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Abstract

Purpose

The purpose of this study is to construct and validate a neuroendocrine differentiation-related molecular model for predicting prognosis in patients with prostate cancer (PCa).

Materials and Methods

Transcriptome data for PCa were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) websites. Differentially expressed neuroendocrine differentiation related genes (NDGs) were identified. By utilizing multivariate Cox analysis, a neuroendocrine differentiation-related molecular model for predicting prognosis was constructed and validated. The study investigated the novel model’s association with the tumor immune microenvironment, clinicopathological characteristics, tumor stemness, and anticancer treatment sensitivity. Additionally, preliminary experimental verifications of Diencephalon / Mesencephalon Homeobox 1 (DMBX1) were conducted.

Results

Finally, we identified a total of 19 differentially expressed NDGs. A neuroendocrine differentiation-related molecular model was established and successfully validated both internally and externally. The high-risk group   exhibited   significantly   poorer biochemical recurrence-free survival (BCRFS) in the training, testing, and validating cohorts. The areas under the receiver operating characteristic curves for the training, testing, and validating cohorts were 0.825, 0.719, and 0.729, respectively. The tumor immune microenvironment, clinicopathological features, tumor stemness, and anti-cancer drug sensitivity was significantly different between high and low-risk patients. Preliminary experiments revealed that higher expression of DMBX1 significantly enhanced the proliferation, migration, and neuroendocrine differentiation of PCa cells.

Conclusion

This research developed a unique neuroendocrine differentiation-related molecular model that is highly suitable for predicting BCRFS. High DMBX1 expression may promote the development and neuroendocrine differentiation of prostate cancer.

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2025-05-15
2025-09-12

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A Neuroendocrine Differentiation-related Molecular Model for Prognosis Prediction in Prostate Cancer Patients
Bentham Science Publishers ; https://doi.org/10.2174/0109298673362568250505074520
/content/journals/cmc/10.2174/0109298673362568250505074520
/content/journals/cmc/10.2174/0109298673362568250505074520
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  • Article Type:     Research Article
Keywords: molecular model ; neuroendocrine differentiation ; Prostate cancer ; prognosis ; tumor stemness ; multivariate

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