Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis
Abstract Background Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascula...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-02-01
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| Series: | European Journal of Medical Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40001-023-01043-4 |
| _version_ | 1797865223778992128 |
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| author | Chunjiang Liu Liming Tang Yue Zhou Xiaoqi Tang Gang Zhang Qin Zhu Yufei Zhou |
| author_facet | Chunjiang Liu Liming Tang Yue Zhou Xiaoqi Tang Gang Zhang Qin Zhu Yufei Zhou |
| author_sort | Chunjiang Liu |
| collection | DOAJ |
| description | Abstract Background Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascular and cerebrovascular complications are more likely to occur. This study aimed to identify diagnostic biomarkers in uremic patients with unstable carotid plaques (USCPs). Methods Four microarray datasets (GSE37171, GSE41571, GSE163154, and GSE28829) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in uremia and USCP. Weighted gene co-expression network analysis (WGCNA) was used to determine the respective significant module genes associated with uremia and USCP. Moreover, a protein–protein interaction (PPI) network and three machine learning algorithms were applied to detect potential diagnostic genes. Subsequently, a nomogram and a receiver operating characteristic curve (ROC) were plotted to diagnose USCP with uremia. Finally, immune cell infiltrations were further analyzed. Results Using the Limma package and WGCNA, the intersection of 2795 uremia-related DEGs and 1127 USCP-related DEGs yielded 99 uremia-related DEGs in USCP. 20 genes were selected as candidate hub genes via PPI network construction. Based on the intersection of genes from the three machine learning algorithms, three hub genes (FGR, LCP1, and C5AR1) were identified and used to establish a nomogram that displayed a high diagnostic performance (AUC: 0.989, 95% CI 0.971–1.000). Dysregulated immune cell infiltrations were observed in USCP, showing positive correlations with the three hub genes. Conclusion The current study systematically identified three candidate hub genes (FGR, LCP1, and C5AR1) and established a nomogram to assist in diagnosing USCP with uremia using various bioinformatic analyses and machine learning algorithms. Herein, the findings provide a foothold for future studies on potential diagnostic candidate genes for USCP in uremic patients. Additionally, immune cell infiltration analysis revealed that the dysregulated immune cell proportions were identified, and macrophages could have a critical role in USCP pathogenesis. |
| first_indexed | 2024-04-09T23:05:45Z |
| format | Article |
| id | doaj.art-dea9a19d0de3430c9bb67ac10fc53e12 |
| institution | Directory Open Access Journal |
| issn | 2047-783X |
| language | English |
| last_indexed | 2024-04-09T23:05:45Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj.art-dea9a19d0de3430c9bb67ac10fc53e122023-03-22T10:46:19ZengBMCEuropean Journal of Medical Research2047-783X2023-02-0128111810.1186/s40001-023-01043-4Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysisChunjiang Liu0Liming Tang1Yue Zhou2Xiaoqi Tang3Gang Zhang4Qin Zhu5Yufei Zhou6Department of General Surgery, Division of Vascular Surgery, Shaoxing People’s Hospital (Shaoxing Hospital of Zhejiang University)Department of General Surgery, Division of Vascular Surgery, Shaoxing People’s Hospital (Shaoxing Hospital of Zhejiang University)Department of General Surgery, Division of Vascular Surgery, Shaoxing People’s Hospital (Shaoxing Hospital of Zhejiang University)Department of General Surgery, Division of Vascular Surgery, Shaoxing People’s Hospital (Shaoxing Hospital of Zhejiang University)Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical CenterHepatobiliary CenterKey Laboratory of Liver TransplantationNHC Key Laboratory of Living Donor Liver Transplantation, The First Affiliated Hospital of Nanjing Medical UniversityChinese Academy of Medical SciencesNanjing Medical University)Shanghai Medical College, Fudan UniversityAbstract Background Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascular and cerebrovascular complications are more likely to occur. This study aimed to identify diagnostic biomarkers in uremic patients with unstable carotid plaques (USCPs). Methods Four microarray datasets (GSE37171, GSE41571, GSE163154, and GSE28829) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in uremia and USCP. Weighted gene co-expression network analysis (WGCNA) was used to determine the respective significant module genes associated with uremia and USCP. Moreover, a protein–protein interaction (PPI) network and three machine learning algorithms were applied to detect potential diagnostic genes. Subsequently, a nomogram and a receiver operating characteristic curve (ROC) were plotted to diagnose USCP with uremia. Finally, immune cell infiltrations were further analyzed. Results Using the Limma package and WGCNA, the intersection of 2795 uremia-related DEGs and 1127 USCP-related DEGs yielded 99 uremia-related DEGs in USCP. 20 genes were selected as candidate hub genes via PPI network construction. Based on the intersection of genes from the three machine learning algorithms, three hub genes (FGR, LCP1, and C5AR1) were identified and used to establish a nomogram that displayed a high diagnostic performance (AUC: 0.989, 95% CI 0.971–1.000). Dysregulated immune cell infiltrations were observed in USCP, showing positive correlations with the three hub genes. Conclusion The current study systematically identified three candidate hub genes (FGR, LCP1, and C5AR1) and established a nomogram to assist in diagnosing USCP with uremia using various bioinformatic analyses and machine learning algorithms. Herein, the findings provide a foothold for future studies on potential diagnostic candidate genes for USCP in uremic patients. Additionally, immune cell infiltration analysis revealed that the dysregulated immune cell proportions were identified, and macrophages could have a critical role in USCP pathogenesis.https://doi.org/10.1186/s40001-023-01043-4Unstable carotid plaqueUremiaDiagnostic biomarkerBioinformatics analysisMachine learningImmune cell infiltration |
| spellingShingle | Chunjiang Liu Liming Tang Yue Zhou Xiaoqi Tang Gang Zhang Qin Zhu Yufei Zhou Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis European Journal of Medical Research Unstable carotid plaque Uremia Diagnostic biomarker Bioinformatics analysis Machine learning Immune cell infiltration |
| title | Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis |
| title_full | Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis |
| title_fullStr | Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis |
| title_full_unstemmed | Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis |
| title_short | Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis |
| title_sort | immune associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis |
| topic | Unstable carotid plaque Uremia Diagnostic biomarker Bioinformatics analysis Machine learning Immune cell infiltration |
| url | https://doi.org/10.1186/s40001-023-01043-4 |
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