog). CD105+ cells adopt 3D grapes like structure under handing drop condition. Sorted CD105+ cells were found positive for human MSC makers such as CD90, CD73, CD44, CD146 and alkaline phosphatase activity. In addition, CD105+ cells were unable to expressed maker for CD24, CD34, CD11b, CD19, CD45 and HLA-DR. 1411 genes were commonly differentially expressed in CD105+ cells (both from primary (Caki-2) and metastatic RCC (ACHN)) cells in comparison to healthy kidney epithelial cell line (ASE-5063). TGF-β, Wnt/β-catenine, Epithelial-Mesenchymal Transition (EMT), Rap1 signaling, PI3K-Akt signaling and Hippo signaling pathway were altered in CD105+ cells after IPA and KEGG pathway analysis. TGFB1, ERBB2, and TNF are most significant transcriptional regulators activated in these cells. Altogether, our results indicate that RCC-CD105+ cells may represents a novel target for CSCs/TICs phenotype and their gene expression profile could be used as initial data for new functional studies and drug design.

Dataset Information

Identification of TSGs Frequently Methylated in Renal Cell carcinoma (expression profiles of Renal cell lines following de-methylation)

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets