Prognostic model identification of ribosome biogenesis-related genes in pancreatic cancer based on multiple machine learning analyses.

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.