Optimized graph neural network-multilayer perceptron fusion classifier for metastatic prostate cancer detection in Western and Asian populations.

Objective: Prostate cancer (PCa) exhibits significant genomic differences between Western and Asian populations. This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation. Methods: We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model. External validation was conducted using an independent cohort of patients. A graph neural network architecture, termed the pathway-aware multi-layered hierarchical network-Western and Asian (P-NETwa), was developed and trained on combined genomic profiles from Chinese and Western cohorts. The model employed a multilayer perceptron (MLP) to identify key signature genes from multi-omics data, enabling precise prediction of PCa metastasis. Results: The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets. The application of integrated Chinese and Western population data improved the accuracy to 0.88, achieving an F1-score of 0.75. The analysis identified 18 signature genes implicated in PCa progression, including established markers ( Conclusion: The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID, enhancing its potential for seamless integration into clinical workflows.