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Machine Learning Diagnostic Model for Hepatocellular Carcinoma Based on Liquid–Liquid Phase Separation and Ferroptosis-Related Genes

Wenchao Chen 1 , Ting Zhu 2 , Xiaofan Pu 1 , Linlin Zhao 3 , Senhao Zhou 4 , Xin Zhong 1 , Suihan Wang 1 , Tianyu Lin 1
1.

Department of General Surgery, Zhejiang University School of Medicine, Sir Run Run Shaw Hospital, Hangzhou, China

2.

Department of Thoracic Surgery, Shaoxing People’s Hospital, Shaoxing, China

3.

Department of Cardiology, Zhejiang University School of Medicine, Sir Run Run Shaw Hospital, Hangzhou, China

4.

Department of Otolaryngology Head and Neck Surgery, Zhejiang University School of Medicine, Sir Run Run Shaw Hospital, Hangzhou, China

Turk J Gastroenterol 2019; 1: -
DOI: 10.5152/tjg.2024.24101
Keywords : Hepatocellular carcinoma, liquid–liquid phase separation, ferroptosis, diagnostic model, survival
Read: 59 Downloads: 29 Published: 07 October 2024
Volume 1, Issue 1, January 2019
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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.

Cite this article as: Chen W, Zhu T, Pu X, et al. Machine learning diagnostic model for hepatocellular carcinoma based on liquidliquid phase separation and ferroptosis-related genes. Turk J Gastroenterol. Published online October 7, 2024. doi 10.5152/ tjg.2024.24101

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