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Integrative Machine Learning Framework Revealing TRPM4-Associated Signatures and Identifying SPATA6 as a Potential

Hang Zhou¹, Wangli Mei¹, Jichen Wang²

¹Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.

Journal of Cancer

|May 25, 2026

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This summary is machine-generated.

The transient receptor potential melastatin 4 (TRPM4) channel

Area of Science:

Oncology
Molecular Biology
Bioinformatics

Background:

The non-selective cation channel TRPM4 is implicated in necrotic cell death via sodium overload.
Its specific role in prostate cancer (PCa) progression is not well understood.

Purpose of the Study:

To investigate the role of TRPM4 in prostate cancer progression.
To develop a prognostic model for PCa based on TRPM4-associated molecular signatures.

Main Methods:

Utilized TCGA-PCa transcriptomic data to identify TRPM4-associated signatures.
Developed and validated prognostic models using machine learning algorithms.
Assessed model associations with clinicopathological features, prognosis, immune infiltration, and drug response.
Validated gene expression in PCa cell lines and performed functional assays for SPATA6.

Keywords:

Machine Learning Prostate cancer SPATA6 TRPM4-realted signatures model sodium overload

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Main Results:

Identified 91 overlapping genes linked to TRPM4 and PCa, enriched in small GTPase and Rap1 signaling pathways.
Developed a 10-gene TRPM4-related signature model (TRSM) with strong prognostic performance.
TRSM risk stratification correlated with disease-free survival, immune infiltration, and immunotherapy response.
High-risk patients showed increased docetaxel sensitivity. SPATA6 overexpression inhibited PCa cell growth and migration.

Conclusions:

TRPM4-associated molecular features are crucial for PCa prognosis.
The TRSM model shows potential for predicting patient outcomes and guiding personalized therapy.