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Abstract Background Previous studies have unequivocally demonstrated that the vitamin D (VD) metabolism pathway significantly influences prognosis and sensitivity to hormone therapy in prostate cancer (PCa). However, the precise underlying mechanism remains unclear. Methods We performed molecular profiling of 1045 PCa patients, leveraging genes linked to VD synthesis and VD receptors. We then identified highly variable gene modules with substantial associations with patient stratification. Subsequently, we intersected these modules with differentially expressed genes between PCa and adjacent paracancerous tissues. Following a meticulous process involving single-factor regression and LASSO regression to eliminate extraneous variables and construct a prognostic model. Within the high-risk subgroup defined by the calculated risk score, we analyzed their differences in cell infiltration, immune status, mutation landscape, and drug sensitivity. Finally, we selected Apolipoprotein E (APOE), which featured prominently in this model for further experimental exploration to evaluate its potential as a therapeutic target. Results The prognostic model established in this study had commendable predictive efficacy. We observed diminished infiltration of various T-cell subtypes and reduced expression of co-stimulatory signals from antigen-presenting cells. Mutation analysis revealed that the high-risk cohort harbored a higher frequency of mutations in the TP53 and FOXA genes. Notably, drug sensitivity analysis suggested the heightened responsiveness of high-risk patients to molecular inhibitors targeting the Bcl-2 and MAPK pathways. Finally, our investigation also confirmed that APOE upregulates the proliferative and invasive capacity of PCa cells and concurrently enhances resistance to androgen receptor antagonist therapy. Conclusion This comprehensive study elucidated the potential mechanisms through which this metabolic pathway orchestrates the biological behavior of PCa and findings hold promise in advancing the development of combination therapies in PCa.

Summary: Sunitinib, a primary treatment for clear cell renal cell carcinoma (ccRCC), frequently encounters the challenge of resistance development. Metabolic reprogramming, a characteristic change in ccRCC, is likely linked to this resistance. Our research revealed a notable decrease in the expression of the key metabolic gene ABAT in ccRCC, which contributed to diminished sensitivity to sunitinib. Downregulation of ABAT led to an increase in the intracellular level of gamma-aminobutyric acid (GABA), triggering abnormal activation of the G-protein-coupled receptor GABA-B. This activation resulted in increased transactivation of the tyrosine kinase receptors SYK and LYN, thereby reducing the antitumor and antiangiogenic properties of sunitinib. However, the application of SYK and LYN inhibitors successfully inhibited this effect. The transactivation of SYK and LYN caused resistance to the antiangiogenic effects of sunitinib through the upregulation of PGF protein levels. Furthermore, the combined application of an LYN inhibitor with sunitinib has been shown to enhance therapeutic efficacy.

Metabolic reprogramming is one of the characteristics of clear cell renal cell carcinoma (ccRCC). Although some treatments associated with the metabolic reprogramming for ccRCC have been identified, remain still lacking. In this study, we identified the differentially expressed genes (DEGs) associated with clinical traits with a total of 965 samples via DEG analysis and weighted correlation network analysis (WGCNA), screened the prognostic metabolism-related genes, and constructed the risk score prognostic models. We took the intersection of DEGs with significant difference coexpression modules and received two groups of intersection genes that were connected with metabolism via functional enrichment analysis. Then we respectively screened prognostic metabolic-related genes from the genes of the two intersection groups and constructed the risk score prognostic models. Compared with the predicted effect of clinical grade and stage for ccRCC patients, finally, we selected the model constructed with genes of ABAT, ALDH6A1, CHDH, EPHX2, ETNK2, and FBP1. The risk scores of the prognostic model were significantly related to overall survival (OS) and could serve as an independent prognostic factor. The Kaplan-Meier analysis and ROC curves revealed that the model efficiently predicts prognosis in the TCGA-KIRC cohort and the validation cohort. Then we investigated the potential underlying mechanism and sensitive drugs between high- and low-risk groups. The six key genes were significantly linked with worse OS and were downregulated in ccRCC, we confirmed the results in clinical samples. These results demonstrated the efficacy and robustness of the risk score prognostic model, based on the characteristics of metabolic reprogramming in ccRCC, and the key genes used in constructing the model also could develop into targets of molecular therapy for ccRCC.