Evidence map›Paper›PMID 31423268›Full record

ArticleOncology letters2019

Integrated miRNA profiling and bioinformatics analyses reveal upregulated miRNAs in gastric cancer.

Chen Yuan, Yue Zhang, Wenwen Tu, Yusheng Guo

Abstract read
In one paragraph

Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  17. Good or not good: Role of miR-18a in cancer biology.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology
    Review
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.

Chen YuanDepartment of Emergency, Huashan Hospital, Fudan University, Shanghai 200040, P.R. China.
Yue ZhangDepartment of Gerontology, Shanghai Jiaotong University Affiliated to Sixth People's Hospital, Shanghai 201499, P.R. China.
Wenwen TuDepartment of Cardiology, Jingan District Zhabei Central Hospital, Shanghai 200040, P.R. China.
Yusheng GuoDepartment of Emergency, Huashan Hospital, Fudan University, Shanghai 200040, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer is one of the most common malignancies in China and exhibits a poor prognosis. The most significant challenge for gastric cancer treatment is the absence of early diagnostic biomarkers. MicroRNAs (miRNAs) are small non-coding RNAs, which possess clinical value in a number of different types of cancer. The current study identified 13 miRNAs (hsa-miR-22, hsa-miR-545, hsa-let-7i, hsa-miR-15b, hsa-miR-221, hsa-miR-196a, hsa-miR-20a, hsa-miR-196b, hsa-miR-93, hsa-miR-19a, hsa-miR-503, hsa-miR-106b and hsa-miR-18a) that were significantly overexpressed in GC, by analyzing 1,000 GC samples included in four public datasets, including GSE23739, GSE78091, GSE30070 and The Cancer Genome Atlas. Furthermore, it was revealed that the expression levels of these 13 miRNAs were significantly higher in gastric cancer tissues of grades I, II and III compared with normal controls. Gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that the differentially expressed miRNAs were involved in regulating transcription, protein amino acid phosphorylation, signal transduction, protein binding, zinc ion binding, the mitogen-activated protein kinase signaling pathway and focal adhesion. In summary, the present study may provide potential new therapeutic and prognostic targets for gastric cancer.

Indexed as

bioinformatics analysisexpression profilinggastric adenocarcinomamicroRNA

Identifiers

PMID31423268
PMCPMC6607103

What OpenQuestion holds

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Registered trials

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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.