Evidence map›Paper›PMID 31888617›Full record

ArticleBMC medical genomics2019

A network view of microRNA and gene interactions in different pathological stages of colon cancer.

Jia Wen, Benika Hall, Xinghua Shi

Abstract read
In one paragraph

Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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.

Jia WenDepartment of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, 28223, NC, USA.
Benika HallDepartment of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, 28223, NC, USA.
Xinghua ShiDepartment of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, 28223, NC, USA. x.shi@uncc.edu.

Funding

A Computational Framework for Scalable Epistasis Analysis on High-Dimensional Genomic Data.R15HG009565 · NHGRI · UNIVERSITY OF NORTH CAROLINA CHARLOTTE · PI SELL, SUSAN M · 2017 to 2017
$438k
NHGRI NIH HHS R15 HG009565
6 · The paper itself

Abstract

backgroundColon cancer is one of the common cancers in human. Although the number of annual cases has decreased drastically, prognostic screening and translational methods can be improved. Hence, it is critical to understand the molecular mechanisms of disease progression and prognosis.

resultsIn this study, we develop a new strategy for integrating microRNA and gene expression profiles together with clinical information toward understanding the regulation of colon cancer. Particularly, we use this approach to identify microRNA and gene expression networks that are specific to certain pathological stages. To demonstrate the application of our method, we apply this approach to identify microRNA and gene interactions that are specific to pathological stages of colon cancer in The Cancer Genome Atlas (TCGA) datasets.

conclusionsOur results show that there are significant differences in network connections between miRNAs and genes in different pathological stages of colon cancer. These findings point to a hypothesis that these networks signify different roles of microRNA and gene regulation in the pathogenesis and tumorigenesis of colon cancer.

Indexed as

Epistasis, GeneticGene Regulatory NetworksColonic NeoplasmsGene Expression Regulation, NeoplasticGene OntologyHumansMicroRNAsNeoplasm StagingMicroRNAs

Identifiers

PMID31888617
PMCPMC6936140

What OpenQuestion holds

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LicenceCC BY
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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.