Evidence map›Paper›PMID 34602937›Full record

ArticleBreast care (Basel, Switzerland)2021

The Identification of Gene Expression Profiles Associated with Granulomatous Mastitis.

Qiang Zhu, Lin Wang, Pilin Wang

Open access · bronzeAbstract read
In one paragraph

Article in Breast care (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.7field-weighted citation impact, top 27% 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

8 citing papers in PubMed, 11 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

3 authors at 1 institution in 1 country.

Qiang ZhuDepartment of Breast Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Lin WangDepartment of Breast Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Pilin WangDepartment of Breast Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Capital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGranulomatous mastitis (GM) is a rare chronic inflammatory disease of the breast. The current therapeutic effects of the antibiotic therapy and surgical or immunomodulatory (steroid) treatment are normally poor due to the unclear etiology.

methodThis study aimed to identify the differentially expressed mRNAs in GM tissues using RNA sequencing and further explored the functions of differentially expressed mRNAs resulting in GM. Moreover, we revealed the relationship between GM and breast cancer by shared highly expressed genes in GM tissues and breast cancer tissues.

resultsA total of 12,115 mRNAs were analyzed in the whole expression profile, and 207 mRNAs (136 upregulated and 71 downregulated mRNAs) were differently expressed between the GM tissues and normal tissues. The enrichment analysis showed that the differentially expressed mRNAs were enriched in the biological processes and played a significant role in the immune system. Besides, the genes expressed significantly highly in breast cancer tissues are found to be enriched with GM genes, which may explain the similar clinical features between breast cancer and GM. We also found that the HSD11B1 gene which was differentially expressed in GM was used as drug target of prednisone, which is a common treatment for GM.

conclusionThis study is the first to use sequencing technology to elucidate the genetic mechanisms of GM. The finding of this study may have potential value in GM diagnosis and also provides potential drug targets for GM treatment.

Indexed as

EtiologyExpression profilesGranulomatous mastitisRNA sequencing

Identifiers

PMID34602937
PMCPMC8436716
OpenAlexW3096471453

What OpenQuestion holds

Textmetadata
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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.