Evidence map›Paper›PMID 30965671›Full record

ArticleCancers2019

DNA Repair Gene Expression Adjusted by the

Leif E Peterson, Tatiana Kovyrshina

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2019. 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
0.4field-weighted citation impact, top 38% of its field
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, 8 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Leif E PetersonDepartment of Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York City, NY 10065, USA. peterson.leif.e@gmail.com.
Tatiana KovyrshinaCenter for Biostatistics, Institute for Academic Medicine, Houston Methodist Research Institute, 6565 Fannin Street, Houston, TX 77030, USA. tkovyrshina@houstonmethodist.org.
Houston Methodist · US

Funding

National Aeronautics and Space Administration NNX-12AO52A
6 · The paper itself

Abstract

Removal of the proliferation component of gene expression by proliferating cell nuclearantigen (PCNA) adjustment via statistical methods has been addressed in numerous survivalprediction studies for breast cancer and all cancers in the Cancer Genome Atlas (TCGA). Thesestudies indicate that the removal of proliferation in gene expression by PCNA adjustment removesthe statistical significance for predicting overall survival (OS) when gene selection is performed ona genome-wide basis. Since cancers become addicted to DNA repair as a result of forced cellularreplication, increased oxidation, and repair deficiencies from oncogenic loss or geneticpolymorphisms, we hypothesized that PCNA adjustment of DNA repair gene expression does notremove statistical significance for OS prediction. The rationale and importance of this translationalhypothesis is that new lists of repair genes which are predictive of OS can be identified to establishnew targets for inhibition therapy. A candidate gene approach was employed using TCGARNA-Seq data for 121 DNA repair genes in 8 molecular pathways to predict OS for 18 cancers.Statistical randomization test results indicate that after PCNA adjustment, OS could be predictedsignificantly by sets of DNA repair genes for 61% (11/18) of the cancers. These findings suggest thatremoval of the proliferation signal in expression by PCNA adjustment does not remove statisticalsignificance for predicting OS. In conclusion, it is likely that previous studies on PCNA adjustmentand survival were biased because genes identified through a genome-wide approach are stronglyco-regulated by proliferation.

Indexed as

DNA repaironcologyPCNA metageneRNA-seqsurvival

Identifiers

PMID30965671
PMCPMC6520950
OpenAlexW2953292339

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.