Evidence map›Paper›PMID 29218114›Full record

ArticleAmerican journal of translational research2017

Comparative transcriptome analysis of the global circular RNAs expression profiles between SHEE and SHEEC cell lines.

Jiachun Sun, Xiaozhi Yuan, Xiangming Li, Dengkui Wang, Tanyou Shan, Wei Wang, Qiuyan Wan, Xinshuai Wang, Junqiang Yan, Shegan Gao

Abstract read
In one paragraph

Article in American journal of translational research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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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

10 citing papers in PubMed.

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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

10 authors.

Jiachun SunHenan Key Laboratory of Cancer Epigenetics, Cancer InstituteHenan, China.
Xiaozhi YuanHenan Key Laboratory of Cancer Epigenetics, Cancer InstituteHenan, China.
Xiangming LiDepartment of Orthopedics, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and TechnologyLuoyang 471003, China.
Dengkui WangClinical Laboratory, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and TechnologyLuoyang 471003, China.
Tanyou ShanHenan Key Laboratory of Cancer Epigenetics, Cancer InstituteHenan, China.
Wei WangHenan Key Laboratory of Cancer Epigenetics, Cancer InstituteHenan, China.
Qiuyan WanDepartment of Neurology, Gansu Provincial Hospital of Traditional Chinese MedicineLanzhou 730000, China.
Xinshuai WangHenan Key Laboratory of Cancer Epigenetics, Cancer InstituteHenan, China.
Junqiang YanNeurological Diseases Institute, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and TechnologyLuoyang 471003, China.
Shegan GaoHenan Key Laboratory of Cancer Epigenetics, Cancer InstituteHenan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Esophageal squamous cell carcinoma (ESCC) is widely regarded as one of the most lethal types of cancer around the world. The fact that early detection of ESCC could dramatically improve the treatment outcome of the patients has sparked considerable interest in searching for reliable and accurate diagnostic biomarkers. Recently, circular RNAs (circRNA) have emerged as a new type of non-coding RNAs with significant RNase resistance, wide abundance and remarkable internal diversity. There is also increasing evidence suggesting that circRNAs could be implicated in the pathogenesis of cancer and other diseases. In this study, we performed a comparative analysis of the global circRNA expression profiles in normal and malignant esophageal epithelial cell lines by a combination of RNA sequencing and bioinformatics analysis. We identified 813 significantly up-regulated and 445 down-regulated circRNA candidates, of which 32 were subsequently validated by quantitative real-time reverse transcription polymerase chain reaction analysis. The differentially expressed circRNAs were found to be associated with pathways involved in metabolism, cell apoptosis, proliferation and migration, which are commonly altered in cancer cells. Based on the obtained data, we constructed a circRNA-miRNA interaction network, in which circRNA9927-NBEAL1 represented the biggest node. Our study could lay the groundwork for further investigation concerning the pathological roles of circRNAs in ESCC.

Indexed as

circular RNAsmiRNASHEESHEECTranscriptome

Identifiers

PMID29218114
PMCPMC5714800

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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.