Chen Lingwu's Team Uses AI to Discover New Biomarkers and Treatment Strategies for Prostate Cancer

Recently, Professor Lingwu Chen's team from Sun Yat-sen University has made significant strides in prostate cancer research by classifying localized prostate cancer into three distinct protein subtypes, each with notably different prognoses. These subtypes are characterized as immunologically activated (best prognosis), arachidonic acid metabolism, and sialic acid metabolism (worst prognosis). The team leveraged artificial intelligence (AI) to identify key protein markers and developed a robust classification model that can accurately identify high-risk patients in clinical settings. This groundbreaking work has been published in the prestigious international journal Nature Communications. Prostate cancer, being the second most common malignancy among men, presents a significant challenge in treatment, especially during its localized stage. Surgical outcomes vary widely, and existing clinical indicators and genomic classifications often fall short in predicting which patients are at high risk of post-operative biochemical recurrence. Professor Chen explains that, on a mechanistic level, inhibiting NANS (N-Acetylneuraminic Acid Synthase) effectively blocks sialic acid metabolism, reducing the sialylation of prostate cancer cell surfaces. This action disrupts the immune-suppressive microenvironment dominated by M2-type macrophages and enhances CD8+ T-cell infiltration, thereby suppressing tumor growth. The study not only identifies new prognostic markers but also proposes a novel therapeutic approach targeting the sialic acid metabolism–immune checkpoint pathway. This dual discovery holds the potential to improve patient outcomes and personalize treatment strategies, marking a significant advancement in the field of prostate cancer research. For those interested in delving deeper into the findings, the full paper can be accessed through this link: https://www.nature.com/articles/s41467-025-58569-w