Analysis and Validation of Tyrosine Metabolism-related Prognostic Features for Liver Hepatocellular Carcinoma Therapy

Area of Science:

• Oncology
• Metabolomics
• Bioinformatics

Background:

• Tyrosine metabolism is crucial in liver hepatocellular carcinoma (LIHC) development.
• Existing prognostic models for LIHC require improvement for precision medicine.

Purpose of the Study:

• To explore tyrosine metabolism characteristics in LIHC.
• To establish a prognostic risk signature for LIHC.
• To develop a tyrosine metabolism-related prognostic model.

Main Methods:

• Utilized The Cancer Genome Atlas (TCGA) database for LIHC gene expression and clinical data.
• Classified LIHC subtypes using consensus clustering on tyrosine metabolism genes.
• Developed a RiskScore model via univariate and Lasso Cox regression.
• Validated prognostic performance using Kaplan-Meier survival analysis and ROC curves.
• Assessed immune infiltration and metabolic pathways via ssGSEA.
• Constructed a nomogram integrating RiskScore and clinical features.

Main Results:

• Identified 3 distinct LIHC subtypes based on tyrosine metabolism genes with significant prognostic differences.
• Developed a RiskScore model using four key genes, effectively stratifying LIHC patients into high- and low-risk groups.
• High-risk LIHC patients exhibited poorer overall survival, reduced immunotherapy response, increased immune infiltration, higher clinical grade, and altered metabolic pathways.
• The RiskScore was confirmed as an independent prognostic indicator for LIHC and showed prognostic performance across multiple cancers.
• The nomogram highlighted the RiskScore's significant contribution to LIHC prognosis prediction.

Conclusions:

• A novel tyrosine metabolism-related risk model was successfully developed for LIHC.
• This risk model demonstrates robust survival prediction capabilities.
• The model holds potential as an independent prognostic predictor for guiding LIHC treatment strategies.