Exploring and validating key genetic biomarkers for diagnosis of Parkinson's disease
Background: Parkinson's disease (PD) is a neurological condition characterized by complex genetic basic, and the reliable diagnosis of PD remained limited. Objective: To identify genes crucial to PD and assess their potential as diagnostic markers. Methods: Differentially expressed genes (DEGs)...
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Elsevier
2025-01-01
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| Series: | Brain Research Bulletin |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0361923024002995 |
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| author | Wen-bin Teng Hao-wei Deng Bing-hua Lv Shao-dan Zhou Bin-ru Li Rui-ting Hu |
| author_facet | Wen-bin Teng Hao-wei Deng Bing-hua Lv Shao-dan Zhou Bin-ru Li Rui-ting Hu |
| author_sort | Wen-bin Teng |
| collection | DOAJ |
| description | Background: Parkinson's disease (PD) is a neurological condition characterized by complex genetic basic, and the reliable diagnosis of PD remained limited. Objective: To identify genes crucial to PD and assess their potential as diagnostic markers. Methods: Differentially expressed genes (DEGs) were screened from the PD tissue dataset and blood dataset. Two machine learning methods were used to identify key PD-related genes. The genes were validated in an independent dataset. Further validation using 120 peripheral blood mononuclear cells (PBMCs) from PD patients. The clinical significance and the diagnostic value of the genes was determined. The function of genes was analyzed and verified by cells experiments. Results: Thirteen common upregulated genes were identified between PD tissue dataset and blood dataset. Two machine learning methods identify three key PD-related genes (GPX2, CR1, ZNF556). An independent dataset and PBMCs samples results showed increased expression in PD patients. Clinical analysis showed that GPX2 and CR1 expression correlated with early-stage PD. The validated dataset of blood samples revealed each three gene showed moderate diagnostic potential for PD, with combined analysis outperforming individual gene analysis (AUC:0.701). The PBMCs samples showed similar diagnostic value of each gene, and the combination of the three genes presented better diagnostic value (AUC:0.801). Functional studies highlighted the involvement of these genes in key pathways in PD pathology. The results of SH-SY5Y cells showed that these three genes increased from PD cell model. Conclusions: GPX2, CR1, ZNF556 were critical to the development of PD and might serve as diagnostic markers for PD. |
| format | Article |
| id | doaj-art-efcd2d3c5c2b4e5fa295411a1ccd3e0c |
| institution | Kabale University |
| issn | 1873-2747 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Brain Research Bulletin |
| spelling | doaj-art-efcd2d3c5c2b4e5fa295411a1ccd3e0c2025-01-10T04:37:01ZengElsevierBrain Research Bulletin1873-27472025-01-01220111165Exploring and validating key genetic biomarkers for diagnosis of Parkinson's diseaseWen-bin Teng0Hao-wei Deng1Bing-hua Lv2Shao-dan Zhou3Bin-ru Li4Rui-ting Hu5Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning 530001, ChinaDepartment of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, ChinaDepartment of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, ChinaDepartment of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning 530001, ChinaDepartment of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning 530001, China; Correspondence to: Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, No. 239 Minxiudong Road, Nanning 530001, ChinaDepartment of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning 530001, China; Correspondence to: Department of Neurology, Minzu Hospital of Guangxi Zhuang Autonomous Region, No. 239 Minxiudong Road, Nanning 530001, ChinaBackground: Parkinson's disease (PD) is a neurological condition characterized by complex genetic basic, and the reliable diagnosis of PD remained limited. Objective: To identify genes crucial to PD and assess their potential as diagnostic markers. Methods: Differentially expressed genes (DEGs) were screened from the PD tissue dataset and blood dataset. Two machine learning methods were used to identify key PD-related genes. The genes were validated in an independent dataset. Further validation using 120 peripheral blood mononuclear cells (PBMCs) from PD patients. The clinical significance and the diagnostic value of the genes was determined. The function of genes was analyzed and verified by cells experiments. Results: Thirteen common upregulated genes were identified between PD tissue dataset and blood dataset. Two machine learning methods identify three key PD-related genes (GPX2, CR1, ZNF556). An independent dataset and PBMCs samples results showed increased expression in PD patients. Clinical analysis showed that GPX2 and CR1 expression correlated with early-stage PD. The validated dataset of blood samples revealed each three gene showed moderate diagnostic potential for PD, with combined analysis outperforming individual gene analysis (AUC:0.701). The PBMCs samples showed similar diagnostic value of each gene, and the combination of the three genes presented better diagnostic value (AUC:0.801). Functional studies highlighted the involvement of these genes in key pathways in PD pathology. The results of SH-SY5Y cells showed that these three genes increased from PD cell model. Conclusions: GPX2, CR1, ZNF556 were critical to the development of PD and might serve as diagnostic markers for PD.http://www.sciencedirect.com/science/article/pii/S0361923024002995Parkinson's diseaseGenetic markersDiagnosisMachine learning methodsBlood |
| spellingShingle | Wen-bin Teng Hao-wei Deng Bing-hua Lv Shao-dan Zhou Bin-ru Li Rui-ting Hu Exploring and validating key genetic biomarkers for diagnosis of Parkinson's disease Brain Research Bulletin Parkinson's disease Genetic markers Diagnosis Machine learning methods Blood |
| title | Exploring and validating key genetic biomarkers for diagnosis of Parkinson's disease |
| title_full | Exploring and validating key genetic biomarkers for diagnosis of Parkinson's disease |
| title_fullStr | Exploring and validating key genetic biomarkers for diagnosis of Parkinson's disease |
| title_full_unstemmed | Exploring and validating key genetic biomarkers for diagnosis of Parkinson's disease |
| title_short | Exploring and validating key genetic biomarkers for diagnosis of Parkinson's disease |
| title_sort | exploring and validating key genetic biomarkers for diagnosis of parkinson s disease |
| topic | Parkinson's disease Genetic markers Diagnosis Machine learning methods Blood |
| url | http://www.sciencedirect.com/science/article/pii/S0361923024002995 |
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