Evidence map›Paper›PMID 40251526›Full record

ArticleClinical proteomics2025

Circulatory prostate cancer proteome landscapes and prognostic biomarkers in metastatic castrate resistant prostate cancer.

Hyejung Lee, Jincheng Shen, Muhammad Zaki Fadlullah, Anna Neibling, Claire Hanson, Enos Ampaw, Tengda Lin, Matt Larsen, Jennifer Lloyd, Benjamin L Maughan and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical proteomics, 2025. 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. 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

21 authors.

Hyejung LeeDepartment of Population Health Sciences, Division of Biostatistics, University of Utah, Salt Lake City, UT, USA.
Jincheng ShenDepartment of Population Health Sciences, Division of Biostatistics, University of Utah, Salt Lake City, UT, USA.
Muhammad Zaki FadlullahDepartments of Oncological Sciences and Biomedical Informatics, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Anna NeiblingPopulation Sciences Study Support, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Claire HansonDivision of Oncology, Department of Internal Medicine, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Enos AmpawDivision of Oncology, Department of Internal Medicine, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Tengda LinDepartment of Population Health Sciences, Division of Biostatistics, University of Utah, Salt Lake City, UT, USA.
Matt LarsenDivision of Oncology, Department of Internal Medicine, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Jennifer LloydDivision of Oncology, Department of Internal Medicine, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Benjamin L MaughanDivision of Oncology, Department of Internal Medicine, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Umang SwamiDivision of Oncology, Department of Internal Medicine, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Sumati GuptaDivision of Oncology, Department of Internal Medicine, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Jonathan TwardDepartment of Radiation Oncology, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Skyler B JohnsonDepartment of Radiation Oncology, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Brock O'NeilDepartment of Surgery, Division of Urology, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Bogdana SchmidtDepartment of Surgery, Division of Urology, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Christopher B DechetDepartment of Surgery, Division of Urology, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Benjamin HaalandDepartment of Population Health Sciences, Division of Biostatistics, University of Utah, Salt Lake City, UT, USA.
Liang WangDepartment of Tumor Microenvironment and Metastasis, Moffitt Cancer Center, Tampa, FL, USA.
Aik-Choon TanDepartments of Oncological Sciences and Biomedical Informatics, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA.
Manish KohliDivision of Oncology, Department of Internal Medicine, University of Utah-Huntsman Cancer Institute, Salt Lake City, UT, USA. Manish.kohli@hci.utah.edu.

Funding

CTSA UM1 Program at University of UtahUM1TR004409 · NCATS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI RACHEL HESS, Jennifer Juhl Majersik · 2023 to 2026
$21.9M
Cell free nucleic acid-based biomarkers in advanced prostate cancerR01CA212097 · NCI · MEDICAL COLLEGE OF WISCONSIN · PI KOHLI, MANISH, WANG, LIANG · 2017 to 2022
$3.1M
NCATS NIH HHS UM1 TR004409NCI NIH HHS R01 CA212097
6 · The paper itself

Abstract

backgroundPlasma-based high-plex proteomic profiling were performed in prostate cancer (PC) patients using the Olink® Explore Proximity Extension Assay to identify plasma proteins associated in different PC states and to explore potential prognostic biomarkers. The progressive PC states include local, organ-confined PC (local PC), metastatic hormone-sensitive PC (mHSPC) and metastatic castrate-resistant PC (mCRPC).

methodsPlasma samples were uniformly processed from 84 PC patients (10 patients with local PC; 29 patients with mHSPC; 45 patients with mCRPC). A proteome-wide association study was performed to identify proteins differentially overexpressed in progressive cancer states. Specifically, a sequential screening approach was employed where proteins overexpressed from one disease state were assessed for overexpression in the progressive disease state. Linear regression, analysis of variance, and t-tests were used for this approach. Differentially expressed proteins (DEPs) in mCRPC were then used to construct a prognostic model for overall survival (OS) in mCRPC patients using the Cox Proportional Hazard Model. The predictive performance of this model was assessed using time-dependent area under the receiver operating characteristic curves (tAUC) in an independent sample of mCRPC patients. The tAUC of the prognostic model was then compared to that of a model excluding DEPs to evaluate the added value of circulatory proteins in predicting survival.

resultsOf 736 tumor-associated proteins, 26 were differentially expressed across local PC, mHSPC, and mCRPC states. Among these, 20 were overexpressed in metastatic states compared to local, and in mCRPC compared to mHSPC states. Of these 20 proteins, Ribonucleoside-diphosphate reductase subunit M2 (RRM2) was identified as a prognostic biomarker for OS in mCRPC, with a hazard ratio of 2.30 (95% confidence interval (CI) 1.17-4.51) per normalized expression unit increase. The tAUC of the model including previously identified clinical prognostic factors was 0.62 (95% CI 0.29-0.91), whereas the model that includes RRM2 with clinical prognostic factors was 0.87 (95% CI 0.51-0.98).

conclusionsPlasma proteome profiling can identify novel circulatory DEPs associated with mCRPC state survivals. Overexpression of RRM2 is linked to poor mCRPC survival and its inclusion alongside conventional prognostic factors enhances the predictive performance of the prognostic model.

Indexed as

LandscapePrognostic modelProstate cancerProteomes

Identifiers

PMID40251526
PMCPMC12008844

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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.