Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis
ObjectiveDiabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes rela...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2022.918657/full |
| _version_ | 1811264461806239744 |
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| author | Shaojie Fu Yanli Cheng Xueyao Wang Jingda Huang Sensen Su Hao Wu Jinyu Yu Zhonggao Xu |
| author_facet | Shaojie Fu Yanli Cheng Xueyao Wang Jingda Huang Sensen Su Hao Wu Jinyu Yu Zhonggao Xu |
| author_sort | Shaojie Fu |
| collection | DOAJ |
| description | ObjectiveDiabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.Materials and methodsGene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.ResultsA total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.ConclusionDUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration. |
| first_indexed | 2024-04-12T20:04:40Z |
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| id | doaj.art-7d2a03ff5d1440af89423a2bcd958a55 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-12T20:04:40Z |
| publishDate | 2022-09-01 |
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| series | Frontiers in Medicine |
| spelling | doaj.art-7d2a03ff5d1440af89423a2bcd958a552022-12-22T03:18:26ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-09-01910.3389/fmed.2022.918657918657Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysisShaojie Fu0Yanli Cheng1Xueyao Wang2Jingda Huang3Sensen Su4Hao Wu5Jinyu Yu6Zhonggao Xu7Department of Nephrology, The First Hospital of Jilin University, Changchun, ChinaDepartment of Nephrology, The First Hospital of Jilin University, Changchun, ChinaDepartment of Nephrology, The First Hospital of Jilin University, Changchun, ChinaDepartment of Nephrology, The First Hospital of Jilin University, Changchun, ChinaDepartment of Nephrology, The First Hospital of Jilin University, Changchun, ChinaDepartment of Nephrology, The First Hospital of Jilin University, Changchun, ChinaDepartment of Urology, The First Hospital of Jilin University, Changchun, ChinaDepartment of Nephrology, The First Hospital of Jilin University, Changchun, ChinaObjectiveDiabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.Materials and methodsGene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.ResultsA total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.ConclusionDUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.https://www.frontiersin.org/articles/10.3389/fmed.2022.918657/fulldiabetic kidney diseaseimmune infiltrationdiagnostic biomarkerbioinformatic analysismachine learning strategy |
| spellingShingle | Shaojie Fu Yanli Cheng Xueyao Wang Jingda Huang Sensen Su Hao Wu Jinyu Yu Zhonggao Xu Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis Frontiers in Medicine diabetic kidney disease immune infiltration diagnostic biomarker bioinformatic analysis machine learning strategy |
| title | Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis |
| title_full | Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis |
| title_fullStr | Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis |
| title_full_unstemmed | Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis |
| title_short | Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis |
| title_sort | identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis |
| topic | diabetic kidney disease immune infiltration diagnostic biomarker bioinformatic analysis machine learning strategy |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2022.918657/full |
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