Evidence map›Paper›PMID 29744354›Full record

ArticleBioMed research international2018

Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis.

Wei Gu, Ying Sun, Xiong Zheng, Jin Ma, Xiao-Ying Hu, Tian Gao, Mei-Jie Hu

Abstract read
In one paragraph

Article in BioMed research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Review
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  13. The Emerging Role of Circ-SHPRH in Cancer.OncoTargets and therapy · 2021
    Review
  14. Article
  15. Identification and Expression of Several Circular RNAs and Knockdown of hsa_circ_0005556 Exerts Oncogenic Functions by miR-767-5p in Gastric Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2020
    Article
  16. CircRNA: a rising star in gastric cancer.Cellular and molecular life sciences : CMLS · 2020
    Review
  17. Article
  18. Article
  19. Article
  20. Article
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

7 authors.

Wei GuDepartment of Gastroenterology, Ruijin Hospital, Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Ying SunDepartment of Gastroenterology, Ruijin Hospital, Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Xiong ZhengDepartment of Gastroenterology, Ruijin Hospital, Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Jin MaDepartment of Gastroenterology, Ruijin Hospital, Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Xiao-Ying HuDepartment of Gastroenterology, Ruijin Hospital, Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Tian GaoDepartment of Geriatrics, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.ORCID 0000-0002-8561-4540
Mei-Jie HuDepartment of Gastroenterology, Ruijin Hospital, Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China.ORCID 0000-0001-7096-2086

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer is one of the common malignant tumors worldwide. Increasing studies have indicated that circular RNAs (circRNAs) play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets. However, the precise mechanism and functions of most circRNAs are still unknown in gastric cancer. In the present study, we performed a microarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples. The miRNA expression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO). The differentially expressed circRNAs and miRNAs were identified through fold change filtering. The interactions between circRNAs and miRNAs were predicted by Arraystar's home-made miRNA target prediction software. After circRNA-related miRNAs and dysregulated miRNAs were intersected, 23 miRNAs were selected. The target mRNAs of miRNAs were predicted by TarBase v7.0. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods for the target mRNAs. The results of pathway analysis showed that p53 signaling pathway and hippo signal pathway were significantly enriched and CCND2 was a cross-talk gene associated with them. Finally, a circRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes and hsa_circRNA_101504 played a central role in the network.

Indexed as

Biomarkers, TumorComputational BiologyGene Expression Regulation, NeoplasticHumansMicroarray AnalysisMicroRNAsOligonucleotide Array Sequence AnalysisRNARNA, CircularRNA, MessengerSignal TransductionStomach NeoplasmsTranscriptomeBiomarkers, TumorMicroRNAsRNARNA, CircularRNA, Messenger

Identifiers

PMID29744354
PMCPMC5878900

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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.