Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease
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...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402500252X |
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| author | Guoqing Zhang Hongyan Luo Xiaohua Lu Yonghua Liu Mei Wang Bo Li Haixia Lu Yali Zheng |
| author_facet | Guoqing Zhang Hongyan Luo Xiaohua Lu Yonghua Liu Mei Wang Bo Li Haixia Lu Yali Zheng |
| author_sort | Guoqing Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e0a9b2eab4f34678866ea41c30b54d27 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-e0a9b2eab4f34678866ea41c30b54d272025-02-02T05:28:26ZengElsevierHeliyon2405-84402025-01-01112e41872Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney diseaseGuoqing Zhang0Hongyan Luo1Xiaohua Lu2Yonghua Liu3Mei Wang4Bo Li5Haixia Lu6Yali Zheng7Department of Nephrology, Ningxia Medical University Affiliated People's Hospital of Autonomous Region, Yinchuan, China; The Third Clinical Medical College, Ningxia Medical University, Yinchuan, ChinaDepartment of Nephrology, Ningxia Medical University Affiliated People's Hospital of Autonomous Region, Yinchuan, China; The Third Clinical Medical College, Ningxia Medical University, Yinchuan, ChinaDepartment of Nephrology, Ningxia Medical University Affiliated People's Hospital of Autonomous Region, Yinchuan, China; The Third Clinical Medical College, Ningxia Medical University, Yinchuan, ChinaDepartment of Nephrology, Ningxia Medical University Affiliated People's Hospital of Autonomous Region, Yinchuan, China; The Third Clinical Medical College, Ningxia Medical University, Yinchuan, ChinaDepartment of Nephrology, Ningxia Medical University Affiliated People's Hospital of Autonomous Region, Yinchuan, China; The Third Clinical Medical College, Ningxia Medical University, Yinchuan, ChinaDepartment of Nephrology, Ningxia Medical University Affiliated People's Hospital of Autonomous Region, Yinchuan, China; Department of Nephrology Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, ChinaDepartment of Nephrology, Ningxia Medical University Affiliated People's Hospital of Autonomous Region, Yinchuan, China; The Third Clinical Medical College, Ningxia Medical University, Yinchuan, ChinaDepartment of Nephrology, Ningxia Medical University Affiliated People's Hospital of Autonomous Region, Yinchuan, China; The Third Clinical Medical College, Ningxia Medical University, Yinchuan, China; Corresponding author. No.301 Zhengyuan North Street, Yinchuan, Ningxia Hui Autonomous Region, 750001, China.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.http://www.sciencedirect.com/science/article/pii/S240584402500252XChronic kidney diseaseAmino acid metabolismImmune cell infiltrationMachineLearningBiomarkers |
| spellingShingle | Guoqing Zhang Hongyan Luo Xiaohua Lu Yonghua Liu Mei Wang Bo Li Haixia Lu Yali Zheng Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease Heliyon Chronic kidney disease Amino acid metabolism Immune cell infiltration Machine Learning Biomarkers |
| title | Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease |
| title_full | Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease |
| title_fullStr | Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease |
| title_full_unstemmed | Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease |
| title_short | Machine learning-based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease |
| title_sort | machine learning based identification and validation of amino acid metabolism related genes as novel biomarkers in chronic kidney disease |
| topic | Chronic kidney disease Amino acid metabolism Immune cell infiltration Machine Learning Biomarkers |
| url | http://www.sciencedirect.com/science/article/pii/S240584402500252X |
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