Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Several diagnostic methods, such as imaging modalities and Serum Alpha-Fetoprotein (AFP) testing, have been used for HCC detection; however, their effectiveness is limited to later stages of the disease. In contrast, transcriptomic analysis of biposy samples has shown promise for early detection. While machine learning techniques have been applied to transcriptomic data for cancer detection, their clinical adoption remains limited due to challenges such as poor generalizability across different datasets, lack of interpretability, and high computational complexity. To address these limitations, we developed a novel predictive formula for HCC detection using the Kolmogorov-Arnold Network (KAN). This formula is based on the expression levels of five genes: VIPR1, CYP1A2, FCN3, ECM1, and LIFR. Derived from the GSE25097 dataset, the formula offers a simple, interpretable, efficient, and accessible approach for HCC identification. It achieves 99% accuracy on the GSE25097 test set and demonstrates robust performance on six additional independent datasets, achieving accuracies of above 90% in all cases. These findings highlight the critical role of these five genes as biomarkers for HCC detection, offering a foundation for future research and clinical applications to improve HCC diagnostic approaches.
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It has been accepted for publication in Biotechnology Journal
