Evidence map›Paper›PMID 31335995›Full record

ArticleCancer science2019

The competitive endogenous RNA regulatory network reveals potential prognostic biomarkers for overall survival in hepatocellular carcinoma.

Zhiqiao Zhang, Jing Li, Tingshan He, Yanling Ouyang, Yiyan Huang, Qingbo Liu, Peng Wang, Jianqiang Ding

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer science, 2019. 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. Review
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  3. The potential ofJournal of thoracic disease · 2025
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  13. [Identification of onco-miRNAs in hepatocellular carcinoma and analysis of their regulatory network].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 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

8 authors.

Zhiqiao ZhangDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China.ORCID https://orcid.org/0000-0003-4631-8818
Jing LiDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China.
Tingshan HeDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China.
Yanling OuyangDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China.
Yiyan HuangDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China.
Qingbo LiuDepartment of Hepatobiliary Surgery, Shunde Hospital, Southern Medical University, Shunde, China.
Peng WangDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China.
Jianqiang DingDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, China.

Funding

Guangdong Provincial Health Department and Guangdong Provincial Financial Department A2016450Guangdong Provincial Health Department and Guangdong Provincial Financial Department B2018237
6 · The paper itself

Abstract

The aim of the present study is to construct a competitive endogenous RNA (ceRNA) regulatory network by using differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in patients with hepatocellular carcinoma (HCC), and to construct a prognostic model for predicting overall survival (OS) of HCC patients. Differentially expressed lncRNAs, miRNAs, and mRNAs were explored between HCC tissues and normal liver tissues. A prognostic model was built for predicting OS of HCC patients and receiver operating characteristic curves were used to evaluate the performance of the prognostic model. There were 455 differentially expressed lncRNAs, 181 differentially expressed miRNAs, and 5035 differentially expressed mRNAs. A ceRNA regulatory network was constructed based on 43 lncRNAs, 37 miRNAs, and 105 mRNAs. Eight mRNA biomarkers (H2AFX, SQSTM1, ITM2A, PFKP, TPD52L1, ACSL4, STRN3, and CPEB3) were identified as independent risk factors by multivariate Cox regression and were used to develop a prognostic model for OS. The C-indexes in the model group were 0.776 (95% confidence interval [CI], 0.730-0.822), 0.745 (95% CI, 0.699-0.791), and 0.789 (95% CI, 0.743-0.835) for 1-, 3-, and 5-year OS, respectively. The current study revealed potential molecular biological regulation pathways and prognostic biomarkers by the ceRNA regulatory network. A prognostic model based on prognostic mRNAs in the ceRNA network might be helpful to predict the individual mortality risk for HCC patients. The individual mortality risk calculator can be used by visiting the following URL: https://zhangzhiqiao.shinyapps.io/Smart_cancer_predictive_system_HCC/.

Indexed as

Gene Expression Regulation, NeoplasticAdultAgedBiomarkers, TumorCarcinoma, HepatocellularDatasets as TopicFemaleFollow-Up StudiesGene Expression ProfilingHumansKaplan-Meier EstimateLiverLiver NeoplasmsMaleMicroRNAsMiddle AgedBiomarkers, TumorMicroRNAsRNA, Long NoncodingRNA, Messengercompetitive endogenous RNAhepatocellular carcinomamRNAoverall survivalprognostic model

Identifiers

PMID31335995
PMCPMC6726690

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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.