Evidence map›Paper›PMID 38331878›Full record

ArticleWorld journal of surgical oncology2024

Individualized detection of TMPRSS2-ERG fusion status in prostate cancer: a rank-based qualitative transcriptome signature.

Yawei Li, Hang Su, Kaidong Liu, Zhangxiang Zhao, Yuquan Wang, Bo Chen, Jie Xia, Huating Yuan, De-Shuang Huang, Yunyan Gu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Overdiagnosis and Overtreatment in Prostate Cancer.Diseases (Basel, Switzerland) · 2025
    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

10 authors at 3 institutions in 1 country.

Yawei Li *School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China.
Hang Su *School of Clinical Medicine, Guizhou Medical University, Guiyang, Guizhou, China.
Kaidong LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Zhangxiang ZhaoThe Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Yuquan WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Bo ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Jie XiaSchool of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China.
Huating YuanSchool of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China.
De-Shuang HuangBioinformatics and BioMedical Bigdata Mining Laboratory, School of Big Health, Guizhou Medical University, Guiyang, Guizhou, China. 4660094239@qq.com.
Yunyan GuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China. guyunyan@ems.hrbmu.edu.cn.
Guiyang Medical University · CNHarbin Medical University · CNFirst Affiliated Hospital of Jinan University · CN

Funding

National Natural Science Foundation of China 32270710
6 · The paper itself

Abstract

backgroundTMPRSS2-ERG (T2E) fusion is highly related to aggressive clinical features in prostate cancer (PC), which guides individual therapy. However, current fusion prediction tools lacked enough accuracy and biomarkers were unable to be applied to individuals across different platforms due to their quantitative nature. This study aims to identify a transcriptome signature to detect the T2E fusion status of PC at the individual level.

methodsBased on 272 high-throughput mRNA expression profiles from the Sboner dataset, we developed a rank-based algorithm to identify a qualitative signature to detect T2E fusion in PC. The signature was validated in 1223 samples from three external datasets (Setlur, Clarissa, and TCGA).

resultsA signature, composed of five mRNAs coupled to ERG (five ERG-mRNA pairs, 5-ERG-mRPs), was developed to distinguish T2E fusion status in PC. 5-ERG-mRPs reached 84.56% accuracy in Sboner dataset, which was verified in Setlur dataset (n = 455, accuracy = 82.20%) and Clarissa dataset (n = 118, accuracy = 81.36%). Besides, for 495 samples from TCGA, two subtypes classified by 5-ERG-mRPs showed a higher level of significance in various T2E fusion features than subtypes obtained through current fusion prediction tools, such as STAR-Fusion.

conclusionsOverall, 5-ERG-mRPs can robustly detect T2E fusion in PC at the individual level, which can be used on any gene measurement platform without specific normalization procedures. Hence, 5-ERG-mRPs may serve as an auxiliary tool for PC patient management.

Indexed as

Prostatic NeoplasmsTranscriptomeHumansMaleOncogene Proteins, FusionRNA, MessengerSerine EndopeptidasesTranscriptional Regulator ERGERG protein, humanOncogene Proteins, FusionRNA, MessengerSerine EndopeptidasesTMPRSS2-ERG fusion protein, humanTMPRSS2 protein, humanTranscriptional Regulator ERG5-ERG-mRPsCross-platformProstate cancerQualitative signatureTMPRSS2-ERG fusion

Identifiers

PMID38331878
PMCPMC10854045
OpenAlexW4391690691

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.