Integrated transcriptomic and machine learning-driven analysis reveals high-confidence circular RNA biomarkers in Lung Adenocarcinoma

Lung cancer remains the leading cause of cancer-related deaths worldwide, with lung adenocarcinoma (LUAD) as its most prevalent subtype. Circular RNAs (circRNAs), known for acting as microRNA sponges and interacting with RNA-binding proteins, have emerged as key regulators in cancer biology. In this study, we introduce an integrated framework combining transcriptome profiling, network analysis, and machine learning to systematically identify and prioritise potential circRNA biomarkers in LUAD. We analysed RNA-seq datasets from LUAD samples and identified 52,744 circRNAs (18,922 novel and 33,822 previously known). Differential expression analysis revealed 1,480 significantly dysregulated circRNAs (855 downregulated and 625 upregulated) between tumour and normal tissues. To overcome limitations of traditional single-parameter screening, we implemented a three-pronged machine learning strategy integrating feature-weighted statistical scoring, unsupervised clustering with outlier detection, and deep neural networks. This multi-algorithm consensus approach identified 34 high-confidence circRNAs (17 upregulated and 17 downregulated) consistently prioritised across all methods, greatly reducing false positives. Functional enrichment analyses revealed distinct roles: upregulated circRNAs predominantly orchestrate metabolic reprogramming, epithelial–mesenchymal transition, and cytoskeletal remodelling, whereas downregulated circRNAs govern transcriptional control, apoptosis regulation, and tumour-suppressor pathways. Key biomarker candidates include has_circ_0024109, hsa_circ_0058736, hsa_circ_0052195, hsa_circ_0085740, and hsa_circ_0060931 (upregulated), and hsa_circ_0016392, hsa_circ_0058622, hsa_circ_0012248, hsa_circ_0037308, and hsa_circ_0048053 (downregulated). This study provides a comprehensive, machine-learning-validated circRNA biomarker panel for LUAD, offering mechanistic insights into circRNA-driven oncogenesis and laying a foundation for next-generation circRNA-based diagnostics and therapeutics in precision oncology.