Multi-level Analysis of Codon Usage Patterns Reveals Systematic Optimization of Oncogenic Gene Expression in Pancreatic Cancer

BackgroundCodon usage bias, the non-random usage of synonymous codons in coding sequences, represents a fundamental feature of genomic organization that has been largely understudied in cancer biology. Pancreatic ductal adenocarcinoma (PDAC), the predominant subtype of pancreatic cancer, is characterized by aggressive disease progression and limited therapeutic options, necessitating novel approaches to understand its molecular pathogenesis. Leveraging publicly available single-cell RNA sequencing data, we performed comprehensive codon usage analyses across different cellular populations in PDAC. ResultsEmploying a variety of computational codon usage indices uncovered the connections between cancer-specific cellular state features and codon usage signatures. Our findings reveal that malignant pancreatic cells express genes with significantly higher GC content, demonstrate preferential usage of optimal codons through increased frequency of preferred synonymous codons, and exhibit a marked preference for more cost-effective amino acids. Analysis of transcript-level bulk RNA-seq data from PDAC tumors revealed that these codon optimization patterns extend to alternative isoform usage, with highly expressed isoforms displaying increased codon optimality and enhanced mRNA stability. ConclusionThese codon usage-dependent adaptations operating at both gene expression and transcript isoform levels may enable malignant cells to enhance gene expression rates, potentially leading to increased translational efficiency and protein production. These insights into the codon usage landscape of PDAC may provide potential biomarkers for disease monitoring and treatment response prediction.