Evidence map›Paper›PMID 37986114›Full record

ArticleJournal of ovarian research2023

The long non-coding RNA NEAT1 promotes the progression of human ovarian cancer through targeting miR-214-3p and regulating angiogenesis.

Yang Liu, Yan Li, Yanzhi Wu, Yiyue Zhao, Xi Hu, Chunyi Sun

Open access · goldAbstract read
In one paragraph

Article in Journal of ovarian research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.6field-weighted citation impact, top 7% 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

14 citing papers in PubMed, 15 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

6 authors at 1 institution in 1 country.

Yang Liu *Department of Reproduction, the Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, China. 13518735544@163.com.
Yan Li *Department of Reproduction, the Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, China.
Yanzhi WuDepartment of Reproduction, the Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, China.
Yiyue ZhaoDepartment of Reproduction, the Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, China.
Xi HuDepartment of Reproduction, the Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, China.
Chunyi SunDepartment of Gynecology, the Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, China. km20150515@163.com.
Kunming Medical University · CN

Funding

Kunming Medical University, Mechanism of RNA m6A methylation modification in POF, 2022 2020S065the Medical Discipline Leader in Health Commission of Yunnan Province D-2019004the National Natural Science Foundation of China 81860515the 'Ten Thousand People Plan' Famous Medical Project of Yunnan Province Yunnan Provincial Health and Family Planning Commission Document (2019) No. 35the 'Ten Thousand People Plan' Youth Top Talents Project of Yunnan Province, the Yunnan Provincial Department of Human Resources and Social Security Document 2018, No. 73the "Yunnan Revitalization Talent Support Program" Famous Doctor Special Project Yunwei Renfa # 8 [2022]
6 · The paper itself

Abstract

backgroundAngiogenesis and metastasis contributes substantially to the poor outcome of patients with ovarian cancer. We aimed to explore the role and mechanisms of the long non-coding RNA NEAT1 (nuclear enriched abundant transcript 1) in regulating angiogenesis and metastasis of human ovarian cancer. NEAT1 expression in human ovarian cancer tissues and cell lines including SKOV-3 and A2780 was investigated through in situ hybridization. Gene knockdown and overexpressing were achieved through lentivirus infection, transfection of plasmids or microRNA mimics. Cell viability was measured with the cell counting kit-8 assay, while apoptosis was determined by flow cytometry. Cell migration and invasion were evaluated by transwell experiments, and protein expression was determined by western blot assays or immunohistochemistry. Duo-luciferase reporter assay was employed to confirm the interaction between NEAT1 and target microRNA. In vivo tumor growth was evaluated in nude mice with xenografted SKOV-3/A2780 cells, and blood vessel formation in tumor was examined by histological staining.

resultsNEAT1 was highly expressed in ovarian cancer tissues of patients and cell lines. MiR-214-3p was identified as a sponging target of NEAT1, and they antagonizedeach other in a reciprocal manner. NEAT1-overexpressing SKOV-3 and A2780 cells had significantly increased proliferation, reduced apoptosis, and augmented abilities of migration and invasion, while cells with NEAT1-knockdown displayed markedly attenuated traits of malignancies. Additionally, the levels of NEAT1 appeared to be positively correlated with the expression levels of angiogenesis-related molecules, including Semaphorin 4D (Sema4D), Sema4D receptor Plexin B1, T-lymphoma invasion and metastasis-inducing protein-1 (Tiam1), and Rho-like GTPases Rac1/2/3. In the xenograft mouse model, more NEAT1 expression resulted in faster in vivo tumor growth, more blood vessel formation in tumor tissues, as well as higher expression levels of angiogenesis-related molecules and CD31.

conclusionsNEAT1 promotes angiogenesis and metastasis in human ovarian cancer. NEAT1 and miR-214-3p are promising targets for developing therapeutics to treat human ovarian cancer.

Indexed as

MicroRNAsOvarian NeoplasmsRNA, Long NoncodingAnimalsCell Line, TumorCell ProliferationFemaleHumansMiceMice, NudeMicroRNAsMIRN214 microRNA, humanRNA, Long NoncodingAngiogenesisMetastasismiR-214-3pNEAT1Ovarian cancer

Identifiers

PMID37986114
PMCPMC10662279
OpenAlexW4388824587

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.