Evidence map›Paper›PMID 33121472›Full record

ArticleBMC medical genomics2020

Elucidation of molecular links between obesity and cancer through microRNA regulation.

Haluk Dogan, Jiang Shu, Zeynep Hakguder, Zheng Xu, Juan Cui

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.4field-weighted citation impact, top 46% 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

6 citing papers in PubMed, 6 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

5 authors at 2 institutions in 1 country.

Haluk DoganSystems Biology and Biomedical Informatics Lab, Department of Computer Science and Engineering University of Nebraska-Lincoln, Lincoln, 68588-0115, NE, USA.
Jiang ShuSystems Biology and Biomedical Informatics Lab, Department of Computer Science and Engineering University of Nebraska-Lincoln, Lincoln, 68588-0115, NE, USA.
Zeynep HakguderSystems Biology and Biomedical Informatics Lab, Department of Computer Science and Engineering University of Nebraska-Lincoln, Lincoln, 68588-0115, NE, USA.
Zheng XuDepartment of Mathematics and Statistics, Wright State University, Dayton, 45435, OH, USA.
Juan CuiSystems Biology and Biomedical Informatics Lab, Department of Computer Science and Engineering University of Nebraska-Lincoln, Lincoln, 68588-0115, NE, USA. jcui@unl.edu.
University of Nebraska–Lincoln · USWright State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundObesity contributes to high cancer risk in humans and the mechanistic links between these two pathologies are not yet understood. Recent emerging evidence has associated obesity and cancer with metabolic abnormalities and inflammation where microRNA regulation has a strong implication.

methodsIn this study, we have developed an integrated framework to unravel obesity-cancer linkage from a microRNA regulation perspective. Different from traditional means of identifying static microRNA targets based on sequence and structure properties, our approach focused on the discovery of context-dependent microRNA-mRNA interactions that are potentially associated with disease progression via large-scale genomic analysis. Specifically, a meta-regression analysis and the integration of multi-omics information from obesity and cancers were presented to investigate the microRNA regulation in a dynamic and systematic manner.

resultsOur analysis has identified a total number of 2,143 unique microRNA-gene interactions in obesity and seven types of cancer. Common interactions in obesity and obesity-associated cancers are found to regulate genes in key metabolic processes such as fatty acid and arachidonic acid metabolism and various signaling pathways related to cell growth and inflammation. Additionally, modulated co-regulations among microRNAs targeting the same functional processes were reflected through the analysis.

conclusionWe demonstrated the statistical modeling of microRNA-mediated gene regulation can facilitate the association study between obesity and cancer. The entire framework provides a powerful tool to understand multifaceted gene regulation in complex human diseases that can be generalized in other biomedical applications.

Indexed as

Gene Expression Regulation, NeoplasticBiomarkers, TumorComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMicroRNAsNeoplasmsObesityRNA, MessengerBiomarkers, TumorMicroRNAsRNA, MessengerCancerMicroRNAMicroRNA regulationObesity

Identifiers

PMID33121472
PMCPMC7596939
OpenAlexW3097540111

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.