Evidence map›Paper›PMID 30538557›Full record

ArticleCancer management and research2018

Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma.

Yujia Zheng, Yulin Liu, Songfeng Zhao, Zhetian Zheng, Chunyi Shen, Li An, Yongliang Yuan

Abstract read
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Article in Cancer management and research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

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46citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

46 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yujia ZhengBiotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Yulin LiuBiotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Songfeng ZhaoDepartment of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China, yylcpu@126.com.
Zhetian ZhengSchool of Computer Science, Yangtze University, Jingzhou, Hubei, China.
Chunyi ShenBiotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Li AnInstitute of Quality Standard and Testing Technology for Agro-products, Henan Academy of Agricultural Sciences, Zhengzhou, China, haaslian@126.com.
Yongliang YuanDepartment of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China, yylcpu@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers. MATERIALS AND

methodsOne hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between HCC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses, and protein-protein interaction network were used. Cox proportional hazards regression analysis was used to identify significant prognostic DEGs, with which a gene expression signature prognostic prediction model was identified in The Cancer Genome Atlas (TCGA) project discovery cohort. The robustness of this panel was assessed in the GSE14520 cohort. We verified details of the gene expression level of the key molecules through TCGA, GEO, and qPCR and used immunohistochemistry for substantiation in HCC tissues. The methylation states of these genes were also explored.

resultsNinety-eight genes, consisting of 13 upregulated and 85 downregulated genes, were screened out in three datasets. KEGG and Gene ontology analysis for the DEGs revealed important biological features of each subtype. Protein-protein interaction network analysis was constructed, consisting of 64 nodes and 115 edges. A subset of four genes (

conclusionThis study identified a novel four-gene signature biomarker for predicting the prognosis of HCC. The biomarkers may also reveal molecular mechanisms underlying development of the disease and provide new insights into interventional strategies.

Indexed as

biomarkerdifferentially expressed genesGEOhepatocellular carcinomaTCGA

Identifiers

PMID30538557
PMCPMC6252784

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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.