Evidence map›Paper›PMID 17962100›Full record

ReviewTaiwanese journal of obstetrics & gynecology2007

Microarray analysis of gene expression of cancer to guide the use of chemotherapeutics.

Tzu-Hao Wang, Angel Chao

Abstract readReview
In one paragraph

Review in Taiwanese journal of obstetrics & gynecology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

2 authors.

Tzu-Hao WangDepartment of Obstetrics and Gynecology, Lin-Kou Medical Center, Chang Gung Memorial Hospital, Chang Gung University, Tao-Yuan, Taiwan. knoxtn@cgmh.org.tw
Angel Chao

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The beauty of microarray analysis of gene expression (MAGE) is that it can be used to discover some genes that were previously thought to be unrelated to a physiologic or pathologic event. During the period from 1999 to 2007, applications of MAGE in cancer investigation have shifted from molecular profiling, identifying previously undiscovered cancer types, predicting outcomes of cancer patients, revealing metastasis signatures of solid tumors, to guiding the use of therapeutics. The roles of cancer genomic signatures have evolved through three phases. In the first phase, genomic signatures were described in stored cancer specimens and dubbed as molecular portraits of cancer. When gene expression profiles were carefully correlated with sufficient clinical information of cancer patients, new subgroups of cancers with distinct outcomes were revealed. In studies of the second phase, validation of cancer signatures was emphasized and commonly performed with independent groups of cancer specimens or independent data set. In the third phase, cancer genomic signatures have been further expanded beyond depicting the molecular portrait of cancer to predicting patient outcomes and guiding the use of cancer therapeutics. Cancer genomic signatures have become an essential part of a new generation of cancer clinical trials. It is advocated that, in future clinical trials of cancer therapy, the cancer specimens of each participant should be tested for currently available predictor genomic signatures, so that the most effective treatment with the least adverse effects for each patient can be identified. Then, participants can be triaged to an appropriate study group.

Indexed as

HumansNeoplasmsOligonucleotide Array Sequence AnalysisTreatment Outcome

Identifiers

PMID17962100
PMCPMC7129596

What OpenQuestion holds

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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.