Integrative multi-omics and machine learning reveal PLAUR as a Pan-Cancer prognostic biomarker and potential therapeutic target.
Machine learning identifies a protein as prognostic across 14 cancer types and suggests immunotherapy combination potential through tumour immune mechanisms.
Li et al. performed integrative multi-omics analysis of PLAUR (urokinase plasminogen activator receptor) across 33 TCGA cancer types using machine learning, identifying it as a robust pan-cancer prognostic biomarker associated with tumour immune infiltration and patient survival. Drug sensitivity analysis suggests clinical testability of PLAUR as an immunotherapy combination target through its roles in extracellular matrix remodelling and immune evasion, with prognostic associations confirmed across 14 cancer types.
What the study was
- Study design
- Multi-omics bioinformatics analysis with machine learning integration
- Population
- Pan-cancer TCGA datasets across 33 cancer types
- Category
- Genomics/Precision Medicine
- Maturity
- Exploratory
- Journal
- Bioorg Chem
Why it surfaced
Pan-cancer ML biomarker study with therapeutic target identification in Bioorganic Chemistry; relevant to T5 precision oncology as ML-driven target discovery.
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