Evidence map›Paper›PMID 25887648›Full record

ArticleBMC bioinformatics2015

Identification of active miRNA and transcription factor regulatory pathways in human obesity-related inflammation.

Xi-Mei Zhang, Lin Guo, Mei-Hua Chi, Hong-Mei Sun, Xiao-Wen Chen

Abstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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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

12 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

5 authors.

Xi-Mei ZhangDepartment of Histology and Embryology, Harbin Medical University, Harbin, 150081, PR China. ximei1119@126.com.
Lin GuoDepartment of Endocrinology and Metabolism, the Second Affiliated Hospital of Harbin Medical University, Harbin, 150081, PR China. guolin821@gmail.com.
Mei-Hua ChiTeaching Experiment Center of Morphology, Harbin Medical University, Harbin, 150081, PR China. 57557413@qq.com.
Hong-Mei SunCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China. sunhongmei@hrbmu.edu.cn.
Xiao-Wen ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China. xiaowenchen8266@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundObesity-induced chronic inflammation plays a fundamental role in the pathogenesis of metabolic syndrome (MS). Recently, a growing body of evidence supports that miRNAs are largely dysregulated in obesity and that specific miRNAs regulate obesity-associated inflammation. We applied an approach aiming to identify active miRNA-TF-gene regulatory pathways in obesity. Firstly, we detected differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) from mRNA and miRNA expression profiles, respectively. Secondly, by mapping the DEGs and DEmiRs to the curated miRNA-TF-gene regulatory network as active seed nodes and connect them with their immediate neighbors, we obtained the potential active miRNA-TF-gene regulatory subnetwork in obesity. Thirdly, using a Breadth-First-Search (BFS) algorithm, we identified potential active miRNA-TF-gene regulatory pathways in obesity. Finally, through the hypergeometric test, we identified the active miRNA-TF-gene regulatory pathways that were significantly related to obesity.

resultsThe potential active pathways with FDR < 0.0005 were considered to be the active miRNA-TF regulatory pathways in obesity. The union of the active pathways is visualized and identical nodes of the active pathways were merged.

conclusionsWe identified 23 active miRNA-TF-gene regulatory pathways that were significantly related to obesity-related inflammation.

Indexed as

AlgorithmsGene Regulatory NetworksDatabases, FactualGene Expression ProfilingHumansInflammationMicroRNAsObesityRNA, MessengerTranscription FactorsMicroRNAsRNA, MessengerTranscription Factors

Identifiers

PMID25887648
PMCPMC4355475

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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.