Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA767228 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR16216963_1.fastq.gz | Fastqsanger.gz | |||
| SRR16216963_2.fastq.gz | Fastqsanger.gz | |||
| SRR16216964_1.fastq.gz | Fastqsanger.gz | |||
| SRR16216964_2.fastq.gz | Fastqsanger.gz | |||
| SRR16216965_1.fastq.gz | Fastqsanger.gz |
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Project description:Increased proliferation and elevated levels of protein synthesis are characteristic of transformed and tumor cells. Though components of the translation machinery are often misregulated in cancers, how tRNA plays a role in cancer cells has not been explored. We compare genome-wide tRNA expression in tumorigenic versus non-tumorigenic breast cell lines, as well as tRNA expression in breast tumors versus normal breast tissues. In tumorigenic versus non-tumorigenic cell lines, nuclear-encoded tRNAs increase by up to 3-fold and mitochondrial-encoded tRNAs increase by up to 5-fold. In tumors versus normal breast tissues, both nuclear and mitochondrial-encoded tRNAs increase by up to 10-fold. This tRNA over-expression is selective and coordinates with the properties of cognate amino acids. Nuclear- and mitochondrial-encoded tRNAs exhibit distinct expression patterns, indicating that tRNAs can be used as biomarkers for breast cancer. We analyzed tRNA expression levels in 2 non-tumorigenic breast cell lines, 6 tumorigenic breast cancer cell lines, 3 normal breast tissue samples, and 9 breast tumor samples. We used a non-tumorigenic breast cell line (MCF10A) as a reference sample in all hybridizations. All data is dye-swapped.
2010-05-22 | E-GEOD-17945 | biostudies-arrayexpress
Project description:Purpose: Gene expression analysis of knockdown and overexpression of LBH (Limb-Bud-and-Heart) in human breast cancer cell lines using RNA-Seq Methods: RNA was collected and analyzed from three biological replicates of each condition (LBH vs vector) for LBH overexpression (OE) in two human breast cancer cell lines (BT549, MCF7). Additionally, RNA was collected and analyzed from three biological replicates of each condition (siLBH vs non-target/NT siRNA) for LBH knockdown (KD) in two human triple negative breast cancer cells lines (HCC1395, MDA-MB-231). High-throughput sequencing used Illumina platforms. Results: Using an optimized data analysis workflow, we mapped about 60 million sequence reads per sample to the human genome (build hg19). Conclusions: Our study represents the first gene profiling analysis of LBH transcriptomes in human breast cancer cell lines, with biologic replicates, generated by RNA-seq technology.
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Project description:A small number of tumor-derived cell lines have formed the mainstay of cancer therapeutic development, yielding drugs with impact typically measured as months to disease progression. To develop more effective breast cancer therapeutics, and more readily understand their potential clinical impact, we constructed a functional metabolic portrait of 46 independently-derived breast tumorigenic cell lines, contrasted with purified normal breast epithelial subsets, freshly isolated pleural effusion breast tumor samples and culture-adapted, non-tumorigenic mammary epithelial cell derivatives. We report our analysis of glutamine uptake, dependence, and identification of a significant subset of triple negative samples that are glutamine auxotrophs. This NCBI GEO submission comprises a small datasest generated to compare the expression profiles of the above nontumorigenic, purified normal and purified pleural effusion samples with 10 established breast cancer-derived cell lines. This dataset was subsequently merged with a previously published expression dataset derived from 45 independent breast cancer derived cell lines (Neve, et al 2006), and analyses contrasting various subsets of the merged dataset were published. Expression data from 26 samples, no replicates: purified normal human mammary epithelial breast cellular subsets CD10+, BerEP4+, and remaining stromal cell samples from 3 independent anonymous donors; 3 anonymous purified human breast cancer pleural effusion samples; 4 HMEC-derived culture adapted but not transformed samples (184A1, 184B5, HMLE, HMLE-PR); and 10 established human breast cancer cell lines.
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Project description:Increased proliferation and elevated levels of protein synthesis are characteristic of transformed and tumor cells. Though components of the translation machinery are often misregulated in cancers, how tRNA plays a role in cancer cells has not been explored. We compare genome-wide tRNA expression in tumorigenic versus non-tumorigenic breast cell lines, as well as tRNA expression in breast tumors versus normal breast tissues. In tumorigenic versus non-tumorigenic cell lines, nuclear-encoded tRNAs increase by up to 3-fold and mitochondrial-encoded tRNAs increase by up to 5-fold. In tumors versus normal breast tissues, both nuclear and mitochondrial-encoded tRNAs increase by up to 10-fold. This tRNA over-expression is selective and coordinates with the properties of cognate amino acids. Nuclear- and mitochondrial-encoded tRNAs exhibit distinct expression patterns, indicating that tRNAs can be used as biomarkers for breast cancer.
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Project description:Bordel2018 - GSMM for Human Metabolic
Reactions (HMR database)
This model is described in the article:
Constraint based modeling of
metabolism allows finding metabolic cancer hallmarks and
identifying personalized therapeutic windows
Sergio Bordel
Oncotarget. 2018; 9:19716-19729
Abstract:
In order to choose optimal personalized anticancer
treatments, transcriptomic data should be analyzed within the
frame of biological networks. The best known human biological
network (in terms of the interactions between its different
components) is metabolism. Cancer cells have been known to have
specific metabolic features for a long time and currently there
is a growing interest in characterizing new cancer specific
metabolic hallmarks. In this article it is presented a method
to find personalized therapeutic windows using RNA-seq data and
Genome Scale Metabolic Models. This method is implemented in
the python library, pyTARG. Our predictions showed that the
most anticancer selective (affecting 27 out of 34 considered
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cell lines) single metabolic reactions are those involved in
cholesterol biosynthesis. Excluding cholesterol biosynthesis,
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targeting different combinations (from 1 to 5 reactions) of
only 18 metabolic reactions, which suggests that a small subset
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be at the core of metabolism based personalized treatments.
This model is hosted on
BioModels Database
and identified by:
MODEL1707250000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
| MODEL1707250000 | BioModels
Project description:We have generated tumorigenic (S2N) and non-tumorigenic (S2), normal-like to basal-like breast cancer cell lines from primary tumors. At high in vivo inoculation cell numbers of 10^6 cells/mouse both S2N and S2 monolayer as well as sphere culture cells grew at similar rates. However, at low inoculation cell numbers down to 10^3 cells only S2N sphere cells generated xenograft tumors. mRNA profiling revealed a unique cluster pattern of the tumorigenic S2N sphere cells, but a detailed analysis of TIC relevant transcription factors like Oct3, Sox and Nanog family members, Myc, Slug or Twist1 revealed no consistently increased expression in the highly tumorigenic cell lines. Our data indicate that the intrinsic genetic and functional markers investigated are not solely indicative of the in vivo tumorigenicity of putative breast tumor-initiating cells.
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2014-07-31 | E-GEOD-41286 | biostudies-arrayexpress