Evidence map›Paper›PMID 27185598›Full record

ArticleThe Kaohsiung journal of medical sciences2016

Bioinformatics microarray analysis and identification of gene expression profiles associated with cirrhotic liver.

Kun-Ming Chan, Tsung-Han Wu, Ting-Jung Wu, Hong-Shiue Chou, Ming-Chin Yu, Wei-Chen Lee

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Article in The Kaohsiung journal of medical sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

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8citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Kun-Ming ChanDepartment of General Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan. Electronic address: chankunming@adm.cgmh.org.tw.
Tsung-Han WuDepartment of General Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Ting-Jung WuDepartment of General Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Hong-Shiue ChouDepartment of General Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Ming-Chin YuDepartment of General Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Wei-Chen LeeDepartment of General Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cirrhosis is the endpoint of liver fibrosis that is accompanied by limited regeneration capacity and complications and is the ultimate cause of death in many patients. Despite this, few studies have thoroughly looked at the gene expression profiles in the cirrhotic liver. Hence, this study aims to identify the genes that were differentially expressed in the cirrhotic liver and to explore the putative related signaling pathway and interaction networks. The gene expression profiles of cirrhotic livers and noncirrhotic livers were examined and compared using microarray gene analysis. Proteins encoded by the differentially expressed genes were analyzed for functional clustering and signaling pathway involvement using MetaCore bioinformatics analyses. The Gene Ontology analysis as well as the Kyoto encyclopedia of Genes and Genomes pathway analysis were also performed. A total of 213 significant genes were differentially expressed at more than a two-fold change in cirrhotic livers as compared to noncirrhotic livers. Of these, 105 upregulated genes and 63 downregulated genes were validated through MetaCore bioinformatics analyses. The signaling pathways and major functions of proteins encoded by these differentially expressed genes were further analyzed; results showed that the cirrhotic liver has a unique gene expression pattern related to inflammatory reaction, immune response, and cell growth, and is potentially cancer related. Our findings suggest that the microarray analysis may provide clues to the molecular mechanisms of liver cirrhosis for future experimental studies. However, further exploration of areas regarding therapeutic strategy might be possible to support metabolic activity, decrease inflammation, or enhance regeneration for liver cirrhosis.

Indexed as

Gene Expression ProfilingGene Expression RegulationAdultAgedComputational BiologyDemographyDown-RegulationFemaleGene Regulatory NetworksHumansInflammationLiver CirrhosisMaleMiddle AgedOligonucleotide Array Sequence AnalysisSignal TransductionBioinformaticsGene profilesLiverLiver cirrhosisMicroarray analysis

Identifiers

PMID27185598
PMCPMC11916604

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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.