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Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer [RNA-seq]

ABSTRACT: Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer [RNA-seq]

PROVIDER: PRJNA721450 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR14218100.fastq.gz	Fastqsanger.gz
	SRR14218101.fastq.gz	Fastqsanger.gz
	SRR14218102.fastq.gz	Fastqsanger.gz
	SRR14218103.fastq.gz	Fastqsanger.gz
	SRR14218104.fastq.gz	Fastqsanger.gz

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Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

Project description:Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

| PRJNA721449 | ENA

Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

Project description:Triple negative breast cancer is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with normal epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-translational modification. Data integration methods identified regulatory features from each omics method had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFbeta signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling.

2021-06-24 | PXD025238 | Pride

Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer [methylation]

Project description:Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer [methylation]

| PRJNA721453 | ENA

Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer [methylation]

2021-07-01 | GSE171956 | GEO

Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer [RNA-seq]

2021-07-01 | GSE171957 | GEO

Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer.

Project description:Triple negative breast cancer (TNBC) is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with non-tumorigenic epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-translational modification. Data integration methods identified regulatory features from each omics method that had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFβ signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling. We observed overexpression of PTEN, which antagonizes the PI3K/AKT/mTOR pathway, and MYC, which downregulates the same pathway in the HCC1937 cells relative to the MDA-MB-231 cells. The PI3K/AKT/mTOR and Wnt/beta-catenin pathways are both downregulated in HCC1937 cells relative to MDA-MB-231 cells, which likely explains the divergent sensitivities of these cell lines to inhibitors of downstream signaling pathways. The DNA methylation and RNAseq data is freely available via GEO GSE171958 and the proteomics data is available via the ProteomeXchange PXD025238.

| S-EPMC8504614 | biostudies-literature

Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-07-01 | GSE171958 | GEO

Heterogeneity of triple-negative breast cancer

Project description:Heterogeneity of triple-negative breast cancer was examined and compared across morphologic and molecular features

2018-01-01 | GSE97342 | GEO

Transcriptomes of triple negative breast cancer enriched breast cancer cohort

Project description:Breast cancers enriched for the triple negative breast cancer phenotype with extensive clinico-pathological features were profiled to establish their comprehensive transcriptional profiles

2016-11-21 | E-MTAB-5270 | biostudies-arrayexpress

Integration of ATAC-seq and RNA-seq identifies typing and prognosis evaluation target molecules of triple negative breast cancer

Project description:Molecular subtyping plays a vital role in the treatment of breast cancer, however, current clinical outcomes remain unsatisfactory despite having multiple molecular subtypes available, particularly for triple-negative breast cancers. Henceforth, our research primarily focuses on traditional molecular subtyping by utilizing transcriptome sequencing and ATAC-seq sequencing techniques to classify both tumor tissues and adjacent tissues from patients with breasts cancers into distinct groups. Through this process, we aim to identify key gene expressions involved in the development of breasts cancers as well as epigenetic characteristics influenced by alterations in chromatin accessibility patterns. Subsequently, employing conventional methods used for classifying breasts cancers into different molecular types enables us to further investigate significant variations observed specifically within triple-negative breasts cancers regarding their gene expression profiles and chromatin accessibility patterns. Lastly, utilizing data from TCGA database pertaining exclusively to cases involving triple-negative breasts cancers allows us to conduct regression analyses concerning our aforementioned findings while simultaneously selecting relevant molecular models closely associated with this particular subtype of breasts malignancy. Additionally evaluating how these differentially expressed genes influence prognosis through prognostic modeling analysis tailored towards predicting outcomes solely within cases involving individuals diagnosed with triple negative-breast malignancies will enable us ultimately construct an innovative model incorporating both gene expressions along with chromatin accessibility patterns as distinguishing features providing more substantial guidance towards improving clinical treatments targeting individuals afflicted by such conditions.

2025-01-01 | GSE254212 | GEO

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