Evidence map›Paper›PMID 32030273›Full record

ArticleJournal of thoracic disease2019

Identification of potential microRNAs and their targets in promoting gefitinib resistance by integrative network analysis.

Fushuang Zheng, Hongyan Zhang, Jibin Lu

Open access · diamondAbstract read
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Article in Journal of thoracic disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.4field-weighted citation impact, top 44% 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

6 citing papers in PubMed, 9 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

3 authors at 1 institution in 1 country.

Fushuang ZhengDepartment of Thoracic Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, China.
Hongyan ZhangDepartment of Thoracic Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, China.
Jibin LuDepartment of Thoracic Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, China.
China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNon-small cell lung cancer (NSCLC) accounts for about 80-85% of lung cancers. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) such as gefitinib are considered the best choice for first-line treatment for the patients with NSCLC harboring EGFR-activating alterations. Nonetheless, 10-30% of patients may not obtain an objective response and may also experience rapid progression. The aim of our research, based on the integrative bioinformatics review, was to identify the possible miRNAs involved in gefitinib resistance.

methodA gefitinib-resistant network composed of 15 miRNAs and 34 targets were constructed by using the bioinformatics analyses of three microarray datasets. Of these miRNAs, effects of miR-342-3p on gefitinib resistance were investigated on a gefitinib-resistant cell model (A549/GR and PC/GR cells).

resultsWe reported that over-expression of miR-342-3p could significantly increase the resistance to gefitinib of A549/GR and PC9/GR cells and vice versa. Then, we recognized CPA4 as a target of hsa-miR-342-3p by a luciferase reporter assay. The increase in hsa-miR-342-3p levels led to a significant reduction in CPA4 protein expression. However, the opposite results were observed upon miR-342-3p knockdown. Finally, we found that enforced CPA4 expression partially reversed miR-342-3p effects in A549/GR cells.

conclusionsCollectively, these findings suggest that the upregulation of miR-342-3p contributes to gefitinib resistance by targeting CPA4, which may serve as a potential treatment option to overcome gefitinib resistance in patients with NSCLC.

Indexed as

CPA4GefitinibmiR-342-3pnon-small cell lung cancer (NSCLC)

Identifiers

PMID32030273
PMCPMC6988016
OpenAlexW2995312119

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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.