Evidence map›Paper›PMID 28299977›Full record

ArticleExperimental biology and medicine (Maywood, N.J.)2017

Identification of lymph node metastasis-related microRNAs in lung adenocarcinoma and analysis of the underlying mechanisms using a bioinformatics approach.

Li Yan, Demin Jiao, Huizhen Hu, Jian Wang, Xiali Tang, Jun Chen, Qingyong Chen

Open access · bronzeAbstract read
In one paragraph

Article in Experimental biology and medicine (Maywood, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

11 citing papers in PubMed, 21 citations in OpenAlex.

  1. Plasma miR-150-5p as a Biomarker for Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2023
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  6. miR-146a-5p Plays an Oncogenic Role in NSCLC via Suppression of TRAF6.Frontiers in cell and developmental biology · 2020
    Article
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  11. Observational
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 at 1 institution in 1 country.

Li Yan1 Department of Oncology, The 117th Hospital of PLA, Hangzhou 310013, P.R. China.
Demin Jiao2 Department of Respiratory Disease, The 117th Hospital of PLA, Hangzhou 310013, P.R. China.
Huizhen Hu2 Department of Respiratory Disease, The 117th Hospital of PLA, Hangzhou 310013, P.R. China.
Jian Wang2 Department of Respiratory Disease, The 117th Hospital of PLA, Hangzhou 310013, P.R. China.
Xiali Tang2 Department of Respiratory Disease, The 117th Hospital of PLA, Hangzhou 310013, P.R. China.
Jun Chen2 Department of Respiratory Disease, The 117th Hospital of PLA, Hangzhou 310013, P.R. China.
Qingyong Chen2 Department of Respiratory Disease, The 117th Hospital of PLA, Hangzhou 310013, P.R. China.
117th Hospital of People's Liberation Army · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to screen lymphatic metastasis-related microRNAs (miRNAs) in lung adenocarcinoma and explore their underlying mechanisms using bioinformatics. The miRNA expression in primary lung adenocarcinoma, matched adjacent non-tumorigenic and lymph node metastasis tissues of patients were profiled via microarray. The screened metastasis-related miRNAs were then validated using quantitative real-time PCR in a second cohort of lung adenocarcinoma patients with lymphatic metastasis. Significance was determined using a paired t-test. Target genes of the metastasis-related miRNAs were predicted using TargetScan, and transcription factors (TFs) were predicted based on the TRANSFAC and ENCODE databases. Furthermore, the related long non-coding RNAs (lncRNAs) were screened with starBase v2.0. The miRNA-TF-mRNA and lncRNA-miRNA-mRNA networks were constructed to determine the key interactions associated with lung adenocarcinoma metastasis. According to the miRNA microarray results, there were 10 miRNAs that were differentially expressed in metastatic tissues compared with primary tumor and adjacent non-tumorigenic tissues. Among them were increased levels of miR-146a-5p, miR-342-3p, and miR-150-5p, which were validated in the second cohort. Based on the miRNA-TF-mRNA network, vascular endothelial growth factor A and transcription factors (TFs) including TP53, SMAD4, and EP300 were recognized as critical targets of the three miRNAs. Interactions involving SNHG16-miR-146a-5p-SMAD4 and RP6-24A23.7-miR-342-3p/miR-150-5p-EP300 were highlighted according to the lncRNA-miRNA-mRNA network. miR-146a-5p, miR-342-3p, and miR-150-5p are lymphatic metastasis-related miRNAs in lung adenocarcinoma. Bioinformatics analyses demonstrated that SNHG16 might inhibit the interaction between miR-146a-5p and SMAD4, while RP6-24A23.7 might weaken miR-342-3p-EP300 and miR-150-5p-EP300 interactions in metastasis.

Indexed as

AdenocarcinomaCase-Control StudiesComputational BiologyFemaleHumansLung NeoplasmsLymphatic MetastasisMaleMicroRNAsMiddle AgedOligonucleotide Array Sequence AnalysisReal-Time Polymerase Chain ReactionRNA, NeoplasmMicroRNAsRNA, Neoplasmbioinformatics approachlong non-coding RNALung adenocarcinomalymphatic metastasismicroRNAsunderlying mechanisms

Identifiers

PMID28299977
PMCPMC5363686
OpenAlexW2553239296

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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.