Evidence map›Paper›PMID 33291647›Full record

ArticleCancers2020

Single-Cell RNA Sequencing of a Postmenopausal Normal Breast Tissue Identifies Multiple Cell Types That Contribute to Breast Cancer.

Sen Peng, Lora L Hebert, Jennifer M Eschbacher, Suwon Kim

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

23 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Review
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  5. [Application progress of single-cell RNA sequencing technology in breast development and related diseases].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025
    Review
  6. Article
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  13. RUNX2 and Cancer.International journal of molecular sciences · 2023
    Review
  14. Review
  15. Article
  16. Article
  17. Ductal keratin 15NPJ breast cancer · 2022
    Article
  18. Review
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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

4 authors at 3 institutions in 1 country.

Sen PengCancer and Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.
Lora L HebertDepartment of Surgery, St. Joseph's Hospital, Dignity Health, Phoenix, AZ 85013, USA.
Jennifer M EschbacherDepartment of Surgery, St. Joseph's Hospital, Dignity Health, Phoenix, AZ 85013, USA.
Suwon KimCancer and Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.ORCID 0000-0003-0633-3183
Translational Genomics Research Institute · USBarrow Neurological Institute · USUniversity of Arizona · US

Funding

Baylor Scott & White Research Institute (BSWRI) and Translational Genomics Research Institute (TGen) Oncology Research Collaboration Initiative grant CP-15
6 · The paper itself

Abstract

The human breast is composed of diverse cell types. Studies have delineated mammary epithelial cells, but the other cell types in the breast have scarcely been characterized. In order to gain insight into the cellular composition of the tissue, we performed droplet-mediated RNA sequencing of 3193 single cells isolated from a postmenopausal breast tissue without enriching for epithelial cells. Unbiased clustering analysis identified 10 distinct cell clusters, seven of which were nonepithelial devoid of cytokeratin expression. The remaining three cell clusters expressed cytokeratins (CKs), representing breast epithelial cells; Cluster 2 and Cluster 7 cells expressed luminal and basal CKs, respectively, whereas Cluster 9 cells expressed both luminal and basal CKs, as well as other CKs of unknown specificity. To assess which cell type(s) potentially contributes to breast cancer, we used the differential gene expression signature of each cell cluster to derive gene set variation analysis (GSVA) scores and classified breast tumors in The Cancer Gene Atlas (TGGA) dataset (

Indexed as

cluster analysiscytokeratin expressionGSVAmammary epithelial cellsmammary fibroblastsnormal breastsingle-cell RNA sequencingTCGA breast cancer dataset: breast cancertriple-negative breast cancer

Identifiers

PMID33291647
PMCPMC7761899
OpenAlexW3108845279

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.