Evidence map›Paper›PMID 29904148›Full record

Trial reportScientific reports2018

Intra-individual Gene Expression Variability of Histologically Normal Breast Tissue.

Xuezheng Sun, Yue Shan, Quefeng Li, Lynn Chollet-Hinton, Erin L Kirk, Gretchen L Gierach, Melissa A Troester

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

7 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

7 authors at 3 institutions in 1 country.

Xuezheng SunDepartment of Epidemiology, Gillings School of Public Health, University of North Carolina, Chapel Hill, USA. amysun@email.unc.edu.
Yue ShanDepartment of Biostatistics, Gillings School of Public Health, University of North Carolina, Chapel Hill, USA.
Quefeng LiDepartment of Biostatistics, Gillings School of Public Health, University of North Carolina, Chapel Hill, USA.
Lynn Chollet-HintonLineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, USA.
Erin L KirkDepartment of Epidemiology, Gillings School of Public Health, University of North Carolina, Chapel Hill, USA.
Gretchen L GierachMetabolic Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockvill, USA.
Melissa A TroesterDepartment of Epidemiology, Gillings School of Public Health, University of North Carolina, Chapel Hill, USA.
University of North Carolina at Chapel Hill · USDivision of Cancer Epidemiology and GeneticsUNC Lineberger Comprehensive Cancer Center

Funding

Hormone-Related CancersZIACP010126 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI GIERACH, GRETCHEN · 2009 to 2025
$103.5M
Re-Entry Supplement: Investigation of Oral Microbial Enzymes for the Detection and Treatment of Periodontal DiseaseUL1TR002489 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, SHAHEEN, NICHOLAS J · 2018 to 2022
$48.6M
Cancer Control Education ProgramT32CA057726 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Melissa B Gilkey, Melissa A. Troester · 2017 to 2026
$3.7M
Semiparametric Regression Analysis of Censored DataR01GM047845 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, DANYU · 1997 to 2018
$2.9M
Pregnancy, Obesogenic Environments, and Basal-like Breast CancerU01ES019472 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI TROESTER, MELISSA A. · 2010 to 2014
$2.3M
Biology of Race and Progression Associated Breast Tumor Gene ExpressionU01CA179715 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI PEROU, CHARLES M, TROESTER, MELISSA A. · 2014 to 2018
$1.6M
NCATS NIH HHS UL1 TR002489NCI NIH HHS T32 CA057726NCI NIH HHS U01 CA179715NIEHS NIH HHS U01 ES019472NIGMS NIH HHS R01 GM047845
6 · The paper itself

Abstract

Several studies have sought to identify novel transcriptional biomarkers in normal breast or breast microenvironment to predict tumor risk and prognosis. However, systematic efforts to evaluate intra-individual variability of gene expression within normal breast have not been reported. This study analyzed the microarray gene expression data of 288 samples from 170 women in the Normal Breast Study (NBS), wherein multiple histologically normal breast samples were collected from different block regions and different sections at a given region. Intra-individual differences in global gene expression and selected gene expression signatures were quantified and evaluated in association with other patient-level factors. We found that intra-individual reliability was relatively high in global gene expression, but differed by signatures, with composition-related signatures (i.e., stroma) having higher intra-individual variability and tumorigenesis-related signatures (i.e., proliferation) having lower intra-individual variability. Histological stroma composition was the only factor significantly associated with heterogeneous breast tissue (defined as > median intra-individual variation; high nuclear density, odds ratio [OR] = 3.42, 95% confidence interval [CI] = 1.15-10.15; low area, OR = 0.29, 95% CI = 0.10-0.86). Other factors suggestively influencing the variability included age, BMI, and adipose nuclear density. Our results underscore the importance of considering intra-individual variability in tissue-based biomarker development, and have important implications for normal breast research.

Indexed as

Biological Variation, IndividualGene Expression ProfilingAdultBreastFemaleGene Expression RegulationHumans

Identifiers

PMID29904148
PMCPMC6002361
OpenAlexW2807067385

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.