AI-driven integration of microbiome and exosome profiles for precision diagnostics and therapeutics in colorectal cancer.

e13634 Background: Exosomes and the microbiome are emerging as critical components in precision oncology, offering innovative avenues for early cancer detection and personalized patient care. While advances have been made in understanding these systems, their combined diagnostic and therapeutic potential in oncology remains underexplored. This study builds on foundational work by Youssef et al. (1, 2), demonstrating exosome-based diagnostics and microbiome modulation in oncology. Using artificial intelligence (AI), we integrate these approaches to identify novel biomarkers relevant to colorectal cancer (CRC), focusing on non-invasive diagnostics and real-time monitoring. Methods: We analyzed publicly available multi-omics datasets, encompassing microbiome profiles, exosomal content, transcriptomics, and metabolomics. Machine learning algorithms uncovered correlations between exosomal microRNAs (e.g., miR-1247-3p) and microbial signatures (e.g., Fusobacterium nucleatum), comparing CRC samples to healthy controls. Results: Our analysis identified three specific bacterial taxa influencing exosomal cargo, including short-chain fatty acids and oncogenic microRNAs that promote tumor progression and immune evasion. CRC-derived exosomes carried biomarkers indicative of oxidative phosphorylation dysregulation and distinct microbial metabolite profiles, supporting their use in non-invasive diagnostics. Furthermore, microbial extracellular vesicles from fecal samples provided complementary insights, reflecting disease-specific microbial ecosystems. Validation confirmed the clinical utility of these biomarkers in distinguishing CRC from healthy states with high sensitivity and specificity. Conclusions: This AI-powered framework highlights the transformative potential of integrating microbiome and exosome data in oncology. By identifying actionable biomarkers, our study supports innovative diagnostic tools for early cancer detection, real-time monitoring, and therapeutic targeting. Additionally, integrating gene therapy concepts underscores the potential for these biomarkers to guide clinical decision-making and enhance therapeutic strategies. Future studies will focus on validating these findings in larger, diverse cohorts and exploring their potential in guiding personalized treatment strategies. References: Youssef E. Enhancing precision in cancer treatment: The role of gene therapy and immune modulation in oncology. Front Med. 2024. doi:10.3389/fmed.2024.1527600 Youssef E. Exosomes in precision oncology: From bench to bedside in diagnostics and therapeutics. (submitted) Preprint posted online January 14, 2025. doi:10.20944/preprints202501.1036.