Wang Qinghua, Qin Xin, Zhao Yan, Jiang Wei, Xu Mingming, Li Xilei, Li Haopeng, Zhou Juan, Wu Gang
Department of Urology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of ICU, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
J Cell Mol Med. 2025 Jun;29(12):e70669. doi: 10.1111/jcmm.70669.
Lactylation plays a pivotal role in the metabolic reprogramming, proliferation, migration and immune evasion of tumour cells. However, its specific impact on prostate cancer (PCa) remains poorly understood. This study aimed to investigate the role of lactylation related genes (LRGs) in PCa. LRGs were identified and analysed using data from The Cancer Genome Atlas (TCGA), DKFZ2018, GSE46602 and GSE70768 cohorts. Unsupervised clustering was employed to categorise patients with PCa into two distinct clusters. Prognostic models for PCa were developed using multiple machine learning techniques. LRGs signature was established and validated through training and validation sets. The role of leukocyte immunoglobulin-like receptor B4 (LILRB4) in PCa was examined both in vitro and in vivo. Analysis of LRG expression and prognosis in patients with PCa revealed two distinct clusters with differing survival rates and immune responses. Machine learning models demonstrated the ability to predict survival risks, potentially aiding in the development of personalised treatment strategies. Additionally, LILRB4, a key LRG, promotes PCa progression by modulating the NF-κB and PI3K/AKT pathways, highlighting its potential as a therapeutic target. LRGs exert a pivotal influence on PCa, impacting patient prognosis, immune response and drug sensitivity. The LRGs signature emerges as an essential prognostic tool and a promising therapeutic target for PCa.
乳酰化在肿瘤细胞的代谢重编程、增殖、迁移和免疫逃逸中起关键作用。然而，其对前列腺癌（PCa）的具体影响仍知之甚少。本研究旨在探讨乳酰化相关基因（LRGs）在PCa中的作用。利用来自癌症基因组图谱（TCGA）、DKFZ2018、GSE46602和GSE70768队列的数据对LRGs进行识别和分析。采用无监督聚类将PCa患者分为两个不同的簇。使用多种机器学习技术建立PCa的预后模型。通过训练集和验证集建立并验证LRGs特征。在体外和体内研究了白细胞免疫球蛋白样受体B4（LILRB4）在PCa中的作用。对PCa患者LRG表达和预后的分析揭示了两个不同的簇，其生存率和免疫反应不同。机器学习模型显示出预测生存风险的能力，可能有助于制定个性化治疗策略。此外，关键的LRG LILRB4通过调节NF-κB和PI3K/AKT途径促进PCa进展，突出了其作为治疗靶点的潜力。LRGs对PCa产生关键影响，影响患者预后、免疫反应和药物敏感性。LRGs特征成为PCa重要的预后工具和有前景的治疗靶点。
