ArticlePLoS computational biology2026
Integrated computational and experimental analysis explores FOLH1 expression patterns across cancers and nominates melatonin as a potential modulator in prostate cancer models.
Article in PLoS computational biology, 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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Abstract
backgroundGrowing evidence indicates that Folate Hydrolase 1 (FOLH1, also known as prostate-specific membrane antigen, PSMA) is aberrantly expressed across multiple malignancies, particularly showing significant upregulation in prostate cancer. However, systematic investigations into its pan-cancer expression patterns, immunomodulatory roles, and immune cell infiltration remain limited. The potential role of FOLH1 in prostate cancer is also not fully elucidated.
methodsWe analyzed FOLH1 mRNA expression, prognostic relevance, and immune infiltration across multiple malignancies, with a particular focus on prostate cancer. A machine learning (ML) workflow incorporating a deep learning model was developed to screen the therapeutic potential of drugs targeting FOLH1. The therapeutic potential of these candidates was validated through in vitro cellular assays and nude mouse xenograft models.
resultsFOLH1 expression was significantly altered in 27 cancer types and showed cancer-specific immune correlations. Our AI platform identified melatonin as a computationally predicted FOLH1-interacting candidate. In vitro and in vivo experiments demonstrated that melatonin suppresses FOLH1 expression in a concentration-dependent manner, inhibits invasive and migratory capacities, and restricts tumor growth under physiological circadian melatonin levels.
conclusionThis study highlights FOLH1's pan-cancer expression patterns and nominates melatonin as an exploratory therapeutic candidate for prostate cancer requiring further mechanistic validation. Our integrated computational-experimental framework highlights the promise of AI-driven drug discovery in oncology, while emphasizing the need for further mechanistic validation.
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