Prognostic Model Identification of Ribosome Biogenesis-Related Genes in Pancreatic Cancer Based on Multiple Machine Learning Analyses
Authors: Sun Yuan , Li Yan , Zhang Anlan , Hu Tao , Li Ming 
Published: January 2025

Abstract

Background
Pancreatic cancer is one of the most aggressive and lethal malignancies within the digestive system, characterized by an extremely low five-year survival rate and formidable treatment challenges. Ribosome biogenesis is a core process in protein synthesis, essential for cell growth, proliferation, and survival. Tumors typically exhibit abnormally high proliferation rates, with ribosome biogenesis often being excessively activated in cancer cells to synthesize a significant quantity of proteins required for tumor development. In pancreatic cancer, enhanced ribosome biogenesis may be associated with tumor drug resistance and malignant phenotypes, presenting it as a potential therapeutic target. 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 obtained from the TCGA database and Gene Cards. Multiple machine learning methods were employed to screen prognostic gene sets closely related to survival, followed by the construction of a risk model. The GEO database was utilized for external validation of the model. The GSE155698 single-cell RNA sequencing dataset was analyzed to evaluate gene expression profiles and associated module scores.
Results
A total of 60 ribosome biogenesis-related prognostic genes in pancreatic cancer were identified. Through univariate Cox regression and various machine learning techniques, nine key prognostic genes (ECT2, CKB, HMGA2, TPX2, ERBB3, SLC2A1, KRT13, PRSS3, CRABP2) were selected as highly sensitive and specific biomarkers for the diagnosis and prognosis of PAAD patients. These genes demonstrated high sensitivity and specificity as biomarkers for the diagnosis and prognosis of pancreatic cancer patients. The risk score feature constructed based on machine learning was found to be an independent factor associated with high mortality, advanced clinical pathological features, and chemotherapy resistance. Additionally, this feature was closely related to cell proliferation and cancer progression-related pathways, suggesting its potential role in promoting pancreatic cancer development through enhanced ribosomal division, mitosis-related processes, and microtubule cytoskeleton organization. Furthermore, the feature was closely associated with immunosuppressive cell infiltration and immune checkpoint expression, indicating its role as a key immunosuppressive factor within the tumor microenvironment. Single-cell RNA sequencing revealed higher expression of CKB, SLC2A1, ERBB3, CRABP2, and PRSS3 in pancreatic ductal epithelial cells.
Conclusions
This study provides insights into 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.