Evidence map›Paper›PMID 40226044›Full record

ArticleJournal of bone oncology2025

A novel bone metastasis-related gene signature for predicting prognosis, anti-androgen resistance, and drug choice in prostate cancer.

Yu Luo, Xiaoqi Deng, Chengcheng Wei, Zhangcheng Liu, Liangdong Song, Kun Han, Yunfan Li, Jindong Zhang, Shuai Su, Delin Wang

Abstract read
In one paragraph

Article in Journal of bone oncology, 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. 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.

Yu LuoDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Xiaoqi DengDepartment of Nephrology, Zigong Fourth People's Hospital, Zigong, Sichuan Province 643000, China.
Chengcheng WeiDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Zhangcheng LiuDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Liangdong SongDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Kun HanDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Yunfan LiDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Jindong ZhangDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Shuai SuDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Delin WangDepartment of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Prostate cancer (PCa) often metastasizes to the bone, posing a significant clinical challenge. This study aims to develop a bone metastasis-related risk model for PCa. Methods: Bone metastasis-related genes (BMRGs) were identified through a combination of differential gene expression analysis and WGCNA using GSE32269 and GSE77930 datasets. Consensus clustering analysis was employed to determine the significance of these genes in molecular subtyping of PCa. LASSO-Cox regression analysis was utilized to construct the bone metastasis-related prognostic gene signature (BMRPS). The predictive performance of BMRPS was assessed using ROC curves, Kaplan-Meier survival curves, and a predictive nomogram. The immune landscape heterogeneity of subgroups was analyzed using CIBERSORT, ESTIMATE, and xCell algorithms. Drug sensitivity and molecular docking analysis were performed to identify drugs associated with BMRPS. Results: Forty-four BMRGs associated with the prognosis of PCa were identified. Consensus clustering revealed the pivotal role of these genes in stratifying PCa into three distinct prognostic clusters. The BMRPS, consisting of 14 BMRGs, demonstrated excellent predictive accuracy for prognosis and served as an independent prognostic factor in PCa. BMRPS effectively predicted the overall survival of bone metastatic PCa and differentiated bone metastasis from other metastatic types. BMRPS showed a close correlation with the immune landscape and immunotherapeutic response biomarkers. Additionally, BMRPS was associated with anti-androgen resistance, and AZD8186 was identified as a potential BMRPS-related drug that holds promise for personalized treatment in PCa. Conclusion: BMRPS facilitates the prediction of prognosis and resistance to anti-androgens in PCa. It also offers insights into the molecular mechanisms of bone metastasis and aids in drug selection for the treatment of PCa.

Indexed as

Anti-androgen resistanceBone metastasisPersonalized medicinePrognostic gene signatureProstate cancer

Identifiers

PMID40226044
PMCPMC11986555

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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.