Evidence map›Paper›PMID 30092792›Full record

ArticleJournal of translational medicine2018

A circRNA-miRNA-mRNA network identification for exploring underlying pathogenesis and therapy strategy of hepatocellular carcinoma.

Dan-Dan Xiong, Yi-Wu Dang, Peng Lin, Dong-Yue Wen, Rong-Quan He, Dian-Zhong Luo, Zhen-Bo Feng, Gang Chen

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 163 papers.

0numbers the graph read from it
0cells of the map it votes in
163citing papers in PubMed
12.2field-weighted citation impact, top 1% 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

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3 · Its place in the literature

Who cites it

163 citing papers in PubMed, 300 citations in OpenAlex.

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103 more citing papers are in PubMed but not listed here.

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

8 authors at 2 institutions in 1 country.

Dan-Dan XiongDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
Yi-Wu DangDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
Peng LinDepartment of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
Dong-Yue WenDepartment of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
Rong-Quan HeDepartment of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
Dian-Zhong LuoDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
Zhen-Bo FengDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China. fengzhenbo_gxmu@163.com.
Gang ChenDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China. chengang@gxmu.edu.cn.
Guangxi Medical University · CNFirst Affiliated Hospital of GuangXi Medical University · CN

Funding

National Natural Science Foundation of China NSFC81560386National Natural Science Foundation of China NSFC81560489Natural Science Foundation of Guangxi, China 2017GXNSFAA198017
6 · The paper itself

Abstract

backgroundCircular RNAs (circRNAs) have received increasing attention in human tumor research. However, there are still a large number of unknown circRNAs that need to be deciphered. The aim of this study is to unearth novel circRNAs as well as their action mechanisms in hepatocellular carcinoma (HCC).

methodsA combinative strategy of big data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology was employed to dig HCC-related circRNAs and to explore their potential action mechanisms. A connectivity map (CMap) analysis was conducted to identify potential therapeutic agents for HCC.

resultsSix differently expressed circRNAs were obtained from three Gene Expression Omnibus microarray datasets (GSE78520, GSE94508 and GSE97332) using the RobustRankAggreg method. Following the RT-qPCR corroboration, three circRNAs (hsa_circRNA_102166, hsa_circRNA_100291 and hsa_circRNA_104515) were selected for further analysis. miRNA response elements of the three circRNAs were predicted. Five circRNA-miRNA interactions including two circRNAs (hsa_circRNA_104515 and hsa_circRNA_100291) and five miRNAs (hsa-miR-1303, hsa-miR-142-5p, hsa-miR-877-5p, hsa-miR-583 and hsa-miR-1276) were identified. Then, 1424 target genes of the above five miRNAs and 3278 differently expressed genes (DEGs) on HCC were collected. By intersecting the miRNA target genes and the DEGs, we acquired 172 overlapped genes. A protein-protein interaction network based on the 172 genes was established, with seven hubgenes (JUN, MYCN, AR, ESR1, FOXO1, IGF1 and CD34) determined from the network. The Gene Oncology, Kyoto Encyclopedia of Genes and Genomes and Reactome enrichment analyses revealed that the seven hubgenes were linked with some cancer-related biological functions and pathways. Additionally, three bioactive chemicals (decitabine, BW-B70C and gefitinib) based on the seven hubgenes were identified as therapeutic options for HCC by the CMap analysis.

conclusionsOur study provides a novel insight into the pathogenesis and therapy of HCC from the circRNA-miRNA-mRNA network view.

Indexed as

Gene Regulatory NetworksAlgorithmsAntineoplastic AgentsBase SequenceCarcinoma, HepatocellularDatabases as TopicGene Expression Regulation, NeoplasticGene OntologyHumansLiver NeoplasmsMicroRNAsMolecular Sequence AnnotationProtein Interaction MapsRNARNA, CircularRNA, MessengerAntineoplastic AgentsMicroRNAsRNARNA, CircularRNA, MessengerceRNACircRNACMapHepatocellular carcinoma

Identifiers

PMID30092792
PMCPMC6085698
OpenAlexW2887683110

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.