Prostate cancer (PCa) ranks as the second leading cause of cancer-related death in men. The main cause of mortality in patients (pts) with advanced PCa is metastasis. Bringing insights into novel and effective biomarkers is needed for understanding metastasis driving mechanisms in patients advanced PCa and for managing to design successful treatment strategies.

YaXuan Wang and the team designed this study to identify metastasis-related genes in PCa using data from GSE8511 and GSE27616 through weighted gene co-expression network analysis (WGCNA).

Researchers employed 2 datasets, GSE8511 and GSE27616, to identify 21 metastasis-related genes using the WGCNA method. Functional analysis of these genes was performed on the Gene Set Cancer Analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC₅₀ scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy.

Further multivariate COX regression analysis advocated the significant role of POLD1 and established its association with DNA methylation. Molecular docking and immunohistochemistry experiments were performed to obtain the binding affinity of POLD1 to PCa drugs and reveal its impact on PCa prognosis.

The study identified 21 metastasis-related genes using the WGCNA method, which were associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms demonstrated strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results.

POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients.

The study concluded with the development of diagnostic and metastasis models that provide potential value for patients with PCa. The discovery of POLD1 as a novel biomarker related to PCa metastasis offers a promising avenue for enhancing the treatment of prostate cancer metastasis.

This study was supported by the Natural Science Foundation of Heilongjiang Province (LH2019H030) and National Natural Science Foundation of China (82002680).

Source: https://pubmed.ncbi.nlm.nih.gov/38918844/

Wang Y., Ji B., Zhang L., et al. (2024). “Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches.” Biol Direct. 2024 Jun 25;19(1):50. doi: 10.1186/s13062-024-00494-x. PMID: 38918844