Identification of ferroptosis related biomarkers and immune infiltration in Parkinson’s disease by integrated bioinformatic analysis
Abstract Background Increasing evidence has indicated that ferroptosis engages in the progression of Parkinson’s disease (PD). This study aimed to explore the role of ferroptosis-related genes (FRGs), immune infiltration and immune checkpoint genes (ICGs) in the pathogenesis and development of PD. M...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12920-023-01481-3 |
| _version_ | 1797863261986619392 |
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| author | Na Xing Ziye Dong Qiaoli Wu Yufeng Zhang Pengcheng Kan Yuan Han Xiuli Cheng Yaru Wang Biao Zhang |
| author_facet | Na Xing Ziye Dong Qiaoli Wu Yufeng Zhang Pengcheng Kan Yuan Han Xiuli Cheng Yaru Wang Biao Zhang |
| author_sort | Na Xing |
| collection | DOAJ |
| description | Abstract Background Increasing evidence has indicated that ferroptosis engages in the progression of Parkinson’s disease (PD). This study aimed to explore the role of ferroptosis-related genes (FRGs), immune infiltration and immune checkpoint genes (ICGs) in the pathogenesis and development of PD. Methods The microarray data of PD patients and healthy controls (HC) from the Gene Expression Omnibus (GEO) database was downloaded. Weighted gene co-expression network analysis (WGCNA) was processed to identify the significant modules related to PD in the GSE18838 dataset. Machine learning algorithms were used to screen the candidate biomarkers based on the intersect between WGCNA, FRGs and differentially expressed genes. Enrichment analysis of GSVA, GSEA, GO, KEGG, and immune infiltration, group comparison of ICGs were also performed. Next, candidate biomarkers were validated in clinical samples by ELISA and receiver operating characteristic curve (ROC) was used to assess diagnose ability. Results In this study, FRGs had correlations with ICGs, immune infiltration. Then, plasma levels of LPIN1 in PD was significantly lower than that in healthy controls, while the expression of TNFAIP3 was higher in PD in comparison with HC. ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750–0.916). Moreover, each biomarker alone could discriminate the PD from HC (LPIN1: AUC = 0.754, 95% CI: 0.659–0.849; TNFAIP3: AUC = 0.754, 95% CI: 0.660–0.849). For detection of early PD from HC, the model of combination maintained diagnostic accuracy with an AUC of 0.831 (95% CI: 0.734–0.927), LPIN1 also performed well in distinguishing the early PD from HC (AUC = 0.817, 95% CI: 0.717–0.917). However, the diagnostic efficacy was relatively poor in distinguishing the early from middle-advanced PD patients. Conclusion The combination model composed of LPIN1 and TNFAIP3, and each biomarker may serve as an efficient tool for distinguishing PD from HC. |
| first_indexed | 2024-04-09T22:33:47Z |
| format | Article |
| id | doaj.art-4a7389e3564a4308b3d73f0401b46efe |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-04-09T22:33:47Z |
| publishDate | 2023-03-01 |
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| series | BMC Medical Genomics |
| spelling | doaj.art-4a7389e3564a4308b3d73f0401b46efe2023-03-22T12:37:57ZengBMCBMC Medical Genomics1755-87942023-03-0116111710.1186/s12920-023-01481-3Identification of ferroptosis related biomarkers and immune infiltration in Parkinson’s disease by integrated bioinformatic analysisNa Xing0Ziye Dong1Qiaoli Wu2Yufeng Zhang3Pengcheng Kan4Yuan Han5Xiuli Cheng6Yaru Wang7Biao Zhang8Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical UniversityClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical UniversityTianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin Huanhu HospitalClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical UniversityDepartment of Clinical Laboratory, Tianjin Huanhu HospitalDepartment of Clinical Laboratory, Tianjin Huanhu HospitalDepartment of Clinical Laboratory, Tianjin Huanhu HospitalChu Hsien-I Memorial Hospital (Metabolic Diseases Hospital) of Tianjin Medical UniversityClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical UniversityAbstract Background Increasing evidence has indicated that ferroptosis engages in the progression of Parkinson’s disease (PD). This study aimed to explore the role of ferroptosis-related genes (FRGs), immune infiltration and immune checkpoint genes (ICGs) in the pathogenesis and development of PD. Methods The microarray data of PD patients and healthy controls (HC) from the Gene Expression Omnibus (GEO) database was downloaded. Weighted gene co-expression network analysis (WGCNA) was processed to identify the significant modules related to PD in the GSE18838 dataset. Machine learning algorithms were used to screen the candidate biomarkers based on the intersect between WGCNA, FRGs and differentially expressed genes. Enrichment analysis of GSVA, GSEA, GO, KEGG, and immune infiltration, group comparison of ICGs were also performed. Next, candidate biomarkers were validated in clinical samples by ELISA and receiver operating characteristic curve (ROC) was used to assess diagnose ability. Results In this study, FRGs had correlations with ICGs, immune infiltration. Then, plasma levels of LPIN1 in PD was significantly lower than that in healthy controls, while the expression of TNFAIP3 was higher in PD in comparison with HC. ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750–0.916). Moreover, each biomarker alone could discriminate the PD from HC (LPIN1: AUC = 0.754, 95% CI: 0.659–0.849; TNFAIP3: AUC = 0.754, 95% CI: 0.660–0.849). For detection of early PD from HC, the model of combination maintained diagnostic accuracy with an AUC of 0.831 (95% CI: 0.734–0.927), LPIN1 also performed well in distinguishing the early PD from HC (AUC = 0.817, 95% CI: 0.717–0.917). However, the diagnostic efficacy was relatively poor in distinguishing the early from middle-advanced PD patients. Conclusion The combination model composed of LPIN1 and TNFAIP3, and each biomarker may serve as an efficient tool for distinguishing PD from HC.https://doi.org/10.1186/s12920-023-01481-3Parkinson’s diseaseFerroptosisImmune infiltrationImmune checkpoint geneELISABioinformatic |
| spellingShingle | Na Xing Ziye Dong Qiaoli Wu Yufeng Zhang Pengcheng Kan Yuan Han Xiuli Cheng Yaru Wang Biao Zhang Identification of ferroptosis related biomarkers and immune infiltration in Parkinson’s disease by integrated bioinformatic analysis BMC Medical Genomics Parkinson’s disease Ferroptosis Immune infiltration Immune checkpoint gene ELISA Bioinformatic |
| title | Identification of ferroptosis related biomarkers and immune infiltration in Parkinson’s disease by integrated bioinformatic analysis |
| title_full | Identification of ferroptosis related biomarkers and immune infiltration in Parkinson’s disease by integrated bioinformatic analysis |
| title_fullStr | Identification of ferroptosis related biomarkers and immune infiltration in Parkinson’s disease by integrated bioinformatic analysis |
| title_full_unstemmed | Identification of ferroptosis related biomarkers and immune infiltration in Parkinson’s disease by integrated bioinformatic analysis |
| title_short | Identification of ferroptosis related biomarkers and immune infiltration in Parkinson’s disease by integrated bioinformatic analysis |
| title_sort | identification of ferroptosis related biomarkers and immune infiltration in parkinson s disease by integrated bioinformatic analysis |
| topic | Parkinson’s disease Ferroptosis Immune infiltration Immune checkpoint gene ELISA Bioinformatic |
| url | https://doi.org/10.1186/s12920-023-01481-3 |
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