Evidence map›Paper›PMID 40815405›Full record

ArticleDiscover oncology2025

A nomogram integrating mutation signatures and clinical features for prognostic stratification in bladder cancer.

Lili Wang, Peng Chen, Huanhuan Liu, Jiayue Qin, Shanbo Cao, Hongzheng Li, Dingkun Hou, Kaibin Wang, Haitao Wang

Abstract read
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

9 authors.

Lili WangDepartment of Pulmonary Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Reseach Center for Cancer, Tianjin, 300060, China.
Peng ChenDepartment of Pulmonary Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Reseach Center for Cancer, Tianjin, 300060, China.
Huanhuan LiuDepartment of Medicine, Acornmed Biotechnology Co., Ltd., Beijing, 100176, China.
Jiayue QinDepartment of Medicine, Acornmed Biotechnology Co., Ltd., Beijing, 100176, China.
Shanbo CaoDepartment of Medicine, Acornmed Biotechnology Co., Ltd., Beijing, 100176, China.
Hongzheng LiDepartment of Oncology, Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Precision Medicine for Sex Hormones and Disease (in Preparation), Tianjin, 300211, China.
Dingkun HouDepartment of Oncology, Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Precision Medicine for Sex Hormones and Disease (in Preparation), Tianjin, 300211, China.
Kaibin WangDepartment of Oncology, Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Precision Medicine for Sex Hormones and Disease (in Preparation), Tianjin, 300211, China.
Haitao WangDepartment of Oncology, Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Precision Medicine for Sex Hormones and Disease (in Preparation), Tianjin, 300211, China. peterrock2000@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeBladder cancer (BLCA) is a typical malignancy in the urinary tract, with a dismal survival rate and limited therapeutic options. There is a growing demand for clinically relevant biomarkers to effectively stratify prognosis. This study aims to develop a prognostic model utilizing mutation signatures and clinical characteristics to inform the clinical management of BLCA.

methodsThe data of 631 BLCA patients were retrospectively reviewed, including 538 patients from The Cancer Genome Atlas (TCGA) and 93 patients from a Chinese cohort. Univariate and multivariate analyses were performed to identify the independent prognostic factors for overall survival (OS).

resultsMultivariate Cox regression analysis revealed that a 30-mutated gene signature and age were independent prognostic factors for BLCA. The prognostic nomogram was constructed to predict the probability of 1-, 3-, and 5-year OS, achieving area under the curve (AUC) values of 0.800, 0.749, and 0.731 in the training group, and 0.641, 0.820, and 0.759 in the validation group, respectively. Additionally, high risk scores were associated with poorer outcomes across all clinical subgroups. Patients with high-risk profiles exhibited higher neoantigen burden (p = 0.029), copy number variation (CNV) count (p = 0.013), and DNA damage response (DDR) mutations (p < 0.001) compared to those with low-risk profiles.

conclusionsThe model incorporating mutation signatures and clinical factors demonstrated accuracy in predicting changing cancer survival risk over time.This suggests that the model has the potential to serve as a valuable prognostic tool for BLCA.

Indexed as

Bladder cancerMutation signaturePrognosisPrognostic model

Identifiers

PMID40815405
PMCPMC12356800

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