Artificial Intelligence-Driven Multiomics and Clinical Investigation Identify Macrophage Migration Inhibitory Factor as a Pan-Cancer Biomarker.

IF 6.2 Q2 GENETICS & HEREDITY

Phenomics (Cham, Switzerland) Pub Date : 2026-05-20 eCollection Date: 2026-06-01 DOI:10.1007/s43657-026-00322-4

Fengsen Liu, Yu Ping, Miaomiao Li, Kaiyuan Guo, Lei Zhang, Peng Wu, Yijun Tian, Jinyan Liu, Yi Zhang

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Abstract

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.

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

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人工智能驱动的多组学和临床研究鉴定巨噬细胞迁移抑制因子作为泛癌症生物标志物。

由于缺乏可靠的泛癌症筛查方法，特别是基于血液的生物标志物，早期癌症检测仍然具有挑战性。利用一种新的三层验证框架，结合人工智能（AI）驱动的18万篇PubMed文章（1950-2024）的文献挖掘，跨主要数据库的多组学整合和广泛的临床验证，我们确定巨噬细胞迁移抑制因子（MIF）是一种有前途的泛癌症检测血液生物标志物。多组学分析显示，在21种癌症类型的转录水平和12种癌症类型的蛋白质水平上，MIF的上调是一致的。独立队列（n = 4269）的临床验证表明，血清MIF蛋白水平在癌症患者和健康对照之间（中位AUC = 0.994）以及癌症和良性疾病之间（中位AUC = 0.881）有效区分。值得注意的是，比较分析显示，MIF表现出优于或与已建立的癌症特异性标志物相当的性能，包括肝癌的AFP （MIF AUC = 0.885, AFP AUC: 0.744-0.887）和卵巢癌的CA125 （MIF AUC = 0.831, CA125 AUC: 0.58-0.71）。28个队列（n = 5347）的荟萃分析证实了MIF的诊断有效性（合并AUC: 0.782）。这种具有成本效益的、基于血液的ELISA方法使MIF成为广泛应用于癌症筛查的有价值的工具。补充信息：在线版本包含补充资料，可在https://doi.org/10.1007/s43657-026-00322-4上获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Phenomics (Cham, Switzerland)

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