Evidence map›Paper›PMID 19115029›Full record

ReviewWorld journal of surgery2009

Basic principles and technologies for deciphering the genetic map of cancer.

Georgios Voidonikolas, Stephanie S Kreml, Changyi Chen, William E Fisher, F Charles Brunicardi, Richard A Gibbs, Marie-Claude Gingras

Abstract readReview
In one paragraph

Review in World journal of surgery, 2009. 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. Review
  2. Article
  3. Article
  4. Genetic variation and its role in malignancy.International journal of biomedical science : IJBS · 2011
    Article
  5. Defining the cancer master switch.World journal of surgery · 2011
    Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Single nucleotide polymorphism in RECQL and survival in resectable pancreatic adenocarcinoma.HPB : the official journal of the International Hepato Pancreato Biliary Association · 2009
    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

7 authors.

Georgios VoidonikolasMichael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA.
Stephanie S Kreml
Changyi Chen
William E Fisher
F Charles Brunicardi
Richard A Gibbs
Marie-Claude Gingras

Funding

Molecular Surgeon Symposium on Genetics and Genomics of Pancreatic CancerR13CA132572 · NCI · BAYLOR COLLEGE OF MEDICINE · PI CHEN, CHANGYI · 2008 to 2008
$5k
NCI NIH HHS R13 CA132572
6 · The paper itself

Abstract

The progress achieved in the field of genomics in recent years is leading medicine to adopt a personalized model in which the knowledge of individual DNA alterations will allow a targeted approach to cancer. Using pancreatic cancer as a model, we discuss herein the fundamentals that need to be considered for the high throughput and global identification of mutations. These include patient-related issues, sample collection, DNA isolation, gene selection, primer design, and sequencing techniques. We also describe the possible applications of the discovery of DNA changes to the approach of this disease and cite preliminary efforts where the knowledge has been translated into the clinical or preclinical setting.

Indexed as

Genetic TechniquesAdenocarcinomaDNA FragmentationGenes, NeoplasmHumansNeoplasmsNucleic Acid Amplification TechniquesPancreatic NeoplasmsPolymorphism, Single NucleotideProto-Oncogene ProteinsProto-Oncogene Proteins p21(ras)ras ProteinsSequence Analysis, DNAKRAS protein, humanProto-Oncogene ProteinsProto-Oncogene Proteins p21(ras)ras Proteins

Identifiers

PMID19115029
PMCPMC2924149

What OpenQuestion holds

Textmetadata
LicenceTDM
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.