Evidence map›Paper›PMID 29254483›Full record

ArticleBMC cancer2017

Gene expression, regulation of DEN and HBx induced HCC mice models and comparisons of tumor, para-tumor and normal tissues.

Qin Tang, Qi Wang, Qiong Zhang, Sheng-Yan Lin, Yanhong Zhu, Xiangliang Yang, An-Yuan Guo

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 24% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
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  5. EGR1 suppresses HCC growth and aerobic glycolysis by transcriptionally downregulating PFKL.Journal of experimental & clinical cancer research : CR · 2024
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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

7 authors at 1 institution in 1 country.

Qin TangHubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, 430074, China.
Qi WangNational Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Qiong ZhangHubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, 430074, China.
Sheng-Yan LinHubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, 430074, China.
Yanhong ZhuNational Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Xiangliang YangNational Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
An-Yuan GuoHubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, 430074, China. guoay@hust.edu.cn.ORCID http://orcid.org/0000-0002-5099-7465
Huazhong University of Science and Technology · CN

Funding

National Basic Research Program of China 2012CB932501National Natural Science Foundation of China No. 31471247
6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is the leading cause of cancer mortality. Chemical and virus induction are two major risk factors, however, the potential molecular mechanisms of their differences remain elusive. In this study, to identify the similarities and differences between chemical and virus induced HCC models, we compared the gene expression profiles between DEN and HBx mice models, as well as the differences among tumor, para-tumor and normal tissues.

methodsWe sequenced both gene and microRNA (miRNA) expression for HCC tumor tissues, para-tumor and normal liver tissues from DEN model mice (30-week-old) and downloaded the corresponding microarray expression data of HBx model from GEO database. Then differentially expressed genes (DEGs), miRNAs and transcription factors (TFs) were detected by R packages and performed functional enrichment analysis. To explore the gene regulatory network in HCC models, miRNA and TF regulatory networks were constructed by target prediction.

resultsFor model comparison, although DEGs between tumor and normal tissues in DEN and HBx models only had a small part of overlapping, they shared common pathways including lipid metabolism, oxidation-reduction process and immune process. For tissue comparisons in each model, genes in oxidation-reduction process were down-regulated in tumor tissues and genes in inflammatory response showed the highest expression level in para-tumor tissues. Genes highly expressed in both tumor and para-tumor tissues in two models mainly participated in immune and inflammatory response. Genes expressed in HBx model were also involved in cell proliferation and cell migration etc. Network analysis revealed that several miRNAs such as miR-381-3p, miR-142a-3p, miR-214-3p and TFs such as Egr1, Atf3 and Klf4 were the core regulators in HCC.

conclusionsThrough the comparative analyses, we found that para-tumor tissue is a highly inflammatory tissue while the tumor tissue is specific with both inflammatory and cancer signaling pathways. The DEN and HBx mice models have different gene expression pattern but shared pathways. This work will help to elucidate the molecular mechanisms underlying different HCC models.

Indexed as

AnimalsCarcinoma, HepatocellularCell MovementCell ProliferationGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansKruppel-Like Factor 4Lipid MetabolismLiver NeoplasmsMiceMicroRNAsSignal TransductionTranscription FactorsKLF4 protein, humanKlf4 protein, mouseKruppel-Like Factor 4MicroRNAsTranscription FactorsHepatocellular carcinoma - DEN - Hepatitis B - Mouse model - Gene expression - Regulatory network

Identifiers

PMID29254483
PMCPMC5735680
OpenAlexW2779770364

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.