Year: 2025
Type: article
Abstract: Hepatocellular carcinoma (HCC) remains the leading cause of cancer-related mortality worldwide, largely because of its late detection and high recurrence rates. Conventional biomarkers such as alpha-fetoprotein (AFP) are unable to detect early-stage diseases with sufficient accuracy. Exosomal microRNAs (miRNAs) are small non-coding RNAs, encapsulated within extracellular vesicles, that have emerged as highly sensitive and specific non-invasive biomarkers with revolutionary potentials for improving HCC diagnosis and prognosis prediction. Several studies have demonstrated that circulating exosomal miRNAs outperform AFP detection in differentiating early-stage HCC from chronic liver disease and in predicting metastasis, recurrence, and patient survival. Furthermore, multi-miRNA panels and AI-driven predictive models integrating exosomal miRNA signatures with clinical parameters enhance the diagnostic accuracy and enable personalized risk stratification. Despite promising results, clinical implementation has been challenged by assay standardization, interpatient variability, and the need for large-scale prospective validation. Future research should include developing robust, high-throughput exosomal miRNA detection platforms, incorporating machine learning for optimized biomarker selection, and integrating exosomal miRNAs with other liquid biopsy approaches for comprehensive disease monitoring. In summary, exosomal miRNAs represent a powerful tool for revolutionizing the early detection and tailored management of HCC, ultimately improving patient outcomes through timely and precise interventions.
Source: Convergence Hepatology
Authors: Sarang Kim, Nayeon Gu, Tae-Su Han
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Award: G8315367991
OpenAlex ID: W4417308326