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Acute Kidney Injury

Comprehensive analysis of cuproptosis-related genes in immune infiltration and development of a novel diagnostic model for acute kidney injury

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Article: 2325035 | Received 29 Aug 2023, Accepted 25 Feb 2024, Published online: 27 Mar 2024
 

Abstract

Background

Acute kidney injury (AKI) represents a diverse range of conditions characterized by high incidence and mortality rates, and it is mainly associated with immune-mediated mechanisms and mitochondrial metabolism dysfunction. Cuproptosis, a recently identified form of programmed cell death dependent on copper, is closely linked to mitochondrial respiration and contributes to various diseases. Our study aimed to investigate the involvement of cuproptosis-related genes (CRGs) in AKI.

Methods

Identification of CRGs was conducted using differential expression analysis, and subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using human sequencing profiles. Utilizing CIBERSORT algorithm, receiver operating characteristic (ROC) curve analysis, nomogram development, and decision curve analysis (DCA), the association among immune scores, CRGs, and the diagnostic value of these genes was explored.

Results

Notably, six CRGs (FDX1, DLD, DLAT, DBT, PDHA1, and ATP7A) were identified as significant differentiators between AKI and non-AKI groups. The ROC curve, based on these six genes, demonstrated an AUC value of 0.917, which was further validated using an additional dataset with an AUC value of 0.902. Nomogram and DCA further confirmed the accuracy of the model in predicting the risk of AKI.

Conclusion

This study elucidated the role of cuproptosis in AKI and revealed the association between CRGs and infiltrated immune cells through comprehensive bioinformatic techniques. The six-gene cuproptosis-related signature exhibited remarkable predictive efficiency for AKI.

Acknowledgments

Not applicable.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of Data and materials

Public datasets were utilized in this study and can be accessed here:(https://www.ncbi.nlm.nih.gov/geo/), GSE139061, GSE30718.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

Yajing Li: Conceptualization, Methodology, Data Curation, Formal analysis, Writing—Original Draft. Yingxue Ding: Project administration, Writing—Review & Editing, Supervision. The author(s) read and approved the final manuscript.