Cao Bin, Chen Huijun, Zhang Luting, Xiao Fang, Liu Qiaoting, Tang Lizhen, You Tao, Ouyang Qiufang
Ultrasound Department, The Second Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Urology Department, The Second Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Transl Androl Urol. 2025 Aug 30;14(8):2218-2234. doi: 10.21037/tau-2025-179. Epub 2025 Aug 26.
Lysine acetylation plays a critical role in prostate cancer (PCa) by modulating androgen receptor (AR) signaling. However, the exact mechanisms by which lysine acetylation impacts PCa prognosis remain unclear. The aim of this study was to investigate the mechanism by which lysine acetylation affects PCa prognosis by modulating the AR signaling pathway.
Data from The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD), GSE54460, and lysine acetylation-related genes (LARGs) were obtained from public databases and literature. Differentially expressed genes (DEGs) were identified in TCGA-PRAD, and key module genes associated with LARGs were selected using weighted gene co-expression network analysis (WGCNA). Candidate genes were identified by overlapping DEGs and key module genes. A biochemical recurrence-free (BCR-free) prognostic model was constructed and validated using BCR-free survival data from patients with PCa. Prognostic genes were further confirmed through machine learning. PCa samples were stratified into high- and low-risk subgroups based on the median risk score. A nomogram model was developed integrating clinicopathological features and risk scores to identify independent prognostic factors. Enrichment analysis, tumor microenvironment profiling, and drug sensitivity assessments were performed for the two risk subgroups.
A total of 2,658 DEGs and 723 key module genes were analyzed, yielding 105 overlapping candidate genes. Five genes-, , , , and -were identified as BCR-free-related prognostic markers in TCGA-PRAD. The prognostic risk model revealed significantly lower BCR-free survival rates in the high-risk subgroup compared to the low-risk subgroup. A nomogram incorporating Gleason score, tumor stage (T stage), and risk score effectively predicted BCR-free survival in patients with PCa. Notably, natural killer (NK) cells, myeloid dendritic cells, endothelial cells, and fibroblasts were significantly correlated with PLS3 (|Cor| >0.3, P<0.05). Drugs such as cisplatin, MK-1775, and ulixertinib were identified as potential therapeutic agents for PCa.
Five BCR-free-related prognostic genes were identified as potential therapeutic targets. Additionally, a BCR-free-related prognostic risk model was developed, offering a robust tool for predicting BCR-free survival in patients with PCa.
赖氨酸乙酰化通过调节雄激素受体（AR）信号通路在前列腺癌（PCa）中发挥关键作用。然而，赖氨酸乙酰化影响PCa预后的确切机制仍不清楚。本研究的目的是探讨赖氨酸乙酰化通过调节AR信号通路影响PCa预后的机制。
从公共数据库和文献中获取来自癌症基因组图谱-前列腺腺癌（TCGA-PRAD）、GSE54460的数据以及赖氨酸乙酰化相关基因（LARGs）。在TCGA-PRAD中鉴定差异表达基因（DEGs），并使用加权基因共表达网络分析（WGCNA）选择与LARGs相关的关键模块基因。通过重叠DEGs和关键模块基因鉴定候选基因。使用PCa患者的无生化复发（BCR-free）生存数据构建并验证无BCR的预后模型。通过机器学习进一步确认预后基因。根据中位风险评分将PCa样本分为高风险和低风险亚组。开发了一个整合临床病理特征和风险评分的列线图模型，以识别独立的预后因素。对两个风险亚组进行富集分析、肿瘤微环境分析和药物敏感性评估。
共分析了2658个DEGs和723个关键模块基因，产生了105个重叠的候选基因。在TCGA-PRAD中，五个基因——、、、和——被确定为与无BCR相关的预后标志物。预后风险模型显示，高风险亚组的无BCR生存率明显低于低风险亚组。一个结合Gleason评分、肿瘤分期（T分期）和风险评分的列线图有效地预测了PCa患者的无BCR生存。值得注意的是，自然杀伤（NK）细胞、髓样树突状细胞、内皮细胞和成纤维细胞与PLS3显著相关（|Cor|>0.3，P<0.05）。顺铂、MK-1775和ulixertinib等药物被确定为PCa的潜在治疗药物。
五个与无BCR相关的预后基因被确定为潜在的治疗靶点。此外，还开发了一个与无BCR相关的预后风险模型，为预测PCa患者的无BCR生存提供了一个强大的工具。
