Evidence map›Paper›PMID 34992437›Full record

ArticleInternational journal of general medicine2021

CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis.

Zhiqiang Yang, Xinglang Wu, Junbo Li, Qiang Zheng, Junwei Niu, Shengwei Li

Open access · goldAbstract read
In one paragraph

Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.0field-weighted citation impact, top 11% 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, 21 citations in OpenAlex.

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  14. ASPM promotes ATR-CHK1 activation and stabilizes stalled replication forks in response to replication stress.Proceedings of the National Academy of Sciences of the United States of America · 2022
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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

6 authors at 1 institution in 1 country.

Zhiqiang YangDepartment of Hepatobiliary Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.ORCID 0000-0001-6242-6742
Xinglang WuDepartment of Hepatobiliary Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Junbo LiDepartment of Hepatobiliary Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Qiang ZhengDepartment of Hepatobiliary Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Junwei NiuDepartment of Hepatobiliary Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Shengwei LiDepartment of Hepatobiliary Surgery, Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Dalian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is a highly malignant, recurrent and drug-resistant tumor, and patients often lose the opportunity for surgery when they are diagnosed. Abnormal gene expression is closely related to the occurrence of HCC. The aim of the present study was to identify the differentially expressed genes (DEGs) between tumor tissue and non-tumor tissue of HCC samples in order to investigate the mechanisms of liver cancer.

methodsThe gene expression profile (GSE62232, GSE89377, and GSE112790) was downloaded from the Gene Expression Omnibus (GEO) and analyzed using the online tool GEO2R to identify differentially expressed genes (DEGs). Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) of these DEGs was analyzed based on the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. In addition, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic value of hub genes expression. HPA database was used to reveal the differences in protein level of hub genes.

resultsA total of 50 upregulated DEGs and 122 downregulated DEGs were identified. Among them, ten hub genes with a high degree of connectivity were picked out. Overexpression of these hub genes was associated with unfavorable prognosis of HCC.

conclusionOur study suggests that CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM were overexpressed in HCC compared with normal liver tissue. Overexpression of these genes was an unfavorable prognostic factor of HCC patients. Further study is needed to explore the value of them in the diagnosis and treatment of HCC.

Indexed as

expression profiling datahepatocellular carcinomahub genesprotein–protein interaction network

Identifiers

PMID34992437
PMCPMC8710976
OpenAlexW4200288519

What OpenQuestion holds

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LicenceCC BY-NC
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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.