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

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 ana

SUBMITTER: Chen W 

PROVIDER: S-EPMC11843271 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature