Evidence map›Paper›PMID 39572917›Full record

ArticleCurrent medicinal chemistry2025

Development and Validation of a Diagnostic Model for AKI Based on the Analysis of Ferroptosis-related Genes.

Hengyue Zhu, Xuejia Yang, Ziwei Yuan, Zujian Hu, Yangyang Guo, Yongheng Bai, Jingzong Zhou

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Article in Current medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Hengyue ZhuDepartment of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Xuejia YangKey Laboratory of Diagnosis and Treatment of Severe Hepato- Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Ziwei YuanKey Laboratory of Diagnosis and Treatment of Severe Hepato- Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Zujian HuKey Laboratory of Diagnosis and Treatment of Severe Hepato- Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Yangyang GuoDepartment of General Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China.
Yongheng BaiKey Laboratory of Diagnosis and Treatment of Severe Hepato- Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Jingzong ZhouDepartment of Endocrinology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China.

Funding

Ningbo University Institute of Geriatrics LNBYJS-2021
6 · The paper itself

Abstract

backgroundAcute kidney injury (AKI) is a common renal condition associated with various factors, including pre-renal, post-renal, and renal causes, with ischemia- reperfusion being a frequent contributor leading to tubular injury. Early identification of AKI is crucial but remains challenging.

methodsThis study explored the molecular signature of AKI using gene microarray data from the GEO dataset, focusing on identifying ferroptosis-related features through three machine-learning algorithms. We also validated potential biomarkers through a hypoxia/ reoxygenation model.

resultsROC curves, expression differences, and associations with immune cells were analyzed for the three markers to confirm their potential as AKI biomarkers, each demonstrating strong diagnostic ability. Combining these markers proved more effective.

conclusionThe combination of AEBP2, MDM2, and NR4A1 as diagnostic biomarkers for AKI not only enhances detection capability but also holds promise as a significant tool in clinical practice, providing patients with diagnostic and therapeutic guidance.

Indexed as

Acute Kidney InjuryFerroptosisBiomarkersHumansMachine LearningProto-Oncogene Proteins c-mdm2BiomarkersProto-Oncogene Proteins c-mdm2Acute kidney injurybiomarkerdiagnoseGEO dataseimmune cellsrenal tubules.

Identifiers

PMID39572917

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.