Evidence map›Paper›PMID 26878388›Full record

ArticleOncogenesis2016

Identification of differentially expressed miRNAs in individual breast cancer patient and application in personalized medicine.

F Peng, Y Zhang, R Wang, W Zhou, Z Zhao, H Liang, L Qi, W Zhao, H Wang, C Wang and 2 more

Open access · goldAbstract read
In one paragraph

Article in Oncogenesis, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.1field-weighted citation impact, top 9% 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

21 citing papers in PubMed, 41 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

12 authors at 1 institution in 1 country.

F PengDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Y ZhangDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
R WangDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
W ZhouDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Z ZhaoDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
H LiangDepartment of Pharmacology, Harbin Medical University, Harbin, China.
L QiDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
W ZhaoDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
H WangDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
C WangDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Z GuoDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Y GuDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) have key roles in breast cancer progression, and their expression levels are heterogeneous across individual breast cancer patients. Traditional methods aim to identify differentially expressed miRNAs in populations rather than in individuals and are affected by the expression intensities of miRNAs in different experimental batches or platforms. Thus it is urgent to conduct miRNA differential expression analysis at an individual level for further personalized medicine research. We proposed a straightforward method to determine the differential expression of each miRNA in an individual patient by utilizing the reversal expression order of miRNA pairs between two conditions (cancer and normal tissue). We applied our method to breast cancer miRNA expression profiles from The Cancer Genome Atlas and two other independent data sets. In total, 292 miRNAs were differentially expressed in individual breast cancer patients. Using the differential expression profile of miRNAs in individual patients, we found that the deregulations of miRNA tend to occur in specific breast cancer subtypes. We investigated the coordination effect between the miRNA and its target, based on the hypothesis that one gene function can be changed by copy number alterations of the corresponding gene or deregulation of the miRNA. We revealed that patients exhibiting an upregulation of hsa-miR-92b and patients with deletions of PTEN did not tend to overlap, and hsa-miR-92b and PTEN coordinately regulated the pathway of 'cell cycle' and so on. Moreover, we discovered a new prognostic signature, hsa-miR-29c, whose downregulation was associated with poor survival of breast cancer patients.

Identifiers

PMID26878388
PMCPMC5154351
OpenAlexW2274415350

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.