Evidence map›Paper›PMID 41836241›Full record

ArticleFrontiers in oncology2026

A systematic characterization of amino acid metabolism-related genes reveals molecular subtypes and a prognostic signature in bladder cancer.

Junrui He, Xu Liu, Shirui Li, Zhongyou Xia, Xiaojun Tan, Lijuan Peng, Qiongxian Long, Ji Wu

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Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Junrui HeDepartment of Urology, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Xu LiuDepartment of Urology, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Shirui LiDepartment of Urology, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Zhongyou XiaDepartment of Urology, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, China.
Xiaojun TanDepartment of Urology, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, China.
Lijuan PengDepartment of Pathology, Beijing Anzhen Nanchong Hospital, Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, China.
Qiongxian LongDepartment of Pathology, Beijing Anzhen Nanchong Hospital, Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, China.
Ji WuDepartment of Urology, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Amino acid metabolism is integral to tumor proliferation, redox control, and immune regulation. Yet, studies in bladder cancer have largely centered on single amino acids, leaving the broader metabolic gene network insufficiently characterized. Methods: Transcriptomic and clinical data from TCGA-BLCA, GSE13507, and GSE32894 were integrated with 32 MSigDB amino acid metabolism gene sets. Differential analysis, enrichment profiling, and consensus clustering defined metabolic subtypes. WGCNA and survival filtering identified candidates for a prognostic model, which was optimized using the MIME platform. Immune features and drug sensitivities were evaluated through multiple deconvolutions and pharmacogenomic resources. Single-cell data (GSE222315) were used to trace the cellular origin of model genes. Results: A total of 144 dysregulated amino acid metabolism-related genes were identified and used to define two distinct metabolic subtypes. One subtype was marked by coordinated upregulation of glutamine, branched-chain amino acid, tryptophan, and serine metabolic programs, accompanied by higher grade and stage, significantly worse survival, and dense but functionally impaired immune infiltration. From 24 candidate genes, a 16-gene metabolic signature was constructed and consistently validated across TCGA, GSE13507, and GSE32894, showing strong and stable prognostic performance superior to several published models. High-risk group displayed activation of cell-cycle, DNA-replication, Conclusions: This study highlights the central role of amino acid metabolic networks in shaping bladder cancer heterogeneity and provides a metabolically grounded framework for risk stratification and therapeutic development.

Indexed as

amino acid metabolismbladder cancermachine learningmolecular subtypeprognostic signaturePSPH

Identifiers

PMID41836241
PMCPMC12982074

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