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Original Research

Open Access

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

  • Jian Sun1,†
  • Xueqi Zhu2,†
  • Kai Li1
  • Ke Zhang1
  • Fei Wang1,*,

1Department of Urology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, 215000 Suzhou, Jiangsu, China

2Department of ICU, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, 215000 Suzhou, Jiangsu, China

DOI: 10.22514/jomh.2024.052 Vol.20,Issue 4,April 2024 pp.39-53

Submitted: 14 September 2023 Accepted: 05 December 2023

Published: 30 April 2024

*Corresponding Author(s): Fei Wang E-mail: lu99ky@stu.njmu.edu.cn

† These authors contributed equally.

Abstract

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.


Keywords

Prostate cancer; Diagnosis; Machine learning


Cite and Share

Jian Sun,Xueqi Zhu,Kai Li,Ke Zhang,Fei Wang. Application of a high-dimensional gene co-expression network to identify metal ion transport-associated epithelial cells with diagnostic function for prostate cancer. Journal of Men's Health. 2024. 20(4);39-53.

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