Evidence map›Paper›PMID 37369491›Full record

ArticleIn vivo (Athens, Greece)

Over-expression of Long Non-coding RNA Urothelial Cancer-associated 1 as a Predictive Marker for Prostate Cancer.

Yoo Jin Lee, Sung Gu Kang, Chul Hwan Kim

Abstract read
In one paragraph

Article in In vivo (Athens, Greece). 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
–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

2 citing papers in PubMed.

  1. Review
  2. 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

3 authors.

Yoo Jin LeeDepartment of Pathology, Korea University Anam Hospital, Seoul, Republic of Korea.
Sung Gu KangDepartment of Urology, Korea University Anam Hospital, Seoul, Republic of Korea.
Chul Hwan KimDepartment of Pathology, Korea University Anam Hospital, Seoul, Republic of Korea; chkap@korea.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimTo determine the expression of long non-coding RNA urothelial cancer-associated 1 (UCA1) by performing array-based quantitative polymerase chain reaction (PCR) and to identify the clinicopathological significance of UCA1 expression in prostate cancer using in situ hybridization (ISH) of surgically resected specimens. MATERIALS AND

methodsArray-based quantitative PCR was performed using 10 pairs of fresh malignant (prostate cancer) and normal tissue samples to determine UCA1 expression. Single-color RNA ISH of surgically resected prostate cancer specimens was performed using 70 formalin-fixed, paraffin-embedded tissue specimens to examine the clinicopathological significance of UCA1.

resultsProstate cancer tissues exhibited higher levels of UCA1 expression than paired benign tissues. Furthermore, a correlation between high UCA1 expression and unfavourable clinicopathological characteristics, including advanced pathologic T stage, extraprostatic extension, presence of Gleason pattern 5, and involvement of the resection margins was observed. Notably, increased UCA1 expression significantly correlated with high- or very-high-risk patients, as defined by the 2023 National Comprehensive Cancer Network guidelines.

conclusionUCA1 could be used as a novel diagnostic and prognostic biomarker for establishing an effective treatment protocol for prostate cancer.

Indexed as

Carcinoma, Transitional CellProstatic NeoplasmsRNA, Long NoncodingUrinary Bladder NeoplasmsBiomarkers, TumorGene Expression Regulation, NeoplasticHumansMaleNeoplasm GradingBiomarkers, TumorRNA, Long NoncodingUCA1 RNA, humanarray-based quantitative polymerase chain reactionin-situ hybridizationLong non-coding RNAprostate cancerurothelial cancer-associated 1

Identifiers

PMID37369491
PMCPMC10347924

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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.