Evidence map›Paper›PMID 29693709›Full record

ArticleMolecular medicine reports2018

Identification of differential expression lncRNAs in gastric cancer using transcriptome sequencing and bioinformatics analyses.

Yiyi Wang, Jue Zhang

Open access · hybridAbstract read
In one paragraph

Article in Molecular medicine reports, 2018. 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
0.6field-weighted citation impact, top 38% of its field
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, 12 citations in OpenAlex.

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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

2 authors at 2 institutions in 1 country.

Yiyi WangDepartment of Laboratory Medicine, Shanghai Shuguang Hospital Affiliated with Shanghai University of TCM, Shanghai 200021, P.R. China.
Jue ZhangDepartment of Laboratory Medicine, Shanghai Shuguang Hospital Affiliated with Shanghai University of TCM, Shanghai 200021, P.R. China.
Shanghai University · CNShanghai University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current study aimed to identify novel long non‑coding RNAs (lncRNAs) associated with gastric cancer (GC). Transcriptome sequencing of the lncRNAs and mRNAs from GC tissues and normal adjacent tissues was performed. The data were analyzed using bioinformatics analysis, specifically analysis of differentially expressed lncRNAs and mRNA, target gene prediction and functional enrichment analysis. A total of 1,181 differentially expressed mRNA and 390 differentially expressed lncRNAs were identified. The targets of upregulated lncRNAs were significantly enriched in functions associated with collagen fibril organization, whereas the downregulated lncRNA were significantly associated with ion transmembrane transport and regulation of membrane potential. A total of 7 lncRNAs were verified by reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). Following RT‑qPCR validation, AC016735.2, AP001626.1, RP11‑400N13.3 and RP11‑243M5.2 were considered to be consistent with the prediction of the bioinformatics analysis. Transcriptome sequencing and RT‑qPCR experiments identified 4 lncRNAs, including AC016735.2, AP001626.1, RP11‑400N13.3 and RP11‑243M5.2 to have an important role in the carcinogenesis of GC.

Indexed as

Computational BiologyGene Expression Regulation, NeoplasticTranscriptomeAgedFemaleGene Expression ProfilingGene Regulatory NetworksHigh-Throughput Nucleotide SequencingHumansMaleReproducibility of ResultsRNA, Long NoncodingStomach NeoplasmsRNA, Long Noncoding

Identifiers

PMID29693709
PMCPMC5983993
OpenAlexW2800710631

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.