Evidence map›Paper›PMID 19331659›Full record

ArticleBMC genomics2009

A signature-based method for indexing cell cycle phase distribution from microarray profiles.

Hideaki Mizuno, Yoshito Nakanishi, Nobuya Ishii, Akinori Sarai, Kunio Kitada

Abstract read
In one paragraph

Article in BMC genomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

20 citing papers in PubMed.

  1. InterferonFrontiers in immunology · 2020
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  17. UtilizingTranslational cancer research · 2013
    Article
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  20. RapidmAbs
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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

5 authors.

Hideaki MizunoKamakura Research Laboratories, Chugai Pharmaceutical Co Ltd, Kamakura, Kanagawa, Japan. mizunohda@chugai-pharm.co.jp
Yoshito Nakanishi
Nobuya Ishii
Akinori Sarai
Kunio Kitada

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe cell cycle machinery interprets oncogenic signals and reflects the biology of cancers. To date, various methods for cell cycle phase estimation such as mitotic index, S phase fraction, and immunohistochemistry have provided valuable information on cancers (e.g. proliferation rate). However, those methods rely on one or few measurements and the scope of the information is limited. There is a need for more systematic cell cycle analysis methods.

resultsWe developed a signature-based method for indexing cell cycle phase distribution from microarray profiles under consideration of cycling and non-cycling cells. A cell cycle signature masterset, composed of genes which express preferentially in cycling cells and in a cell cycle-regulated manner, was created to index the proportion of cycling cells in the sample. Cell cycle signature subsets, composed of genes whose expressions peak at specific stages of the cell cycle, were also created to index the proportion of cells in the corresponding stages. The method was validated using cell cycle datasets and quiescence-induced cell datasets. Analyses of a mouse tumor model dataset and human breast cancer datasets revealed variations in the proportion of cycling cells. When the influence of non-cycling cells was taken into account, "buried" cell cycle phase distributions were depicted that were oncogenic-event specific in the mouse tumor model dataset and were associated with patients' prognosis in the human breast cancer datasets.

conclusionThe signature-based cell cycle analysis method presented in this report, would potentially be of value for cancer characterization and diagnostics.

Indexed as

AlgorithmsAnimalsBreast NeoplasmsCell CycleCell Cycle ProteinsCell Line, TumorDatabases, GeneticGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiceModels, GeneticNeoplasmsOligonucleotide Array Sequence AnalysisPrognosisCell Cycle Proteins

Identifiers

PMID19331659
PMCPMC2676301

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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.