FerroScore: a statistical approach for quantifying tumor-related ferroptosis based on omics data

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ID: 319845
2026
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Abstract
Abstract Ferroptosis is a novel form of programmed cell death driven by iron-dependent lipid peroxidation, and can significantly influence the progression of complex diseases such as cancer. Current methods of detecting ferroptosis rely primarily on experimental techniques that are typically low-throughput and costly, limiting their clinical applications. Here we develop an effective statistical method, FerroScore, to quantify ferroptosis by generating a score that integrates the activities of three core pathways—iron, glutathione, and lipid metabolism. This method enables the cross-resolution assessment of ferroptosis and provides mechanistic insights into tumor, immune, and neurodegenerative diseases, thus having potential applications in targeted therapy and drug discovery. When applied to pancreatic cancer transcriptomic data, FerroScore reveals: (i) a U-shaped relationship between ferroptosis and patient survival; (ii) heterogeneous ferroptosis activity across cell types in the tumor microenvironment, with high sensitivity to Macrophages, CD8 Tcm cells, and a population of nCAFs; (iii) the role of ferroptosis-active cells in reshaping the immunosuppressive and pro-metastatic microenvironment through intercellular communication.
Reference Key
openalex_W7167465193 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jiaqi Teng, Qi Gong, Zhaohang Cai, Tianshou Zhou
Journal Briefings in bioinformatics
Year 2026
DOI
10.1093/bib/bbag368
URL
https://doi.org/10.1093/bib/bbag368
Keywords Keywords not found

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