Dai Haojie, Zhao Kai, Zhao You, Jiang Ke, Hang Zhenyu, Huang Xin, Luo Weiping, Nie Jun, Qin Chao, Zhou Weiwen
Liyang Branch of the First Affiliated Hospital of Nanjing Medical University, The Affiliated Liyang People's Hospital of Kangda College of Nanjing Medical University, Changzhou, Jiangsu, China.
The First Clinical Medical College, Nanjing Medical University, Nanjing, Jiangsu, China.
BMC Urol. 2025 Sep 15;25(1):229. doi: 10.1186/s12894-025-01914-4.
Crotonylation, a post-translational modification, is implicated in cancer progression, but its prognostic significance in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to demystify crotonylation heterogeneity and establish a robust prognostic model for ccRCC.
Using multi-omics approaches, we analyzed transcriptomic data from TCGA-KIRC and GEO cohorts (GSE40435, GSE167573, GSE29609). Crotonylation scores were calculated via ssGSEA, with related gene modules identified through WGCNA. We integrated 10 machine learning algorithms to develop a prognostic model. Immune microenvironment was profiled using Cibersort, mutation landscapes via maftools, and drug sensitivity through oncoPredict. Spatial transcriptomics and single-cell data were analyzed for expression patterns, validated by qRT-PCR in 786-O and HK-2 cell lines.
Dysregulation of 16/18 crotonylation-related genes was observed in ccRCC. WGCNA revealed crotonylation related modules significantly enriched in angiogenesis, calcium/Ras signaling, and cancer stemness pathways. A 5-gene prognostic model (PLCL1, DNASE1L3, CD248, CDH13, PDGFD) demonstrated robust stratification: High-risk patients showed poorer overall survival, higher Treg infiltration, elevated tumor mutation burden and increased sensitivity to several chemotherapy approaches like Cisplatin. Molecular docking identified diacetylmorphine as a potential therapeutic agent (binding energy: -7.278 kcal/mol with DNASE1L3). Spatial/single-cell analyses confirmed cell-type-specific gene expression and the diffferential expression between tumor and normal cell lines was validated by qRT-PCR.
This study establishes a crotonylation-based prognostic model that effectively stratifies ccRCC risk and elucidates key mechanisms linking crotonylation heterogeneity to immune evasion, mutational burden, and metabolic reprogramming. The model offers clinical utility for personalized therapy selection.
巴豆酰化作为一种翻译后修饰，与癌症进展有关，但其在透明细胞肾细胞癌（ccRCC）中的预后意义仍不清楚。本研究旨在揭示巴豆酰化异质性，并建立一个可靠的ccRCC预后模型。
我们使用多组学方法分析了来自TCGA-KIRC和GEO队列（GSE40435、GSE167573、GSE29609）的转录组数据。通过单样本基因集富集分析（ssGSEA）计算巴豆酰化评分，并通过加权基因共表达网络分析（WGCNA）确定相关基因模块。我们整合了10种机器学习算法来开发一个预后模型。使用Cibersort分析免疫微环境，通过maftools分析突变图谱，并通过oncoPredict分析药物敏感性。对空间转录组学和单细胞数据进行表达模式分析，并在786-O和HK-2细胞系中通过qRT-PCR进行验证。
在ccRCC中观察到16/18个巴豆酰化相关基因的失调。WGCNA显示，巴豆酰化相关模块在血管生成、钙/Ras信号传导和癌症干性途径中显著富集。一个由5个基因组成的预后模型（PLCL1、DNASE1L3、CD248、CDH13、PDGFD）显示出强大的分层能力：高危患者的总生存期较差，调节性T细胞（Treg）浸润较高，肿瘤突变负担增加，对几种化疗方法（如顺铂）的敏感性增加。分子对接确定二乙酰吗啡为一种潜在治疗剂（与DNASE1L3的结合能为-7.278千卡/摩尔）。空间/单细胞分析证实了细胞类型特异性基因表达，肿瘤和正常细胞系之间的差异表达通过qRT-PCR得到验证。
本研究建立了一个基于巴豆酰化的预后模型，该模型有效地对ccRCC风险进行分层，并阐明了将巴豆酰化异质性与免疫逃逸、突变负担和代谢重编程联系起来的关键机制。该模型为个性化治疗选择提供了临床实用性。
