Evidence map›Paper›PMID 18359977›Full record

ArticleCancer genomics & proteomics

Global comparative gene expression analysis of melanoma patient samples, derived cell lines and corresponding tumor xenografts.

Yaguang Xi, Adam Riker, Lalita Shevde-Samant, Rajeev Samant, Christopher Morris, Elaine Gavin, Oystein Fodstad, Jingfang Ju

Abstract readComparative Study
In one paragraph

Article in Cancer genomics & proteomics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

10 citing papers in PubMed.

  1. Personalized genomic disease risk of volunteers.Proceedings of the National Academy of Sciences of the United States of America · 2013
    Trial
  2. Article
  3. Article
  4. Membrane Transporters and Channels in Melanoma.Reviews of physiology, biochemistry and pharmacology · 2021
    Review
  5. Article
  6. Article
  7. Melanoma in patients with GATA2 deficiency.Pigment cell & melanoma research · 2018
    Article
  8. Article
  9. Article
  10. Article
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.

Yaguang XiMitchell Cancer Institute, Mobile, AL 36688, USA.
Adam Riker
Lalita Shevde-Samant
Rajeev Samant
Christopher Morris
Elaine Gavin
Oystein Fodstad
Jingfang Ju

Funding

Modulation of Serotonergic TransportersR01MH075020 · NIMH · GEORGETOWN UNIVERSITY · PI SIDHU, ANITA · 2007 to 2010
$1.3M
Novel Method for Isolating Actively Translated mRNAsR21CA114043 · NCI · UNIVERSITY OF SOUTH ALABAMA · PI JU, JINGFANG · 2006 to 2007
$259k
NCI NIH HHS R21 CA114043NCI NIH HHS R21CA114043NIMH NIH HHS R01 MH075020NIMH NIH HHS R01MH075020
6 · The paper itself

Abstract

Various in vitro and in vivo experimental models have been used for the discovery of genes and pathways involved in melanoma and other types of cancer. However, in many cases, the results from various tumor models failed to be validated successfully in clinical studies. Limited information is available on how closely these models reflect the in vivo physiological conditions. In this study, a comprehensive genomics approach was used to systematically compare the expression patterns of snap frozen samples obtained from patients with primary melanoma, lymph node metastasis, and distant metastases, and compare these patterns to those of their corresponding cell lines and tumor xenografts in nude mice. The GE Healthcare 20k human genome array was used and the expression data was normalized and analyzed using GeneSpring 7.2 software. Based on the expression analysis, the correlation rate between the snap frozen primary patient samples vs. derived cell lines was 66%, with 1687 differentially expressed genes. The correlation rate between the snap frozen primary patient samples and the tumor xenografts was 75%, with 1,374 differentially expressed genes, and the correlation rate comparing tumor xenografts to derived cell lines ranged between 58% and 84%. These results demonstrated significant gene expression differences between tumor materials with different in vitro and in vivo growth microenvironments. Such studies can help us to distinguish between genes up- or down-regulated as a result of the microenvironment and those stably expressed independently of the tumor milieu. With the extensive use of cell lines and xenografts in cancer research, the information obtained using our approach may help to better interpret results generated from different tumor models by understanding common differences, as well as similarities at the gene expression level, information that may have important practical and biological implications.

Indexed as

Gene Expression Regulation, NeoplasticAnimalsCell Line, TumorCluster AnalysisGene Expression ProfilingGenome, HumanHumansMelanomaMiceMice, NudeNeoplasm MetastasisOligonucleotide Array Sequence AnalysisTransplantation, Heterologous

Identifiers

PMID18359977
PMCPMC3226839

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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.