Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells
BackgroundRenal fibrosis is a physiological and pathological characteristic of chronic kidney disease (CKD) to end-stage renal disease. Since renal biopsy is the gold standard for evaluating renal fibrosis, there is an urgent need for additional non-invasive diagnostic biomarkers.MethodsWe used R pa...
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1161436/full |
| _version_ | 1827946136018616320 |
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| author | Yangyang Guo Ziwei Yuan Zujian Hu Yuanyuan Gao Hangcheng Guo Hengyue Zhu Kai Hong Kenan Cen Yifeng Mai Yongheng Bai Xuejia Yang |
| author_facet | Yangyang Guo Ziwei Yuan Zujian Hu Yuanyuan Gao Hangcheng Guo Hengyue Zhu Kai Hong Kenan Cen Yifeng Mai Yongheng Bai Xuejia Yang |
| author_sort | Yangyang Guo |
| collection | DOAJ |
| description | BackgroundRenal fibrosis is a physiological and pathological characteristic of chronic kidney disease (CKD) to end-stage renal disease. Since renal biopsy is the gold standard for evaluating renal fibrosis, there is an urgent need for additional non-invasive diagnostic biomarkers.MethodsWe used R package “limma” to screen out differently expressed genes (DEGs) based on Epithelial-mesenchymal transformation (EMT), and carried out the protein interaction network and GO, KEGG enrichment analysis of DEGs. Secondly, the least absolute shrinkage and selection operator (LASSO), random forest tree (RF), and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify candidate diagnostic genes. ROC curves were plotted to evaluate the clinical diagnostic value of these genes. In addition, mRNA expression levels of candidate diagnostic genes were analyzed in control samples and renal fibrosis samples. CIBERSORT algorithm was used to evaluate immune cells level. Additionally, gene set enrichment analysis (GSEA) and drug sensitivity were conducted.ResultsAfter obtaining a total of 24 DEGs, we discovered that they were mostly involved in several immunological and inflammatory pathways, including NF-KappaB signaling, AGE-RAGE signaling, and TNF signaling. Five genes (COL4A2, CXCL1, TIMP1, VCAM1, and VEGFA) were subsequently identified as biomarkers for renal fibrosis through machine learning, and their expression levels were confirmed by validation cohort data sets and in vitro RT-qPCR experiment. The AUC values of these five genes demonstrated significant clinical diagnostic value in both the training and validation sets. After that, CIBERSORT analysis showed that these biomarkers were strongly associated with immune cell content in renal fibrosis patients. GSEA also identifies the potential roles of these diagnostic genes. Additionally, diagnostic candidate genes were found to be closely related to drug sensitivity. Finally, a nomogram for diagnosing renal fibrosis was developed.ConclusionCOL4A2, CXCL1, TIMP1, VCAM1, and VEGFA are promising diagnostic biomarkers of tissue and serum for renal fibrosis. |
| first_indexed | 2024-03-13T10:54:25Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-13T10:54:25Z |
| publishDate | 2023-05-01 |
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| series | Frontiers in Immunology |
| spelling | doaj.art-7ac9f63b3ace48ada6f304c4516e189d2023-05-17T05:36:18ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-05-011410.3389/fimmu.2023.11614361161436Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cellsYangyang Guo0Ziwei Yuan1Zujian Hu2Yuanyuan Gao3Hangcheng Guo4Hengyue Zhu5Kai Hong6Kenan Cen7Yifeng Mai8Yongheng Bai9Xuejia Yang10Department of General Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, ChinaKey Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaKey Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaKey Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaKey Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of General Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, ChinaThe Affiliated Hospital of Medical School of Ningbo University, Ningbo, ChinaThe Affiliated Hospital of Medical School of Ningbo University, Ningbo, ChinaKey Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaKey Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaBackgroundRenal fibrosis is a physiological and pathological characteristic of chronic kidney disease (CKD) to end-stage renal disease. Since renal biopsy is the gold standard for evaluating renal fibrosis, there is an urgent need for additional non-invasive diagnostic biomarkers.MethodsWe used R package “limma” to screen out differently expressed genes (DEGs) based on Epithelial-mesenchymal transformation (EMT), and carried out the protein interaction network and GO, KEGG enrichment analysis of DEGs. Secondly, the least absolute shrinkage and selection operator (LASSO), random forest tree (RF), and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify candidate diagnostic genes. ROC curves were plotted to evaluate the clinical diagnostic value of these genes. In addition, mRNA expression levels of candidate diagnostic genes were analyzed in control samples and renal fibrosis samples. CIBERSORT algorithm was used to evaluate immune cells level. Additionally, gene set enrichment analysis (GSEA) and drug sensitivity were conducted.ResultsAfter obtaining a total of 24 DEGs, we discovered that they were mostly involved in several immunological and inflammatory pathways, including NF-KappaB signaling, AGE-RAGE signaling, and TNF signaling. Five genes (COL4A2, CXCL1, TIMP1, VCAM1, and VEGFA) were subsequently identified as biomarkers for renal fibrosis through machine learning, and their expression levels were confirmed by validation cohort data sets and in vitro RT-qPCR experiment. The AUC values of these five genes demonstrated significant clinical diagnostic value in both the training and validation sets. After that, CIBERSORT analysis showed that these biomarkers were strongly associated with immune cell content in renal fibrosis patients. GSEA also identifies the potential roles of these diagnostic genes. Additionally, diagnostic candidate genes were found to be closely related to drug sensitivity. Finally, a nomogram for diagnosing renal fibrosis was developed.ConclusionCOL4A2, CXCL1, TIMP1, VCAM1, and VEGFA are promising diagnostic biomarkers of tissue and serum for renal fibrosis.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1161436/fullrenal fibrosisdiagnostic biomarkersmachine learningimmune cellEMT |
| spellingShingle | Yangyang Guo Ziwei Yuan Zujian Hu Yuanyuan Gao Hangcheng Guo Hengyue Zhu Kai Hong Kenan Cen Yifeng Mai Yongheng Bai Xuejia Yang Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells Frontiers in Immunology renal fibrosis diagnostic biomarkers machine learning immune cell EMT |
| title | Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells |
| title_full | Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells |
| title_fullStr | Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells |
| title_full_unstemmed | Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells |
| title_short | Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells |
| title_sort | diagnostic model constructed by five emt related genes for renal fibrosis and reflecting the condition of immune related cells |
| topic | renal fibrosis diagnostic biomarkers machine learning immune cell EMT |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1161436/full |
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