Evidence map›Paper›PMID 32000807›Full record

ArticleJournal of translational medicine2020

Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma.

Chen Hao Jiang, Xin Yuan, Jiang Fen Li, Yu Fang Xie, An Zhi Zhang, Xue Li Wang, Lan Yang, Chun Xia Liu, Wei Hua Liang, Li Juan Pang and 8 more

Abstract read
In one paragraph

Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

34 citing papers in PubMed.

  1. Exploring Cirrhosis: Insights into Advances in Therapeutic Strategies.International journal of molecular sciences · 2025
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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

18 authors.

Chen Hao JiangDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Xin YuanDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Jiang Fen LiDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Yu Fang XieDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
An Zhi ZhangDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Xue Li WangDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Lan YangDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Chun Xia LiuDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Wei Hua LiangDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Li Juan PangDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Hong ZouDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Xiao Bin CuiDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Xi Hua ShenDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Yan QiDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Jin Fang JiangDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Wen Yi GuAustralian Institute of Bioengineering and Nanotechnology, University of Queensland, Brisbane, QLD, 4072, Australia.
Feng LiDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China.
Jian Ming HuDepartment of Pathology and Key Laboratory for Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Xinjiang, 832002, China. jianming.120@163.com.ORCID 0000-0002-8033-1066

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is the most common type of liver tumour, and is closely related to liver cirrhosis. Previous studies have focussed on the pathogenesis of liver cirrhosis developing into HCC, but the molecular mechanism remains unclear. The aims of the present study were to identify key genes related to the transformation of cirrhosis into HCC, and explore the associated molecular mechanisms.

methodsGSE89377, GSE17548, GSE63898 and GSE54236 mRNA microarray datasets from Gene Expression Omnibus (GEO) were analysed to obtain differentially expressed genes (DEGs) between HCC and liver cirrhosis tissues, and network analysis of protein-protein interactions (PPIs) was carried out. String and Cytoscape were used to analyse modules and identify hub genes, Kaplan-Meier Plotter and Oncomine databases were used to explore relationships between hub genes and disease occurrence, development and prognosis of HCC, and the molecular mechanism of the main hub gene was probed using Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis.

resultsIn total, 58 DEGs were obtained, of which 12 and 46 were up- and down-regulated, respectively. Three hub genes (CDKN3, CYP2C9 and LCAT) were identified and associated prognostic information was obtained. CDKN3 may be correlated with the occurrence, invasion, and recurrence of HCC. Genes closely related to changes in the CDKN3 hub gene were screened, and Kyoto Encyclopedia of Genes and Genomes (KEGGs) pathway analysis identified numerous cell cycle-related genes.

conclusionCDKN3 may affect the transformation of liver cirrhosis into HCC, and represents a new candidate molecular marker of the occurrence and progression of HCC.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticHumansLiver CirrhosisNeoplasm Recurrence, LocalBioinformaticsCDKN3Hepatocellular carcinomaLiver cirrhosisMicroarrayMolecular markersProtein–protein interactions

Identifiers

PMID32000807
PMCPMC6993496

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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.