BACKGROUND

Prostate cancer (PCa), a highly heterogeneous cancer with a complex molecular pathogenesis, is a leading cause of cancer-related mortality among men globally. The present study presents a lipid metabolism-based risk model for PCa and explores the role of succinyl-CoA ligase GDP-forming subunit beta (SUCLG2), a potential marker and therapeutic target in PCa involved in lipid metabolism and cancer progression, from the perspective of developing effective diagnostic and therapeutic strategies.

METHODS

High-throughput RNA sequencing and single-cell RNA sequencing were used to investigate the expression and functional relevance of SUCLG2 in PCa. We analyzed 497 PCa samples from The Cancer Genome Atlas and conducted a comprehensive bioinformatics analysis, including univariate Cox proportional hazards regression, least absolute shrinkage and selection operator regression, and gene set enrichment analysis. Furthermore, quantitative real-time polymerase chain reaction and immunofluorescence assays were performed to validate SUCLG2 expression in clinical samples and the prostate carcinoma epithelial cell line 22Rv1.

RESULTS

Our findings revealed that lipid metabolism-related genes, including SUCLG2, have significant prognostic value, based on a 16-gene risk model constructed that accurately predicted PCa prognosis. In particular, SUCLG2 was significantly enriched in luminal and basal/intermediate cell subsets, highlighting its potential role in tumor progression and therapy resistance. Drug sensitivity analysis indicated that SUCLG2 expression is correlated with the efficacy of several chemotherapeutic agents, based on which strategies for personalized therapy in PCa treatment could be devised.

CONCLUSION

SUCLG2 plays a pivotal role in the metabolic reprogramming of PCa, thus offering new insights into its progression and potential therapeutic targets. Our study underscores the importance of metabolic pathways in PCa pathogenesis and paves the way for the development of targeted therapies, thus contributing to personalized medicine in PCa management.