Identifying cancer prognosis genes through causal learning.

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Accurate cancer prognosis is crucial for effective treatment and disease management.
• Identifying causal genes aids in understanding cancer progression and patient outcomes.
• Existing methods may lack the precision or generalizability needed for diverse cancer types.

Purpose of the Study:

• To develop and validate CPCG (Cancer Prognosis's Causal Gene), a novel framework for identifying gene sets causally linked to cancer prognosis.
• To leverage transcriptomic data for robust and generalizable prognostic biomarker discovery across multiple cancer types.
• To provide an interpretable and powerful tool for clinical decision-making in oncology.

Main Methods:

• A two-stage framework combining ensemble modeling of gene expression and survival data with parametric/semiparametric hazard models.
• Inference of a causal skeleton using iterative conditional independence testing and graph pruning to pinpoint prognostic genes.
• Experimental validation using large-scale transcriptomic datasets from The Cancer Genome Atlas, Gene Expression Omnibus, and Chinese Glioma Genome Atlas Project.

Main Results:

• CPCG effectively predicts cancer prognosis across 18 cancer types using transcriptomic data, validated by four evaluation metrics.
• Robustness and generalizability confirmed across 24 additional datasets spanning 12 cancer types.
• Identified a concise set of prognostic genes linked to critical cancer biological processes, bypassing complex gene combination analysis.

Conclusions:

• CPCG is a powerful, interpretable, and generalizable tool for identifying cancer prognostic biomarkers.
• The framework demonstrates robustness and stability in causal skeleton inference.
• CPCG facilitates targeted interventions and holds promise for advancing clinical cancer treatment strategies.