Evidence map›Paper›PMID 28157702›Full record

SynthesisOncotarget2017

Long noncoding RNA MEG3, a potential novel biomarker to predict the clinical outcome of cancer patients: a meta-analysis.

Xiangrong Cui, Xuan Jing, Chunlan Long, Jie Tian, Jing Zhu

Abstract readMeta-Analysis
In one paragraph

Synthesis in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

23 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  9. The Clinical Significance ofOncoTargets and therapy · 2020
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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

5 authors.

Xiangrong CuiPediatric Research Institute, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400014, China.
Xuan JingClinical Laboratory, Shanxi Province People's Hospital, Shanxi, 030000, China.
Chunlan LongPediatric Research Institute, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400014, China.
Jie TianCardiovascular Department (Internal Medicine), Children's Hospital of Chongqing Medical University, Chongqing, 400014, China.
Jing ZhuPediatric Research Institute, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, 400014, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Numerous studies have demonstrated that the expression level of maternally expressed gene 3 (MEG3) was lost in various cancers. Low expression of MEG3 is associated with an increased risk of metastasis and a poor prognosis in cancer patients. This meta-analysis investigated the association between MEG3 levels and distant metastasis (DM), lymph node metastasis (LNM), overall survival (OS), and recurrence-free survival (RFS) of cancer patients. A total of 536 participants from 9 articles were finally enrolled. The results showed a significant negative association between MEG3 levels and DM (OR = 2.16, 95% CI = 0.99-4.71, P = 0.05, fixed-effect), and it could also predict poor OS (HR = 0.43, 95% CI = 0.15-1.24, P = 0.006, fixed-effect) and RFS (HR = 0.52, 95% CI = 0.29-0.92, P = 0.02, fixed-effect) in cancer patients. In conclusion, this meta-analysis indicated that MEG3 might serve as a potential novel biomarker for indicating the clinical outcomes in human cancers.

Indexed as

Biomarkers, TumorHumansNeoplasmsPrognosisRNA, Long NoncodingBiomarkers, TumorMEG3 non-coding RNA, humanRNA, Long Noncodingcarcinomaclinical outcomelncRNAMEG3meta-analysis

Identifiers

PMID28157702
PMCPMC5386668

What OpenQuestion holds

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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.