INTEGRATED COMPUTATIONAL CHARACTERIZATION OF CIRCULAR RNA BIOMARKERS, REGULATORY NETWORKS, AND DERIVED PEPTIDES IN LUNG ADENOCARCINOMA

Abstract

Lung adenocarcinoma (LUAD) represents a molecularly heterogeneous malignancy characterised by a paucity of validated biomarkers for early-stage detection and precision therapeutic targeting. Although circular RNAs (circRNAs) have emerged as critical regulators of tumour biology, their mechanistic contributions to LUAD progression at both the post-transcriptional and translational levels remain insufficiently characterised. In this study, we performed an integrated computational analysis combining circRNA expression profiling, regulatory network reconstruction, multi-criteria computational biomarker prioritization, RNA-binding protein (RBP) interaction, and characterization of dysregulated circRNA-derived peptides in LUAD. We analysed publicly available RNA-Seq datasets and identified 52,744 circRNAs, among which 1,480 were significantly dysregulated, including 855 downregulated and 625 upregulated circRNAs. A multi-algorithm consensus framework shortlisted 34 high-confidence biomarker candidates from which, hsa_circ_0058622, hsa_circ_0085740, hsa_circ_0012248, and hsa_circ_0024109 showed the strongest consensus across different models. Integrated circRNA-miRNA-mRNA and RBP interaction analyses predicted EIF4A3, AGO2, and HuR as important regulatory hubs linked with epithelial-mesenchymal transition, metabolic adaptation, and oncogenic signalling pathways. Additionally, we predicted four novel circRNA-derived peptides and characterized them using physicochemical analysis and 500 ns molecular dynamics simulations. Nonetheless, these results are computational and depend on experimental validation to establish their biological significance.