Evidence map›Paper›PMID 28404921›Full record

SynthesisOncotarget2017

Circulating miR-31 as an effective biomarker for detection and prognosis of human cancer: a meta-analysis.

Yingjun Ma, Yunfang Chen, Jinbo Lin, Yi Liu, Kai Luo, Yong Cao, Tieqiang Wang, Hongwei Jin, Zhan Su, Haolin Wu and 2 more

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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Review
  2. Review
  3. Circulating MicroRNAs as Cancer Biomarkers in Liquid Biopsies.Advances in experimental medicine and biology · 2022
    Review
  4. Article
  5. Article
  6. Article
  7. Article
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

12 authors.

Yingjun MaRespiratory Medicine, Guangming District People's Hospital of Shenzhen, Shenzhen, P.R. China.
Yunfang ChenPain Department, The Eight Affiliated Hospital, Sun Yat-sen University, ShenZhen, P.R. China.
Jinbo LinMedical oncology, Longgang District Central Hospital of Shenzhen, Shenzhen, P.R. China.
Yi LiuCenter for Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, P.R. China.
Kai LuoAffiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, P.R. China.
Yong CaoCenter for Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, P.R. China.
Tieqiang WangCenter for Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, P.R. China.
Hongwei JinCenter for Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, P.R. China.
Zhan SuCenter for Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, P.R. China.
Haolin WuCenter for Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, P.R. China.
Xiaoliang ChenCenter for Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, P.R. China.
Jinquan ChengMolecular Biology Laboratory, Shenzhen Center for Disease Control and Prevention, Shenzhen, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeCirculating miR-31 was found to be associated with cancers detection and prognosis. The present meta-analysis aimed to explore the effect of circulating miR-31 on cancer detection and prognosis.

methodThe studies were accessed using multiple databases. RevMan5.3, Meta-DiSc 1.4, and STATA14.0 were used to estimate the pooled effects, heterogeneity among studies, and publication bias.

resultsA total of 14 studies with 1397 cancer patients and 1039 controls were included. For the 12 prognostic tests, the adjusted pooled-AUC was 0.79 (95% CI: 0.73-0.86) as the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odd ratio (DOR) from 10 tests was 0.79 (95% CI: 0.76-0.82), 0.79 (95% CI: 0.76-0.82), 3.81 (95% CI: 2.90-5.01), 0.26 (95% CI: 0.20-0.35), and 16.81 (95% CI: 9.67-29.25), respectively. For the 5 prognosis analyses, the pooled HR (hazard ratio) of overall survival (OS) was 1.55 (95% CI 1.30-1.86) for high versus low circulating miR-31 expression. However, high expression of circulating miR-31 did not significantly increase the risk of poor differentiation (pooled OR=1.39, 95% CI: 0.56-3.47) and LNM (pooled OR=3.46, 95% CI: 0.96-12.42) in lung cancer.

conclusionCirculating miR-31 is an effective biomarker and could be used as a component of miRs signature for cancer detection and prognosis surveillance.

Indexed as

Biomarkers, TumorDatabases, FactualHumansLung NeoplasmsMicroRNAsPrognosisProportional Hazards ModelsBiomarkers, TumorMicroRNAsMIRN31 microRNA, humancarcinomadetectionmeta-analysismiR-31prognosis

Identifiers

PMID28404921
PMCPMC5438681

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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.