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Prognostic Value of Oxidative Stress Biomarkers in Acute Myeloid Leukemia: A Systematic Review

Sep 2026 · The Scientist · Vol 8, pp. 253 · 0 citations

TL;DR

These biomarkers may have prognostic value, particularly for OS, but heterogeneity limits clinical application, and standardized validation and integration into applicable risk models are required.

Abstract

Background: Acute myeloid leukemia (AML) is biologically heterogeneous and associated with poor outcomes. Although oxidative stress contributes to AML pathogenesis, the prognostic value of related biomarkers remains uncertain. Objective: This study aimed to evaluate associations between oxidative stress-related biomarkers and prognosis in adults with AML, focusing on overall survival (OS), complete remission (CR), relapse, relapse-free survival (RFS), event-free survival (EFS), and early mortality. Methods: This PRISMA-compliant systematic review was registered in PROSPERO (CRD420261364956). MEDLINE/PubMed, Scopus, Cochrane Library, Science Citation Index, ClinicalTrials.gov, and WHO ICTRP were searched from January 2015 through 31 July 2026. Eligible studies included adults with AML, oxidative stress-related biomarkers measured in biological samples, and extractable prognostic data. Risk of bias was assessed using QUIPS and certainty of evidence using GRADE. Results: Thirteen of 537 records met the inclusion criteria; 203 supplementary reports yielded no additional studies. Investigated biomarkers included ROS-related phenotypes, antioxidant and redox-regulatory genes, glutathione metabolism, iron/inflammation-related markers, and oxidative DNA damage indicators. Adverse outcomes were associated with ROS-related phenotypes, combined ROS/aldehyde dehydrogenase activity, GPX3, GSTP1, ferritin, gamma-glutamyl transpeptidase-to-albumin ratio, SOD1, and glutathione-related metabolic profiles. Conclusions: These biomarkers may have prognostic value, particularly for OS, but heterogeneity limits clinical application. Standardized validation and integration into applicable risk models are required.

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