ArticleBiochemistry and biophysics reports2026
Enhancing colorectal cancer diagnosis with the ferroptosis marker GPX4 and serum biomarkers: A retrospective analysis and machine learning approach.
Article in Biochemistry and biophysics reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external image-input validation.Journal of translational medicine · 2026Article
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11 authors.
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Abstract
Background: Colorectal cancer (CRC) remains a significant global health burden characterized by high incidence and mortality. Although early diagnosis is crucial for improving prognosis, current screening methods are limited by insufficient sensitivity. Ferroptosis, an iron-dependent form of regulated cell death, is implicated in tumor progression, with Glutathione peroxidase 4 (GPX4) serving as a key regulator in this process. Objective: This study aims to evaluate the potential of GPX4 as an early diagnostic biomarker for CRC and to construct an integrated predictive model combining ferroptosis-related indicators with machine learning algorithms to enhance diagnostic precision. Methods: Clinical data and GPX4 gene expression profiles were retrieved from the TCGA-COAD database for survival, differential expression, single-cell transcriptomic, and immune infiltration analyses. Subsequently, GPX4 expression was validated in CRC tissues, adjacent normal tissues, and cell lines with varying metastatic potentials using immunohistochemistry (IHC), qRT-PCR, and Western blotting. Serum levels of carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), GPX4, and serum iron were quantified in 120 CRC patients and 120 healthy controls. The diagnostic efficacy of individual markers was assessed using receiver operating characteristic (ROC) curves and Kappa analysis. Finally, nine machine learning algorithms were employed to develop a combinatorial diagnostic model based on serum ferroptosis-related indicators, with performance evaluated via ROC and decision curve analysis (DCA). Conclusion: Ferroptosis plays a critical role in CRC pathogenesis and progression. GPX4 was identified as a robust biomarker for early diagnosis. The integrated machine learning model, incorporating GPX4, CEA, CA19-9, and serum iron, demonstrated superior diagnostic performance compared to conventional markers, offering a promising strategy for the early detection of CRC.
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