Identifying squalene epoxidase as a metabolic vulnerability in high-risk osteosarcoma using an artificial intelligence-derived prognostic index.

ABSTRACT:

Background

Osteosarcoma (OSA) presents a clinical challenge and has a low 5-year survival rate. Currently, the lack of advanced stratification models makes personalized therapy difficult. This study aims to identify novel biomarkers to stratify high-risk OSA patients and guide treatment.

Methods

We combined 10 machine-learning algorithms into 101 combinations, from which the optimal model was established for predicting overall survival based on transcriptomic profiles for 254 samples. Alterations in transcriptomic, genomic and epigenomic landscapes were assessed to elucidate mechanisms driving poor prognosis. Single-cell RNA sequencing (scRNA-seq) unveiled genes overexpressed in OSA cells as potential therapeutic targets, one of which was validated via tissue staining, knock

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10875711 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature