Machine learning and multi-omics characterization of SLC2A1 as a prognostic factor in hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) is characterized by high incidence, significant mortality, and marked heterogeneity, making accurate molecular subtyping essential for effective treatment. Using multi-omics data from HCC patients, we applied diverse clustering algorithms to identify three HCC subtypes (HSs) with distinct prognostic characteristics. Among these, HS1 emerged as an immune-compromised subtype associated with the poorest prognosis. Additionally, we developed a novel, robust, and highly accurate machine learning-guided prognostic signature (MLPS) by integrating multiple machine learning algorithms and their combinations. Our study also identified SLC2A1, the core gene of MLPS, as being highly expressed during advanced stages of tumor progression. Knockdown experiments demonstrated that reducing SLC2A1 expression significantly suppressed the malignant behavior of HCC cells. Furthermore, SLC2A1 expression was linked to responsiveness to dasatinib and vincristine, suggesting potential therapeutic relevance. MLPS and SLC2A1 offer promising tools for individualized prognosis prediction and targeted therapy in HCC, providing new opportunities to improve patient outcomes.