DOI: 10.31083/fbe49059 ISSN: 1945-0494

Codon Usage Bias Analysis in Oral Cancer: Comprehensive Molecular and Evolutionary Insights

Manal Abouelwafa, John J Georrge, Supriyo Chakraborty

Background: Oral squamous cell carcinoma (OSCC) is represented as a major global health concern with few treatment options. Codon usage bias (CUB) is a non-random selection of synonymous codons. It offers information on gene expression patterns and molecular evolution. Methods: This study examined CUB patterns in 1328 differentially expressed genes (651 up-regulated, 677 down-regulated) from oral cancer tissues. Nucleotide composition analysis, effective number of codons (ENC), Relative Synonymous Codon Usage (RSCU) analysis, neutrality plot analysis, and gene expression analysis were performed to assess codon usage patterns and their relationship to gene expression levels and pathway classification. Results: Nucleotide composition analysis indicated a clear GC-rich tendency with base frequencies arranged as C > G > A > T for up-regulated genes and C > A > G > T for down-regulated genes. The average ENC values were 47.21 (up-regulated) and 49.08 (down-regulated), suggesting a low codon usage bias. RSCU analysis revealed 25 more frequently used (mean RSCU >1.0) and 34 less frequently used (mean RSCU <1.0) codons in up-regulated genes, while down-regulated genes had 30 more frequently used and 29 less frequently used codons. Neutrality plot analysis showed that both gene groups were predominantly shaped by selective constraints rather than mutational pressure (slopes of 0.202 and 0.228 for up- and down-regulated genes, respectively), though these slopes provide only indirect, qualitative evidence and should not be interpreted as exact quantitative contributions. Gene expression analysis revealed 1328 differentially expressed genes with functional enrichment in metabolic pathways and cellular components. Conclusions: These discoveries offer molecular insights into the genetic pathways that support oral cancer development. It implies that codon optimization could affect the translation efficiency of cancer-promoting genes.