Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms
Parkinson’s disease (PD) is a common progressive neurodegenerative disorder. Various evidence has revealed the possible penetration of peripheral immune cells in the substantia nigra, which may be essential for PD. Our study uses machine learning (ML) to screen for potential PD genetic biomarkers. G...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/13/2/175 |
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| author | Yiwen Bao Lufeng Wang Fei Yu Jie Yang Dongya Huang |
| author_facet | Yiwen Bao Lufeng Wang Fei Yu Jie Yang Dongya Huang |
| author_sort | Yiwen Bao |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is a common progressive neurodegenerative disorder. Various evidence has revealed the possible penetration of peripheral immune cells in the substantia nigra, which may be essential for PD. Our study uses machine learning (ML) to screen for potential PD genetic biomarkers. Gene expression profiles were screened from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) were selected for the enrichment analysis. A protein–protein interaction (PPI) network was built with the STRING database (Search Tool for the Retrieval of Interacting Genes), and two ML approaches, namely least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), were employed to identify candidate genes. The external validation dataset further tested the expression degree and diagnostic value of candidate biomarkers. To assess the validity of the diagnosis, we determined the receiver operating characteristic (ROC) curve. A convolution tool was employed to evaluate the composition of immune cells by CIBERSORT, and we performed correlation analyses on the basis of the training dataset. Twenty-seven DEGs were screened in the PD and control samples. Our results from the enrichment analysis showed a close association with inflammatory and immune-associated diseases. Both the LASSO and SVM algorithms screened eight and six characteristic genes. AGTR1, GBE1, TPBG, and HSPA6 are overlapping hub genes strongly related to PD. Our results of the area under the ROC (AUC), including AGTR1 (AUC = 0.933), GBE1 (AUC = 0.967), TPBG (AUC = 0.767), and HSPA6 (AUC = 0.633), suggested that these genes have good diagnostic value, and these genes were significantly associated with the degree of immune cell infiltration. AGTR1, GBE1, TPBG, and HSPA6 were identified as potential biomarkers in the diagnosis of PD and provide a novel viewpoint for further study on PD immune mechanism and therapy. |
| first_indexed | 2024-03-11T09:04:52Z |
| format | Article |
| id | doaj.art-4a29e2abf50343b68fa211b496a16946 |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-11T09:04:52Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-4a29e2abf50343b68fa211b496a169462023-11-16T19:27:14ZengMDPI AGBrain Sciences2076-34252023-01-0113217510.3390/brainsci13020175Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM AlgorithmsYiwen Bao0Lufeng Wang1Fei Yu2Jie Yang3Dongya Huang4Tongji University School of Medicine, East Hospital, Department of Neurology, Tongji University, Shanghai 200070, ChinaTongji University School of Medicine, East Hospital, Department of Neurology, Tongji University, Shanghai 200070, ChinaTongji University School of Medicine, East Hospital, Department of Neurology, Tongji University, Shanghai 200070, ChinaTongji University School of Medicine, East Hospital, Department of Neurology, Tongji University, Shanghai 200070, ChinaTongji University School of Medicine, East Hospital, Department of Neurology, Tongji University, Shanghai 200070, ChinaParkinson’s disease (PD) is a common progressive neurodegenerative disorder. Various evidence has revealed the possible penetration of peripheral immune cells in the substantia nigra, which may be essential for PD. Our study uses machine learning (ML) to screen for potential PD genetic biomarkers. Gene expression profiles were screened from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) were selected for the enrichment analysis. A protein–protein interaction (PPI) network was built with the STRING database (Search Tool for the Retrieval of Interacting Genes), and two ML approaches, namely least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), were employed to identify candidate genes. The external validation dataset further tested the expression degree and diagnostic value of candidate biomarkers. To assess the validity of the diagnosis, we determined the receiver operating characteristic (ROC) curve. A convolution tool was employed to evaluate the composition of immune cells by CIBERSORT, and we performed correlation analyses on the basis of the training dataset. Twenty-seven DEGs were screened in the PD and control samples. Our results from the enrichment analysis showed a close association with inflammatory and immune-associated diseases. Both the LASSO and SVM algorithms screened eight and six characteristic genes. AGTR1, GBE1, TPBG, and HSPA6 are overlapping hub genes strongly related to PD. Our results of the area under the ROC (AUC), including AGTR1 (AUC = 0.933), GBE1 (AUC = 0.967), TPBG (AUC = 0.767), and HSPA6 (AUC = 0.633), suggested that these genes have good diagnostic value, and these genes were significantly associated with the degree of immune cell infiltration. AGTR1, GBE1, TPBG, and HSPA6 were identified as potential biomarkers in the diagnosis of PD and provide a novel viewpoint for further study on PD immune mechanism and therapy.https://www.mdpi.com/2076-3425/13/2/175Parkinson’s diseaseimmune infiltratesleast absolute shrinkage and selection operatorsupport vector machine |
| spellingShingle | Yiwen Bao Lufeng Wang Fei Yu Jie Yang Dongya Huang Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms Brain Sciences Parkinson’s disease immune infiltrates least absolute shrinkage and selection operator support vector machine |
| title | Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms |
| title_full | Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms |
| title_fullStr | Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms |
| title_full_unstemmed | Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms |
| title_short | Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms |
| title_sort | parkinson s disease gene biomarkers screened by the lasso and svm algorithms |
| topic | Parkinson’s disease immune infiltrates least absolute shrinkage and selection operator support vector machine |
| url | https://www.mdpi.com/2076-3425/13/2/175 |
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