ArticleThe Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology2024
Machine Learning Diagnostic Model for Hepatocellular Carcinoma Based on Liquid-Liquid Phase Separation and Ferroptosis-Related Genes.
Article in The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Targeting ferroptosis induction via nanomaterials in hepatocellular carcinoma: an insight into mechanism and therapeutic potential.Journal of nanobiotechnology · 2026Review
- Phase Separation of NFIB Suppresses SLC3A2-Mediated Ferroptosis in Castration-Resistant Prostate Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence-based prognostic modeling of immunoradiotherapy in Barcelona clinic liver cancer stage C hepatocellular carcinoma: a multicenter retrospective study.Frontiers in oncology · 2026Article
- Cuproptosis in lung cancer: a nexus of ncRNA regulation, epigenetics, and tumor microenvironment Remodeling.Clinical and experimental medicine · 2025Review
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Abstract
Background/Aims: Hepatocellular carcinoma (HCC) represents a primary liver malignancy with a multifaceted molecular landscape. The interplay between liquid-liquid phase separation (LLPS) and ferroptosis-a regulated form of cell death-has garnered interest in tumorigenesis. However, the precise role of LLPS and ferroptosis-related genes in HCC progression and prognosis remains obscure. Unraveling this connection could pave the way for innovative diagnosis and therapeutic strategies. Materials and Methods: The differentially expressed genes (DEGs) were identified based on 3 GEO datasets, followed by overlapping with LLPS-related and ferroptosis-related genes. Based on central hub genes, a diagnostic model was developed through LASSO regression and validated using KM survival analysis and real-time quantitative polymerase chain reaction (RT-qPCR). Then the effects of NRAS on the development of HCC and ferroptosis were also detected. Results: We identified 24 DEGs overlapping among HCC-specific, LLPS, and ferroptosis-related genes. A diagnostic model, centered on 5 hub genes, was developed and validated. Lower expression of these genes corresponded with enhanced patient survival rates, and they were distinctly overexpressed in HCC cells. NRAS downregulation significantly inhibited HepG2 cell proliferation and migration (P < .01). Fe2+ content and ROS levels were both significantly increased in the si-NRAS group when compared to those in the si-NC group (P < .01), while opposite results were observed for the protein level of GPX4 and GSH content. Conclusion: The diagnostic model with 5 hub genes (EZH2, HSPB1, NRAS, RPL8, and SUV39H1) emerges as a potential innovative tool for the diagnosis of HCC. NRAS promotes the carcinogenesis of HCC cells and inhibits ferroptosis.
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