Evidence map›Paper›PMID 21799915›Full record

ArticlePloS one2011

ChIP-seq defined genome-wide map of TGFβ/SMAD4 targets: implications with clinical outcome of ovarian cancer.

Brian A Kennedy, Daniel E Deatherage, Fei Gu, Binhua Tang, Michael W Y Chan, Kenneth P Nephew, Tim H-M Huang, Victor X Jin

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 29 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. TGF-β - an excellent servant but a bad master.Journal of translational medicine · 2012
    Review
  17. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 2 countries.

Brian A KennedyDepartment of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America.
Daniel E Deatherage
Fei Gu
Binhua Tang
Michael W Y Chan
Kenneth P Nephew
Tim H-M Huang
Victor X Jin
The Ohio State University · USCancer Genetics (United States) · USIndiana University Bloomington · USNational Chung Cheng University · TW

Funding

Predicting Drug Resistance in Cancer Genomes by DMA Methylation ProfilingU54CA113001 · NCI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI HUANG, TIM H.-M. · 2004 to 2014
$16.1M
DNA Hypermethylation in Breast CancerR01CA069065 · NCI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI HUANG, TIM H.-M. · 2002 to 2011
$2.4M
DNA Methylation and Ovarian CancerR01CA085289 · NCI · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI NEPHEW, KENNETH P · 2002 to 2012
$2.4M
NCI NIH HHS CA85289NCI NIH HHS R01 CA069065NCI NIH HHS R01CA069065NCI NIH HHS R01 CA085289NCI NIH HHS U54 CA113001NCI NIH HHS U54CA113001
6 · The paper itself

Abstract

Deregulation of the transforming growth factor-β (TGFβ) signaling pathway in epithelial ovarian cancer has been reported, but the precise mechanism underlying disrupted TGFβ signaling in the disease remains unclear. We performed chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate genome-wide screening of TGFβ-induced SMAD4 binding in epithelial ovarian cancer. Following TGFβ stimulation of the A2780 epithelial ovarian cancer cell line, we identified 2,362 SMAD4 binding loci and 318 differentially expressed SMAD4 target genes. Comprehensive examination of SMAD4-bound loci, revealed four distinct binding patterns: 1) Basal; 2) Shift; 3) Stimulated Only; 4) Unstimulated Only. TGFβ stimulated SMAD4-bound loci were primarily classified as either Stimulated only (74%) or Shift (25%), indicating that TGFβ-stimulation alters SMAD4 binding patterns in epithelial ovarian cancer cells. Furthermore, based on gene regulatory network analysis, we determined that the TGFβ-induced, SMAD4-dependent regulatory network was strikingly different in ovarian cancer compared to normal cells. Importantly, the TGFβ/SMAD4 target genes identified in the A2780 epithelial ovarian cancer cell line were predictive of patient survival, based on in silico mining of publically available patient data bases. In conclusion, our data highlight the utility of next generation sequencing technology to identify genome-wide SMAD4 target genes in epithelial ovarian cancer and link aberrant TGFβ/SMAD signaling to ovarian tumorigenesis. Furthermore, the identified SMAD4 binding loci, combined with gene expression profiling and in silico data mining of patient cohorts, may provide a powerful approach to determine potential gene signatures with biological and future translational research in ovarian and other cancers.

Indexed as

Biomarkers, TumorCell Line, TumorChromatin ImmunoprecipitationChromosome MappingDisease-Free SurvivalFemaleGene Expression Regulation, NeoplasticGene Regulatory NetworksGenetic LociHumansOvarian NeoplasmsSequence AnalysisSignal TransductionSmad4 ProteinTranscriptomeTransforming Growth Factor betaBiomarkers, TumorSmad4 ProteinTransforming Growth Factor beta

Identifiers

PMID21799915
PMCPMC3143154
OpenAlexW2008814792

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.