Evidence map›Paper›PMID 29297603›Full record

SynthesisThoracic cancer2018

APC gene promoter aberrant methylation in serum as a biomarker for breast cancer diagnosis: A meta-analysis.

Xiaojun Qian, Liwei Ruan

Abstract readMeta-Analysis
In one paragraph

Synthesis in Thoracic cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Review
  7. 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

2 authors.

Xiaojun QianDepartment of Breast and Thyroid Surgery, Shaoxing People's Hospital, Shaoxing Hospital of Zhejiang University, Shaoxing, China.ORCID 0000-0002-2523-0684
Liwei RuanDepartment of Breast and Thyroid Surgery, Shaoxing People's Hospital, Shaoxing Hospital of Zhejiang University, Shaoxing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of this study was to evaluate the clinical efficacy of APC gene promoter methylation in serum as a biomarker for breast cancer (BC) diagnosis.

methodsTwo reviewers systematically searched online resources to identify the publications relevant to APC gene promoter methylation and BC. The data of true positive, false positive, false negative, and true negative were extracted from each included study and pooled for diagnostic sensitivity, specificity, and summary receiver operating characteristic curve.

resultsTwelve studies finally fulfilled the inclusion criteria and were included in this meta-analysis. The diagnostic sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio, and area under the receiver operating characteristic curve were 0.20 (95% confidence interval [CI] 0.17-0.23), 0.96 (95% CI 0.93-0.97), 3.69 (95% CI 1.60-8.50), 0.83 (95% CI 0.75-0.92), 4.58 (95% CI 1.85-11.37) and 0.80, respectively. A Deeks' funnel plot and Egger's line regression test (t = 1.43, P = 0.18) indicated no publication bias was present.

conclusionBecause of low sensitivity, APC gene promoter methylation in serum was not suitable for BC screening. However, as specificity was very high, detection of serum APC gene promoter methylation could be used as tool to confirm BC.

Indexed as

Adenomatous Polyposis Coli ProteinBiomarkers, TumorBreast NeoplasmsDNA MethylationEarly Detection of CancerFemaleHumansPromoter Regions, GeneticAdenomatous Polyposis Coli ProteinAPC protein, humanBiomarkers, TumorAPC genebreast cancermeta-analysismethylationpromoter

Identifiers

PMID29297603
PMCPMC5792726

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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.