Evidence map›Paper›PMID 29423105›Full record

ArticleOncotarget2018

Transcriptome profiling identified differentially expressed genes and pathways associated with tamoxifen resistance in human breast cancer.

Xin Men, Jun Ma, Tong Wu, Junyi Pu, Shaojia Wen, Jianfeng Shen, Xun Wang, Yamin Wang, Chao Chen, Penggao Dai

Abstract read
In one paragraph

Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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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

10 authors.

Xin MenNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.
Jun MaNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.
Tong WuNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.
Junyi PuNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.
Shaojia WenNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.
Jianfeng ShenNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.
Xun WangNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.
Yamin WangNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.
Chao ChenNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.
Penggao DaiNational Engineering Research Center for Miniaturized Detection Systems, College of Life Science, Northwest University, Xi'an, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tamoxifen (TAM) resistance is an important clinical problem in the treatment of breast cancer. In order to identify the mechanism of TAM resistance for estrogen receptor (ER)-positive breast cancer, we screened the transcriptome using RNA-seq and compared the gene expression profiles between the MCF-7 mamma carcinoma cell line and the TAM-resistant cell line TAMR/MCF-7, 52 significant differential expression genes (DEGs) were identified including

Indexed as

breast cancerresistanceRNA-seqtamoxifentranscriptome

Identifiers

PMID29423105
PMCPMC5790522

What OpenQuestion holds

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Registered trials

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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.