Dataset Information

Charting the regulatory landscape of TP53 on transposable elements in cancer [ATAC-seq]

ABSTRACT: Comprehensive profiling of transposable element (TE)-derived transcripts dynamics under the regulation of p53 provides valuable resources for more clarity in p53 pleiotropic roles in cancer. In this project, we created three cancer cell lines with p53 genetic status as the only variable and used long-read RNA-seq to profile TE and TE-derived transcripts’ regulatory dynamics. By using both short-read RNA-seq and long-read RNA-seq, novel TE-derived transcripts as a function of p53 status were accurately profiled for the first time. We identified in total 2503 transcripts that use TE as potential promoters, among which, 141 to 210 are activated by p53 across three cancer cell lines. We found ERVs to serve as potential driving promoters, followed by LINEs. Epigenomic information from chromatin accessibility and DNA methylation provided additional support for active promoter potential for p53 upregulated TE-derived transcripts. Short-term restoration of p53 partially recovered chronic p53-regulated TE-derived transcriptional profile; whereas gain of function TP53 mutations, R175H and R273H, did not show evidence to act via TE network. Overall, in this paper, we provide a controlled isogenic cancer cell line system with TP53 mutation status as the only genetic variable, deliver a high confidence TE and TE-derived transcript atlas, and comprehensively identify active TE promoters that are direct and indirect targets of p53.

ORGANISM(S): Homo sapiens

PROVIDER: GSE262051 | GEO | 2024/10/20

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background: Gene expression profiling of breast carcinomas has increased our understanding of the heterogeneous biology of this disease and promises to impact clinical care. The aim of this study was to evaluate the prognostic value of gene expression-based classification along with established prognostic markers and mutation status of the TP53 gene, in a group of breast cancer patients with long-term (12-16 years) follow-up. Methods: The clinical and histopathological parameters of 200 breast cancer patients were studied for their effects on clinical outcome using univariate/multivariate Cox regression. The prognostic impact of mutations in the TP53 gene, identified using TTGE and sequencing, was also evaluated. Eighty of the samples were analyzed for gene expression using 42K cDNA microarrays and the patients were assigned to five previously defined molecular expression groups. The strength of the gene expression based classification versus standard markers was evaluated by adding this variable to the Cox regression model used to analyze all samples. Results: Both univariate and multivariate analysis showed that TP53 mutation status, tumor size and lymph node status were the strongest predictors of breast cancer survival for the whole group of patients. Analyses of the patients with gene expression data showed that TP53 mutation status, gene expression based classification, tumor size and lymph node status were significant predictors of survival. The TP53 mutation status showed strong association with the ?basal-like? and ?ERBB2+? gene expression subgroups, and tumors with mutation had a characteristic gene expression pattern. Conclusions: TP53 mutation status and gene-expression based groups are important survival markers of breast cancer, and these molecular markers may provide prognostic information that complements clinical variables. The study adds experience and knowledge to an ongoing characterization and classification of the disease. Experiment set consisting of 80 primary breast carcinomas collected at Ulleval University Hospital (ULL-samples), Oslo, Norway from 1990-94, and one normal sample from breast reduction surgery.

Dataset Information

Charting the regulatory landscape of TP53 on transposable elements in cancer [ATAC-seq]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets