Evidence map›Paper›PMID 25932099›Full record

ArticleInternational journal of clinical and experimental medicine2015

MiR-16 family as potential diagnostic biomarkers for cancer: a systematic review and meta-analysis.

Jianxiong Cui

Abstract read
In one paragraph

Article in International journal of clinical and experimental medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  4. Article
  5. Article
  6. Article
  7. Review
  8. MiR-15a/16-1 deficiency induces IL-10-producing CD19Journal of cellular and molecular medicine · 2019
    Article
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  12. Clinical impact of circulating microRNAs as blood-based marker in childhood acute lymphoblastic leukemia.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016
    Article
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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

1 author.

Jianxiong CuiDepartment of Oncology, Sichuan Provincial Crops Hospital of Chinese People's Armed Police Forces Leshan 614000, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer is a main public health problem all over the world with its high morbidity and mortality. MicroRNA-16 (miRNA-16, miR-16) family members have been considered as potential biomarkers in cancer diagnosis in several previous studies, but their results were inconsistent.

objectiveThe present meta-analysis was conducted to assess the diagnostic efficacy of miR-16 family for cancer systematically.

methodsMultiple search strategies and random-effects model were used. Pooled sensitivity, specificity and other parameters were calculated. Totally, 1,259 cancer patients and 855 controls from 16 articles were enrolled in this meta-analysis.

resultsThe pooled results for sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) were 0.80 (95% CI: 0.73-0.85), 0.77 (95% CI: 0.70-0.84), 3.5 (95% CI: 2.5-5.0), 0.26 (95% CI: 0.19-0.36), 14 (95% CI: 8-25) and 0.86 (95% CI: 0.82-0.88), respectively. Our subgroup analyses indicated miR-16 family assay was more appropriate in Asian populations.

conclusionsOur findings demonstrated that miR-16 family members have a relatively high value as promising biomarkers in diagnosing cancers. Nevertheless, the clinical application of miR-16 family profiling for cancers diagnosis still needs further large-scale studies and additional improvements of substantiation.

Indexed as

cancerdiagnosismeta-analysisMicroRNA-16 family

Identifiers

PMID25932099
PMCPMC4402746

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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.