Sun Yuan, Li Yan, Zhang Anlan, Hu Tao, Li Ming
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Discov Oncol. 2025 May 24;16(1):905. doi: 10.1007/s12672-025-02733-7.
Pancreatic cancer is a highly aggressive cancer characterized by low survival rate. Enhanced ribosome biogenesis may be associated with tumor drug resistance and malignant phenotypes, representing a potential therapeutic target in pancreatic cancer. Therefore, exploring the molecular mechanisms of ribosome biogenesis in pancreatic cancer may uncover new biomarkers and potential therapeutic targets, facilitating the development of personalized treatment strategies.
Ribosome biogenesis-related gene signatures were acquired from TCGA and Gene Cards databases. Prognostic gene sets were screened using machine learning algorithms to construct a risk model, which was externally validated via GEO database. Single-cell RNA sequencing analysis (GSE155698 dataset) was performed to assess gene expression patterns and module scores.
Sixty ribosome biogenesis-related prognostic genes were identified in pancreatic cancer. Cox regression and machine learning algorithms selected nine pivotal biomarkers (ECT2; CKB; HMGA2; TPX2; ERBB3; SLC2A1; KRT13; PRSS3; CRABP2) with high diagnostic and prognostic specificity for PAAD. The machine learning-derived risk score correlated strongly with tumor proliferation pathways and immunosuppression, suggesting dual roles in tumor promotion and immunosuppressive microenvironment remodeling. Single-cell analysis highlighted predominant expression of CKB, SLC2A1, ERBB3, CRABP2, and PRSS3 in pancreatic ductal epithelial cells.
Our results shed light on the potential connections between ribosome biogenesis-related molecular characteristics and clinical features, the tumor microenvironment, and clinical drug responses. The research underscores the critical role of ribosome biogenesis in the progression and treatment resistance of pancreatic cancer, offering valuable new perspectives for prognostic evaluation and therapeutic response prediction in pancreatic cancer.
胰腺癌是一种侵袭性很强的癌症，生存率低。核糖体生物合成增强可能与肿瘤耐药性和恶性表型有关，是胰腺癌潜在的治疗靶点。因此，探索胰腺癌中核糖体生物合成的分子机制可能会发现新的生物标志物和潜在的治疗靶点，促进个性化治疗策略的发展。
从TCGA和基因卡数据库中获取核糖体生物合成相关基因特征。使用机器学习算法筛选预后基因集以构建风险模型，并通过GEO数据库进行外部验证。进行单细胞RNA测序分析（GSE155698数据集）以评估基因表达模式和模块评分。
在胰腺癌中鉴定出60个核糖体生物合成相关的预后基因。Cox回归和机器学习算法选择了9个关键生物标志物（ECT2；CKB；HMGA2；TPX2；ERBB3；SLC2A1；KRT13；PRSS3；CRABP2），对PAAD具有高诊断和预后特异性。机器学习得出的风险评分与肿瘤增殖途径和免疫抑制密切相关，表明在肿瘤促进和免疫抑制微环境重塑中具有双重作用。单细胞分析突出了CKB、SLC2A1、ERBB3、CRABP2和PRSS3在胰腺导管上皮细胞中的主要表达。
我们的结果揭示了核糖体生物合成相关分子特征与临床特征、肿瘤微环境和临床药物反应之间的潜在联系。该研究强调了核糖体生物合成在胰腺癌进展和治疗耐药中的关键作用，为胰腺癌的预后评估和治疗反应预测提供了有价值的新视角。
