Evidence map›Paper›PMID 35322101›Full record

ArticleScientific reports2022

Identification of potentially functional circular RNAs hsa_circ_0070934 and hsa_circ_0004315 as prognostic factors of hepatocellular carcinoma by integrated bioinformatics analysis.

Pejman Morovat, Saman Morovat, Arash M Ashrafi, Shahram Teimourian

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In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Review
  3. Development and validation of a cuproptosis-related gene signature for predicting prognosis and drug sensitivity in gastric cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  4. Review
  5. Review
  6. Article
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  8. Article
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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

4 authors.

Pejman MorovatDepartment of Medical Biotechnology, Faculty of Medicine, Babol University of Medical Sciences, Babol, Iran.
Saman MorovatDepartment of Medical Genetics and Molecular Biology, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran.
Arash M AshrafiNeuroscience Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran.
Shahram TeimourianDepartment of Medical Genetics and Molecular Biology, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran. teimourian.sh@iums.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide, which has a high mortality rate and poor treatment outcomes with yet unknown molecular basis. It seems that gene expression plays a pivotal role in the pathogenesis of the disease. Circular RNAs (circRNAs) can interact with microRNAs (miRNAs) to regulate gene expression in various malignancies by acting as competitive endogenous RNAs (ceRNAs). However, the potential pathogenesis roles of the ceRNA network among circRNA/miRNA/mRNA in HCC are unclear. In this study, first, the HCC circRNA expression data were obtained from three Gene Expression Omnibus microarray datasets (GSE164803, GSE94508, GSE97332), and the differentially expressed circRNAs (DECs) were identified using R limma package. Also, the liver hepatocellular carcinoma (LIHC) miRNA and mRNA sequence data were retrieved from TCGA and differentially expressed miRNAs (DEMIs) and mRNAs (DEGs) were determined using the R DESeq2 package. Second, CSCD website was used to uncover the binding sites of miRNAs on DECs. The DECs' potential target miRNAs were revealed by conducting an intersection between predicted miRNAs from CSCD and downregulated DEMIs. Third, candidate genes were uncovered by intersecting targeted genes predicted by miRWalk and targetscan online tools with upregulated DEGs. The ceRNA network was then built using the Cytoscape software. The functional enrichment and the overall survival time of these potential targeted genes were analyzed, and a PPI network was constructed in the STRING database. Network visualization was performed by Cytoscape, and ten hub genes were detected using the CytoHubba plugin tool. Four DECs (hsa_circ_0000520, hsa_circ_0008616, hsa_circ_0070934, hsa_circ_0004315) were obtained and six miRNAs (hsa-miR-542-5p, hsa-miR-326, hsa-miR-511-5p, hsa-miR-195-5p, hsa-miR-214-3p, and hsa-miR-424-5p) which are regulated by the above DECs were identified. Then 543 overlapped genes regulated by six miRNAs mentioned above were predicted. Functional enrichment analysis showed that these genes are mostly associated with regulatory pathways in cancer. Ten hub genes (TTK, AURKB, KIF20A, KIF23, CEP55, CDC6, DTL, NCAPG, CENPF, PLK4) have been screened from the PPI network of the 204 survival-related genes. KIF20A, NCAPG, TTK, PLK4, and CDC6 were selected for the highest significance p-values. At the end, a circRNA-miRNA-mRNA regulatory axis was established for five final selected hub genes. This study implies the potential pathogenesis of the obtained network and proposes that the two DECs (has_circ_0070934 and has_circ_0004315) may be important prognostic markers for HCC.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsMicroRNAsCell Cycle ProteinsComputational BiologyGene Regulatory NetworksHumansPrognosisProtein Serine-Threonine KinasesRNA, CircularRNA, MessengerCell Cycle ProteinsCep55 protein, humanMicroRNAsMIRN214 microRNA, humanMIRN326 microRNA, humanPLK4 protein, humanProtein Serine-Threonine KinasesRNA, CircularRNA, Messenger

Identifiers

PMID35322101
PMCPMC8943026

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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.