Evidence map›Paper›PMID 33274225›Full record

ArticleBioMed research international2020

Investigation of the miRNA and mRNA Coexpression Network and Their Prognostic Value in Hepatocellular Carcinoma.

Hao Zhang, Xi Chen, Yufeng Yuan

Abstract read
In one paragraph

Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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  7. The Effects of Naringenin on miRNA-mRNA Profiles in HepaRG Cells.International journal of molecular sciences · 2021
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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.

Hao ZhangDepartment of Hepatobiliary and Pancreatic Surgery, Zhongnan Hospital of Wuhan University, Donghu Road 169# Wuhan 430071, China.ORCID https://orcid.org/0000-0001-5330-8218
Xi ChenDepartment of Hepatobiliary and Pancreatic Surgery, Zhongnan Hospital of Wuhan University, Donghu Road 169# Wuhan 430071, China.
Yufeng YuanDepartment of Hepatobiliary and Pancreatic Surgery, Zhongnan Hospital of Wuhan University, Donghu Road 169# Wuhan 430071, China.ORCID https://orcid.org/0000-0003-3924-3803

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo identify pivotal differentially expressed miRNAs and genes and construct their regulatory network in hepatocellular carcinoma.

methodsmRNA (GSE101728) and microRNA (GSE108724) microarray datasets were obtained from the NCBI Gene Expression Omnibus (GEO) database. Then, we identified the differentially expressed miRNAs and mRNAs. Sequentially, transcription factor enrichment and gene ontology (GO) enrichment analysis for miRNA were performed. Target genes of these differential miRNAs were obtained using packages in

resultsA total of 35 miRNAs and 295 mRNAs were involved in the network. These differential genes were enriched in positive regulation of cell-cell adhesion, positive regulation of leukocyte cell-cell adhesion, and so on. Eight differentially expressed miRNAs were found to be associated with the OS of patients with HCC. Among which, miR-425 and miR-324 were upregulated while the other six, including miR-99a, miR-100, miR-125b, miR-145, miR-150, and miR-338, were downregulated.

conclusionIn conclusion, these results can provide a potential research direction for further studies about the mechanisms of how miRNA affects malignant behavior in hepatocellular carcinoma.

Indexed as

Gene Expression Regulation, NeoplasticGene Regulatory NetworksCarcinoma, HepatocellularGene Expression ProfilingGene OntologyGenes, Tumor SuppressorHumansLiver NeoplasmsMicroRNAsOncogenesPrognosisRNA, MessengerSurvival AnalysisMicroRNAsRNA, Messenger

Identifiers

PMID33274225
PMCPMC7676931

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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.