Evidence map›Paper›PMID 38880858›Full record

ArticleEJNMMI research2024

Evaluation of gastrin-releasing peptide receptor, prostate-specific membrane antigen, and neurotensin receptor 1 as potential biomarkers for accurate prostate cancer stratified diagnosis.

Ling Xiao, Zhihui Fang, Yongxiang Tang, Yanyan Sun, Zehua Zhu, Jian Li, Ming Zhou, Nengan Yang, Kai Zheng, Shuo Hu

Abstract read
In one paragraph

Article in EJNMMI research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Dual-tracer PET/CT with [European journal of nuclear medicine and molecular imaging · 2026
    Trial
  2. Article
  3. Article
  4. Review
  5. The additive value ofEuropean journal of nuclear medicine and molecular imaging · 2025
    Article
  6. Article
  7. 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

10 authors.

Ling Xiao *Department of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, 410008, Hunan Province, P.R. China.
Zhihui Fang *Department of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, 410008, Hunan Province, P.R. China.
Yongxiang TangDepartment of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, 410008, Hunan Province, P.R. China.
Yanyan SunDepartment of Hematology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450000, China.
Zehua ZhuDepartment of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, 410008, Hunan Province, P.R. China.
Jian LiDepartment of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, 410008, Hunan Province, P.R. China.
Ming ZhouDepartment of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, 410008, Hunan Province, P.R. China.
Nengan YangDepartment of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, 410008, Hunan Province, P.R. China.
Kai ZhengDepartment of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, 410008, Hunan Province, P.R. China.
Shuo HuDepartment of Nuclear Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, 410008, Hunan Province, P.R. China. hushuo2018@163.com.ORCID http://orcid.org/0000-0003-0998-8943

Funding

Clinical research foundation of the National Clinical Research Center for Geriatric Diseases (XIANGYA) 2020LNJJ01Medical New Technology Innovation Guidance Program of Hunan Province 2020SK53704National Natural Science Foundation of China 81771873National Natural Science Foundation of China 91859207Science and Technology Innovation Program of Hunan Province 2021 RC4056
6 · The paper itself

Abstract

backgroundStudies on single-target PET imaging of gastrin-releasing peptide receptor (GRPR), prostate-specific membrane antigen (PSMA), or neurotensin receptor 1(NTR1) have been reported. However, the performance of these three targets in the progression of PCa remains unclear. Our study aims to compare the expression of GRPR, PSMA, and NTR1 in patients with prostatic intraepithelial neoplasia (PIN), prostate cancer (PCa), and lymph node metastasis. We synthesized molecular probes targeting the markers to achieve a non-invasive precise detection of PCa patients with PET/CT imaging.

methodsIn this study, the expression of GRPR, PSMA, and NTR1 was evaluated by immunohistochemistry in 34 PIN, 171 PCa, and 22 lymph node metastasis tissues of patients. The correlation between their expression and the clinicopathological parameters of PCa patients was assessed. Sixteen PCa patients with different Gleason scores (GS) underwent dual-tracer (

resultsIn the PIN stage, the expression of GRPR was significantly higher than that of PSMA and NTR1 (P < 0.001), while NTR1 expression was significantly higher than PSMA and GRPR expression in primary PCa (P = 0.001). High PSMA expression in PCa patients was associated with shorter progression-free survival (P = 0.037) and overall survival (P = 0.035). PCa patients with high GS had higher tumor uptake of

conclusionsThis study presents three novel biomarkers (PSMA, GRPR, and NTR1) as imaging agents for PET/CT, and may offer a promising approach for non-invasive precise detection and Gleason grade prediction of PCa patients.

Indexed as

DiagnosisPositron-Emission tomographyPrognosisProstate cancer

Identifiers

PMID38880858
PMCPMC11180645

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.