International Journal of Molecular Medical Science, 2025, Vol.15, No.1, 9-19 http://medscipublisher.com/index.php/ijmms 17 and global access. Multi-omics approaches, which combine genomics, transcriptomics, proteomics, and metabolomics, offer a comprehensive analysis of tumor molecular characteristics. This approach helps identify complex biomarkers that may be missed by analyzing a single data type and enhances diagnostic accuracy and therapeutic target identification through the integration of multiple layers, such as gene expression and DNA methylation data. However, the complexity of these datasets requires robust computational tools and standardized analysis protocols, and future efforts must refine these integration methods to translate multi-omics findings into clinical practice. Artificial Intelligence (AI) is transforming biomarker discovery, especially in handling complex genomic data. Machine learning algorithms and deep learning models can analyze multi-omics data to identify key biomarkers associated with oral cancer prognosis. AI also optimizes the analysis of liquid biopsy data, such as ctDNA and circulating miRNAs, helping to detect early-stage cancer-related genetic changes with high precision. However, challenges remain in creating interpretable AI models that clinicians can understand and trust. Future research needs to focus on improving AI transparency and developing standardized frameworks for validating AI models in clinical environments. The widespread implementation of genomic biomarkers in clinical settings faces challenges related to regulatory approval, costs, and global access. High-throughput technologies like next-generation sequencing remain expensive, limiting their use in resource-constrained regions. Reducing testing costs through technological innovation and developing affordable point-of-care tests are crucial for expanding access. Additionally, standardizing testing protocols and ensuring consistency across laboratories are essential for regulatory approvals and broad clinical adoption. International collaborations can facilitate data sharing and best practices, accelerating the validation of biomarkers and their global implementation, ultimately ensuring that patients worldwide benefit from early detection and personalized therapies. Acknowledgments The author expresses gratitude to two anonymous peer reviewers from Shanghai Jiao Tong University School of Medicine and Peking University Health Science Center for their thorough and meticulous review of this manuscript. Sincere thanks are extended to these anonymous reviewers for their valuable feedback on the initial draft of the study. Their critical evaluations and constructive suggestions greatly contributed to the improvement of the manuscript. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Adeoye J., Alade A., Zhu W.Y, Wang W., Choi S., and Thomson P., 2021, Efficacy of hypermethylated DNA biomarkers in saliva and oral swabs for oral cancer diagnosis: systematic review and meta-analysis, Oral Diseases, 28(3): 541-558. https://doi.org/10.1111/odi.13773 Auzair L.B.M., Vincent-Chong V., Ghani W., Kallarakkal T., Ramanathan A., Lee C., Rahman Z., Ismail S., Abraham M., and Zain R., 2016, Caveolin 1 (Cav-1) and actin-related protein 2/3 complex, subunit 1B (ARPC1B) expressions as prognostic indicators for oral squamous cell carcinoma (OSCC), European Archives of Oto-Rhino-Laryngology, 273(7): 1885-1893. https://doi.org/10.1007/s00405-015-3703-9 Arunkumar G., Rao A., Manikandan M., Rao H., Subbiah S., Ilangovan R., Murugan A., and Munirajan A., 2017, Dysregulation of miR-200 family microRNAs and epithelial-mesenchymal transition markers in oral squamous cell carcinoma, Oncology Letters, 15(1): 649-657. https://doi.org/10.3892/ol.2017.7296 Blatt S., Kämmerer P.W., Krüger M., Surabattula R., Thiem D., Dillon S., Al-Nawas B., Libermann T., and Schuppan D., 2023, High-multiplex aptamer-based serum proteomics to identify candidate serum biomarkers of oral squamous cell carcinoma, Cancers, 15(7): 2071. https://doi.org/10.3390/cancers15072071 Bronkhorst A., Ungerer V., and Holdenrieder S., 2019, Early detection of cancer using circulating tumor DNA: biological, physiological and analytical considerations, Critical Reviews in Clinical Laboratory Sciences, 57: 253-269. https://doi.org/10.1080/10408363.2019.1700902 Boutros P., 2015, The path to routine use of genomic biomarkers in the cancer clinic, Genome Research, 25: 1508-1513. https://doi.org/10.1101/gr.191114.115
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