Evidence map›Paper›PMID 23526751›Full record

ArticleKorean journal of urology2013

Pyrosequencing Analysis of APC Methylation Level in Human Prostate Tissues: A Molecular Marker for Prostate Cancer.

Hyung-Yoon Yoon, Young-Won Kim, Ho Won Kang, Won Tae Kim, Seok-Joong Yun, Sang-Cheol Lee, Wun-Jae Kim, Yong-June Kim

Open access · hybridAbstract read
In one paragraph

Article in Korean journal of urology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
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

8 authors at 1 institution in 1 country.

Hyung-Yoon YoonDepartment of Urology, Chungbuk National University College of Medicine, Cheongju, Korea.
Young-Won Kim
Ho Won Kang
Won Tae Kim
Seok-Joong Yun
Sang-Cheol Lee
Wun-Jae Kim
Yong-June Kim
Chungbuk National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeEpigenetic alterations such as abnormal DNA methylation are associated with many human cancers. Differences in methylation patterns between neoplastic and normal cells can be used to detect cancer. The aim of the present study was to evaluate the effectiveness of detecting Adenomatous polyposis coli (APC) hypermethylation by quantitative pyrosequencing for discriminating between normal and prostate cancer (PCa) cells and for predicting tumor behaviors. MATERIALS AND

methodsA total of 218 human prostate tissues obtained from our institute were assessed: 106 specimens of benign prostatic hyperplasia (BPH) and 112 specimens of PCa. The methylation status of APC was analyzed by quantitative pyrosequencing. The association between the APC methylation level and clinicopathological parameters was explored.

resultsThe level of APC methylation was significantly higher in PCa specimens than in BPH specimens (33.3%±20.7% vs. 1.3%±1.8%, p<0.001). The sensitivity and specificity of APC methylation status in discriminating between PCa and BPH reached 89.3% and 98.1%, respectively. Similar results were obtained after stratification by stage, Gleason score, and prostate-specific antigen level. The APC methylation level correlated positively with Gleason score (p trend=0.016). There was no association between the APC methylation level and the PSA level or staging.

conclusionsOur results demonstrate that APC methylation is associated with PCa and its aggressive tumor features.

Indexed as

Adenomatous polyposis coliBiological markersDNA methylationProstatic neoplasmsSequence analysis

Identifiers

PMID23526751
PMCPMC3604574
OpenAlexW2101556050

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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.