Background: Prostate cancer (PCa) is a prevalent malignancy among elderly men. Biochemical recurrence (BCR), which typically occurs after radical treatments such as radical prostatectomy or radiation therapy, serves as a critical indicator of potential disease progression. However, reliable and effective methods for predicting BCR in PCa patients remain limited. Methods: In this study, we used Bayesian deconvolution combined with 10 machine learning algorithms to build a five-gene model for predicting PCa progression. The model and the five selected genes were externally validated. Various analyses such as prognosis, clinical subgroups, tumor microenvironment, immunity, genetic variants, and drug sensitivity were performed on MSMB/Epithelial_cells subgroups. Results: Our model outperformed 102 previously published prognostic features. Notably, PCa patients with a high proportion of MSMB/epithelial cells were characterized by a greater progression-free Interval (PFI), a higher proportion of early-stage tumors, a lower stromal component, and a reduced presence of tumor-associated fibroblasts (CAF). The high proportion of MSMB/epithelial cells was also associated with higher frequencies of SPOP and TP53 mutations. Drug sensitivity analysis revealed that patients with a poorer prognosis and lower MSMB/epithelial cell ratio showed increased sensitivity to cyclophosphamide, cisplatin, and dasatinib. Conclusions: The model developed in this study provides a robust and accurate tool for predicting PCa progression. It offers significant potential for enhancing risk stratification and informing personalized treatment strategies for PCa patients.

Aims: To investigate the effect of Glutathione metabolism in prostate cancer pathogenesis. Background: There is growing evidence that Glutathione metabolism plays an important role in prostate cancer, with genes encoding key enzymes in this pathway potentially serving as diagnostic or prognostic biomarkers. Objective: To explore whether there is a causal relationship between key enzymes in the Glutathione metabolism and prostate cancer, and to further investigate the molecular mechanisms and roles of the genes encoding their proteins in relation to prostate cancer. Method: Transcriptomic datasets from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed genes (DEGs) and enriched pathways in prostate cancer versus normal tissues. Two-sample bidirectional Mendelian randomization (MR) was employed to assess causal relationships between Glutathione metabolic enzymes (exposure) and prostate cancer risk (outcome). Immune infiltration analysis and LASSO regression were performed to construct a diagnostic model. Single-cell RNA sequencing (scRNA-seq) data were utilized to elucidate cell-type-specific expression patterns and functional associations of target genes. Result: The results of two-sample bidirectional MR showed that Glutathione S-transferase P (GSTP) in Glutathione metabolism could reduce the risk of prostate cancer. The Glutathione S-transferase Pi-1 (GSTP1) gene was lowly expressed in prostate cancer and was able to diagnose prostate cancer more accurately. Single-cell analysis showed that the high expression of GSTP1 in prostate cancer epithelial cells was closely associated with the upregulation of the P53 pathway and apoptosis. Conclusions: Our study reveals that GSTP in Glutathione metabolism reduces the risk of prostate cancer and further analyzes the genetic association and mechanism of action between GSTP1 and prostate cancer.

Background: Prostate cancer, as an androgen-dependent malignant tumor in older men, has attracted the attention of a wide range of clinicians. BCR remains a significant challenge following early prostate cancer treatment. Methods: The specific expression pattern of the Anoikis gene set in prostate cancer cells was first explored by single-cell and spatial transcriptomics analysis. Genes causally associated with prostate cancer were screened using Summary-data-based Mendelian Randomization (SMR). Subsequently, we explored the role and mechanism of CASP8 in prostate cancer cells and defined a new cell type: the CASP8 T cell. We constructed a prediction model that can better predict the BCR of prostate cancer, and explored the differences in various aspects of clinical subgroups, tumor microenvironments, immune checkpoints, drug sensitivities, and tumor-immune circulations between high- and low-risk groups. The results of SMR analysis indicated that CASP8 could increase the risk of prostate cancer. Based on the differential genes of CASP8-positive and -negative T cells, we constructed a four-gene prognostic model with a 5-year AUC of 0.713. Results: The results revealed that high-risk prostate cancer BCR patients had various characteristics such as higher tumor purity, higher BCR rate, downregulated SIRPA immune checkpoints, and unique drug sensitivity. Conclusions: In summary, CASP8 may be a potential biomarker for prostate cancer.