Application of a high-dimensional gene co-expression network to identify metal ion transport-associated epithelial cells with diagnostic function for prostate cancer

Prostate cancer is a prevalent malignancy and leading cause of male mortality worldwide, thus highlighting the need for precision medicine and a combined single-cell and bulk transcriptome-based diagnostic assessment model. First, we used single-cell RNA sequencing data and the high dimensional weighted gene co-expression network analysis (hdWGCNA) method to identify specific cell types in tumor tissues. We identified higher proportions of epithelial cells and increased intra-tissue heterogeneity in prostate cancer; most of these epithelial cells were closely associated with metal ion transport functions. Lasso regression identified diagnostic-related genes that were incorporated into a sample evaluation model using multiple machine learning algorithms. Thus, we established a diagnostic model that combined immune cells, diagnostic genes and deep learning methods. The Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) models exhibited the highest diagnostic efficacy for Lasso genes. Neural network models, combining immune microenvironment assessment and diagnostic gene expression, also showed good diagnostic efficacy. Our study highlights the potential of machine learning and convolutional neural networks for the diagnostic assessment of prostate cancer, thus providing support for personalized treatment decisions, and identifying areas for future research.

Prostate cancer; Diagnosis; Machine learning

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