Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA767228 | ENA |
REPOSITORIES: ENA
Similar Datasets
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: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.
2013-11-22 | E-GEOD-48984 | biostudies-arrayexpress
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.
2009-09-29 | GSE17945 | GEO
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. 4 samples with 3 replicates each
2012-12-21 | E-GEOD-32526 | biostudies-arrayexpress
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
cancer cell lines and only 1 out of 6 healthy mesenchymal stem
cell lines) single metabolic reactions are those involved in
cholesterol biosynthesis. Excluding cholesterol biosynthesis,
all the considered cell lines can be selectively affected by
targeting different combinations (from 1 to 5 reactions) of
only 18 metabolic reactions, which suggests that a small subset
of drugs or siRNAs combined in patient specific manners could
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.
2012-12-21 | GSE32526 | GEO
Project description:Protein profiling of 18 breast cancer cell lines in triplicates using TMT10-plex.
2020-07-04 | PXD013276 | JPOST Repository
Project description:Bulk cancer cell populations are known to display distinct metabolic properties compared to their normal counterparts. However, relatively little is known about heterogeneity of metabolic properties within tumors. In this study we show that, analogous to some normal stem cells, breast tumor initiating cells (TICs, also called cancer stem cells) have distinct metabolic properties compared to non-tumorigenic cancer cells (NTCs). Transcriptome profiling using RNA-Seq revealed TICs under-express genes involved in mitochondrial oxidative phosphorylation and although TICs are relatively quiescent, they preferentially perform glycolysis over oxidative phosphorylation compared to NTCs. TICs contain fewer mitochondria and display lower expression and activity of pyruvate dehydrogenase (Pdh), a key regulator of oxidative phosphorylation. Metabolic reprogramming of TICs by pharmacologic activation of Pdh preferentially eliminates TICs in vitro and in vivo. Our findings reveal unique metabolic properties of TICs and indicate that metabolic reprogramming represents a promising strategy for targeting these cells. Examination transcriptome profiles for breast tumor initiating cells (TICs) and non-tumorigenic cells (NTCs)
2014-07-31 | E-GEOD-41286 | biostudies-arrayexpress