Evidence map›Paper›PMID 20964848›Full record

ArticleBMC cancer2010

Comparative survival analysis of breast cancer microarray studies identifies important prognostic genetic pathways.

Jeffrey C Miecznikowski, Dan Wang, Song Liu, Lara Sucheston, David Gold

Abstract readComparative Study
In one paragraph

Article in BMC cancer, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

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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.

Jeffrey C MiecznikowskiDepartment of Biostatistics, University at Buffalo (SUNY), Buffalo, New York 14214, USA. jcm38@buffalo.edu
Dan Wang
Song Liu
Lara Sucheston
David Gold

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn estimated 12% of females in the United States will develop breast cancer in their lifetime. Although, there are advances in treatment options including surgery and chemotherapy, breast cancer is still the second most lethal cancer in women. Thus, there is a clear need for better methods to predict prognosis for each breast cancer patient. With the advent of large genetic databases and the reduction in cost for the experiments, researchers are faced with choosing from a large pool of potential prognostic markers from numerous breast cancer gene expression profile studies.

methodsFive microarray datasets related to breast cancer were examined using gene set analysis and the cancers were categorized into different subtypes using a scoring system based on genetic pathway activity.

resultsWe have observed that significant genes in the individual studies show little reproducibility across the datasets. From our comparative analysis, using gene pathways with clinical variables is more reliable across studies and shows promise in assessing a patient's prognosis.

conclusionsThis study concludes that, in light of clinical variables, there are significant gene pathways in common across the datasets. Specifically, several pathways can further significantly stratify patients for survival. These candidate pathways should help to develop a panel of significant biomarkers for the prognosis of breast cancer patients in a clinical setting.

Indexed as

Gene Expression Regulation, NeoplasticBiomarkers, TumorBreast NeoplasmsCell CycleDisease-Free SurvivalFemaleGene Expression ProfilingHumansModels, StatisticalOligonucleotide Array Sequence AnalysisPrognosisProportional Hazards ModelsRegression AnalysisSurvival AnalysisTreatment OutcomeBiomarkers, Tumor

Identifiers

PMID20964848
PMCPMC2972286

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Registered trials

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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.