Evidence map›Paper›PMID 42794706›Full record

ArticleInternational journal of molecular sciences2026

Population-Associated Molecular Variation in Histologically Normal Breast Tissue Is Associated with Distinct Baseline Transcriptional States.

William Drew Hulsy, Karen Salazar, Dimitra Chalkia, Yonny Chavez, Yuchen Zhao, Georgia Halkia, Olga V Razorenova, Nikolas Nikolaidis

Abstract readComparative Study
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

William Drew HulsyDepartment of Biological Science, Center for Applied Biotechnology Studies, and Titan Supercomputing Center, California State University Fullerton, Fullerton, CA 92831, USA.
Karen SalazarDepartment of Biological Science, Center for Applied Biotechnology Studies, and Titan Supercomputing Center, California State University Fullerton, Fullerton, CA 92831, USA.
Dimitra ChalkiaCenter for Mitochondrial and Epigenomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.ORCID 0000-0003-4780-544X
Yonny ChavezDepartment of Biological Science, Center for Applied Biotechnology Studies, and Titan Supercomputing Center, California State University Fullerton, Fullerton, CA 92831, USA.
Yuchen ZhaoDepartment of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA.
Georgia HalkiaDepartment of Public Health, College of Health and Public Service, Utah Valley University, Orem, UT 84058, USA.
Olga V RazorenovaDepartment of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA.
Nikolas NikolaidisDepartment of Biological Science, Center for Applied Biotechnology Studies, and Titan Supercomputing Center, California State University Fullerton, Fullerton, CA 92831, USA.ORCID 0000-0002-5633-1883

Funding

U-RISE at Cal State FullertonT34GM149493 · NIGMS · CALIFORNIA STATE UNIVERSITY FULLERTON · PI MATH P CUAJUNGCO · 2023 to 2026
$1.3M
Project EAGER: Enabling Achievement in Genomics Education and ResearchR25HG013571 · NHGRI · CALIFORNIA STATE UNIVERSITY FULLERTON · PI Doris Bachtrog, Sam Behseta · 2024 to 2026
$968k
Racial disparity in triple-negative breast cancer lipid metabolismP20CA253251 · NCI · CALIFORNIA STATE UNIVERSITY FULLERTON · PI TOLMASKY, MARCELO E · 2021 to 2024
$895k
Interaction between HspA1A, a seventy-kDa heat shock protein, and lipids in stressed cellsSC3GM121226 · NIGMS · CALIFORNIA STATE UNIVERSITY FULLERTON · PI NIKOLAIDIS, NIKOLAS · 2017 to 2024
$843k
NCI NIH HHS P20 CA253251NHGRI NIH HHS R25 HG013571NIGMS NIH HHS SC3 GM121226NIGMS NIH HHS T34 GM149493NIGMS NIH HHS T34GM149493
6 · The paper itself

Abstract

Population-associated molecular variation in breast tissue may contribute to differences in tissue biology and disease susceptibility. Still, the extent to which such variation is shaped by underlying tissue state remains unclear. We performed a pilot RNA-seq and lipidomic analysis of histologically normal breast tissue from African American (AA) and Caucasian White (CW) individuals. Unsupervised transcriptomic analysis identified two baseline tissue states, G1 and G2, representing the dominant axis of molecular variation and associated with epithelial-enriched and vascular-enriched tissue contexts, respectively. Across the full cohort, AA and CW samples showed minimal transcriptomic differences. However, within G1, 191 genes were differentially expressed between AA and CW samples, with coordinated enrichment of extracellular matrix organization and proliferative/cytoskeletal processes in AA samples; these patterns were consistent across enrichment methods and sensitivity analyses. No comparable population-associated transcriptional signal was detected in G2. Lipidomic profiles showed limited separation and no robust population-associated differences after correction for multiple testing. Together, these pilot findings suggest that population-associated molecular variation in histologically normal breast tissue may be state-dependent, becoming detectable within a specific baseline transcriptional context rather than uniformly across the cohort. These results further underscore baseline tissue state as a major source of variation in small, heterogeneous bulk-tissue cohorts and provide a framework for future cell-resolved studies of tissue variation and disease susceptibility.

Indexed as

BreastTranscription, GeneticTranscriptomeBlack or African AmericanFemaleGene Expression ProfilingHumansLipidomicsWhitebaseline transcriptional statesbreast tissuecontext-dependent variationextracellular matrixlipidomicstissue heterogeneitytissue microenvironmenttranscriptomics

Identifiers

PMID42794706
PMCPMC13607183

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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.