Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease

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Abstract

Objectives

Chronic kidney disease (CKD) is a progressive illness with a high rate of morbidity and mortality with no proven therapy. Alterations of amino acid(AA) metabolism are associated with the incidence and progression of CKD. To characterize the potential value of AA metabolism related genes in the diagnosis and progression of CKD.

Methods

We filtered the key genes associated with AA metabolism based on the least absolute shrinkage and selection operator (LASSO) and SVM algorithm. Then, we constructed logistic regression models and evaluated the accuracy and specificity by nomogram analysis and DCA. Also, we mapped the ROC curves.Meanwhile, in order to determine the underlying mechanism and relevant biological features of CKD, we conducted differential analysis between high and low risk subgroups in CKD. Moreover,we employed ssGSEA algorithm to evaluate the infiltration abundance of immune cells and calculated the correlation among the immune cells with the key genes. Finally,we validated the expression and clinical relevance of amino acid metabolism key genes via cultured cells and clinical data. A total of six key genes related to amino acid metabolism were identified, including ALDH18A1, CENPF, CSAD, CTH, CYP27B1, HBB.

Results

All six genes exhibited promising diagnostic capabilities (AUC:0.7 to 0.9). Immune cells such as Activated CD4 ⁺ T cells, Regulatory T cells, Immature B cells and MDSC,etc.infiltrated differentially in the high and low risk groups of CKD. There were correlations between immune cells abundance and the expression of key genes. All key genes correlated significantly with markers of kidney injury, such as eGFR and serum creatinine. The expression of ALDH18A1, CENPF were increased while CSAD, CTH and CYP27B1 were decreased in HK-2 cells cultured with indole sulfate.

Conclusions

Our study identified key genes involved in amino acid metabolism associated with immune cells infiltration and renal function in CKD, which may be potential biomarkers for the diagnosis and prognosis of CKD.

Related collections

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Author and article information

Contributors

Yali Zheng

Journal

Journal ID (nlm-ta): Heliyon

Journal ID (iso-abbrev): Heliyon

Title: Heliyon

Publisher: Elsevier

ISSN (Electronic): 2405-8440

Publication date PMC-release: 10 January 2025

Publication date Collection: 30 January 2025

Publication date (Electronic): 10 January 2025

Volume: 11

Issue: 2

Electronic Location Identifier: e41872

Affiliations

[a ]Department of Nephrology, Ningxia Medical University Affiliated People's Hospital of Autonomous Region, Yinchuan, China

[b ]The Third Clinical Medical College, Ningxia Medical University, Yinchuan, China

[c ]Department of Nephrology Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China

Author notes

[* ]Corresponding author. No.301 Zhengyuan North Street, Yinchuan, Ningxia Hui Autonomous Region, 750001, China. zhengyali@ 123456nxmu.edu.cn

[1]

Equal contributors.

Article

Publisher Item ID: S2405-8440(25)00252-X Publisher ID: e41872

DOI: 10.1016/j.heliyon.2025.e41872

PMC ID: 11786826

PubMed ID: 39897884

SO-VID: 9b89dad7-f3ed-4324-8231-78d529a45653

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 1 August 2024

Date revision received : 3 January 2025

Date accepted : 9 January 2025