Cancer Genetics and Epigenetics, 2025, Vol.13, No.3, 106-116 http://medscipublisher.com/index.php/cge 112 treatment effects. However, to achieve wide clinical application, further verification and approval from regulatory authorities are still needed (Song et al., 2020; Caruso et al., 2023; Wang et al., 2025). Continuous research and large-scale clinical trials are crucial for determining the clinical value and reliability of exosome-based liquid biopsy products. 6.2 Issues in clinical application: standardization, ethical considerations and regulatory policies One of the major difficulties in the clinical application of liquid biopsy of exosomes is the lack of unified norms for the separation, identification and analysis of exosomes. Due to the different methods of sample collection, processing and analysis, inconsistent test results are prone to occur, and it is difficult to obtain the same or comparable results among different research and clinical institutions (Genova et al., 2019). Therefore, formulating unified operation methods and strict quality control measures is crucial for ensuring the reliability of exosome diagnostic results and their practical application in clinical practice (Breakefield et al., 2021; Cai et al., 2022; Wang et al., 2025). In the process of applying exosome liquid biopsy to clinical practice, ethics and supervision are also very important. As exosome testing is increasingly used in daily medical care, issues such as patient consent to participate in the testing, personal data protection, and how to handle unexpected test results must be properly addressed (Zhang et al., 2024). Before approving new diagnostic products, regulatory authorities need to have sufficient evidence to prove that the products are safe, effective and helpful for clinical treatment. This requires conducting well-planned clinical trials and reporting the trial results truthfully and clearly (Wang et al., 2025). 6.3 Obstacles and strategies for transforming research results into clinical practice The main obstacles faced in clinical transformation include: the technical difficulties in achieving high-purity and high-sensitivity exosome isolation, as well as the demand for an economical, scalable and easy-to-operate platform suitable for clinical laboratories (Breakefield et al., 2021). The biological characteristics of exosomes vary greatly and there is a lack of recognized biomarkers, which further increases the difficulty of developing standardized diagnostic methods (Cai et al., 2022; Jiang et al., 2022). Furthermore, there are no clear judgment criteria and reference ranges for exosome biomarkers, which also hinders their interpretation and application in clinical practice (Genova et al., 2019; Wang et al., 2025). To overcome these obstacles, cooperation among clinicians, researchers and enterprises is highly necessary. The strategies that can be adopted include: developing a new platform integrating microfluidic and biosensor technologies, conducting multi-center validation studies, and establishing a large and well-annotated biobank for the discovery and validation of biomarkers (Song et al., 2020; Cai et al., 2022). The active participation of regulatory authorities and the clarification of clinical application value through prospective trials are crucial for the successful application of exosome-based liquid biopsy in conventional cancer treatment (Breakefield et al., 2021; Jiang et al., 2022; Wang et al., 2025). 7 Future Development Directions 7.1 Integrate multimodal diagnosis and AI-assisted data analysis Combining exosome detection with other liquid biopsy methods, such as circulating tumor DNA (ctDNA), holds great promise for making the diagnostic results more accurate and comprehensive. Integrating multiple biomarker sources can present the molecular characteristics of tumors more completely, which is helpful for the early detection, type determination and disease monitoring of cancer. This multimodal diagnostic approach gives full play to the strengths of each biomarker and may overcome the shortcomings of single-biomarker detection to form a more reliable, sensitive and accurate cancer detection scheme (Kalluri and LeBleu, 2020). Artificial intelligence (AI) and machine learning are increasingly being applied in handling the complex data generated from exosome diagnosis. AI algorithms can efficiently analyze the spectral and image data collected by advanced biosensors, identify subtle patterns, and construct predictive models for precise diagnosis. The use of interpretable and uninterpretable AI models can enhance the transparency and accuracy of exosome diagnosis, laying the foundation for the application of real-time bedside diagnosis and mobile healthcare (Zhu et al., 2024).
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