Evidence map›Paper›PMID 31186635›Full record

ArticleJournal of oncology2019

Prognostic Effect of Long Noncoding RNA NEAT1 Expression Depends on p53 Mutation Status in Cancer.

Masashi Idogawa, Hiroshi Nakase, Yasushi Sasaki, Takashi Tokino

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in Journal of oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.4field-weighted citation impact, top 22% 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

20 citing papers in PubMed, 27 citations in OpenAlex.

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  18. The role ofContemporary oncology (Poznan, Poland) · 2020
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Masashi IdogawaDepartment of Medical Genome Sciences, Research Institute for Frontier Medicine, Sapporo Medical University School of Medicine, Japan.ORCID 0000-0002-8507-1726
Hiroshi NakaseDepartment of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Japan.
Yasushi SasakiBiology, Department of Liberal Arts and Sciences Center for Medical Education, Sapporo Medical University, Japan.
Takashi TokinoDepartment of Medical Genome Sciences, Research Institute for Frontier Medicine, Sapporo Medical University School of Medicine, Japan.ORCID 0000-0001-9428-9543
Sapporo Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, many studies have revealed that long noncoding RNAs (lncRNAs) play important roles in various biological and pathological processes. Our previous study reported that lncRNA NEAT1 is a direct transcriptional target of p53. NEAT1 is an essential component of paraspeckles, which have recently been identified as a novel type of nuclear compartment. Although our previous findings indicate that NEAT1 induction contributes to the tumor-suppressor function of p53, the role of NEAT1 in cancer is still controversial. In this study, we comprehensively analyzed the relationship between NEAT1 expression and p53 mutation status. Interestingly, survival analysis based on NEAT1 expression in several cancer tissues revealed that the p53 wild-type group with high NEAT1 expression had a good prognosis, while poor prognosis or no correlation between NEAT1 expression and survival was observed in the p53-mutated group. These results demonstrate that the tumor-suppressive effect of NEAT1 depends on p53 function and is consistent with our previous report showing that NEAT1 supports the tumor-suppressive function of p53. Specifically, NEAT1 seems to play a tumor-suppressive role only in the presence of wild-type p53. These results provide important clues to the roles of NEAT1 in cancer.

Identifiers

PMID31186635
PMCPMC6521466
OpenAlexW2942638078

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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.