Artificial Intelligence-Driven Multiomics and Clinical Investigation Identify Macrophage Migration Inhibitory Factor...

Early cancer detection remains challenging due to the lack of reliable pan-cancer screening methods, particularly blood-based biomarkers. Using a novel three-tiered validation framework combining artificial intelligence (AI)-powered literature mining of 180,000 PubMed articles (1950-2024), multiomics integration across major databases, and extensive clinical validation, we identified macrophage migration inhibitory factor (MIF) as a promising blood-based biomarker for pan-cancer detection. Multiomics analysis revealed consistent MIF upregulation across 21 cancer types at the transcriptional level and across 12 cancer types at the protein level. Clinical validation in independent cohorts (n = 4,269) showed that serum MIF protein levels discriminated effectively between cancer patients and healthy controls (median AUC = 0.994) and between cancer and benign conditions (median AUC = 0.881). Notably, comparative analyses showed that MIF demonstrated superior or comparable performance to established cancer-specific markers, including AFP for hepatocellular carcinoma (MIF AUC = 0.885 vs. AFP AUC: 0.744-0.887) and CA125 for ovarian cancer (MIF AUC = 0.831 vs. CA125 AUC: 0.58-0.71). Meta-analysis of 28 cohorts (n = 5,347) confirmed the diagnostic efficacy of MIF (pooled AUC: 0.782). This cost-effective, blood-based ELISA approach establishes MIF as a valuable tool for broad applications in cancer screening.

The online version contains supplementary material available at https://doi.org/10.1007/s43657-026-00322-4.