Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson’s disease

Abstract Background Parkinson’s disease (PD) is a common neurodegenerative disorder. The role of protein post-translational modifications (PTMs), especially small ubiquitin-like modifier (SUMO) conjugation (SUMOylation), in PD pathogenesis remains unclear. This study aimed to investigate the relatio...

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Main Authors: Yifo Wei, Xinning Zhang, Rui Zuo, Wenxin Dang, Lu Chen, Fan Liu, Jia Yao, Weizheng Ran, Zhigang Chen, Xiaoyan Wang, Furong Lv, Yue Yu
Format: Article
Language:English
Published: BMC 2025-08-01
Series:Hereditas
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Online Access:https://doi.org/10.1186/s41065-025-00525-1
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author Yifo Wei
Xinning Zhang
Rui Zuo
Wenxin Dang
Lu Chen
Fan Liu
Jia Yao
Weizheng Ran
Zhigang Chen
Xiaoyan Wang
Furong Lv
Yue Yu
author_facet Yifo Wei
Xinning Zhang
Rui Zuo
Wenxin Dang
Lu Chen
Fan Liu
Jia Yao
Weizheng Ran
Zhigang Chen
Xiaoyan Wang
Furong Lv
Yue Yu
author_sort Yifo Wei
collection DOAJ
description Abstract Background Parkinson’s disease (PD) is a common neurodegenerative disorder. The role of protein post-translational modifications (PTMs), especially small ubiquitin-like modifier (SUMO) conjugation (SUMOylation), in PD pathogenesis remains unclear. This study aimed to investigate the relationship between SUMOylation and PD. Methods The analysis included the GSE22491 dataset, GSE18838 dataset, and 189 SUMO related genes. Differentially expressed genes (DEGs) between the PD group and the control group were identified in GSE22491; these were then intersected with SUMO related genes to identify candidate genes. Machine learning was used to select biomarkers consistent across both datasets, which were validated in GSE6631. Further analyses included back propagation (BP) neural network analysis, enrichment analysis, immune infiltration analysis, regulatory network construction, drug prediction, and molecular docking. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate the biomarkers. Results An overlap analysis of 3,222 DEGs and 189 SUMO related genes identified 25 candidate genes. Subsequent validation using the GSE22491 and GSE18838 datasets narrowed these biomarkers down to SUMO3 and SEH1L, which are involved in pathways (such as the nuclear pore pathway) associated with PD. Significant positive correlations were observed between specific immune cell subtypes and both biomarkers. Based on these correlations, relevant transcription factors (ZNF394, IRF4, FOXM1, EGR1) and drugs (Cianidanol, Methylmethanesulfonate, Valproic acid) were predicted. Additionally, RT-qPCR results confirmed that SUMO3 is significantly downregulated in PD. Conclusion SUMO3 and SEH1L were identified as novel biomarkers for PD, offering potential targets for early diagnosis and therapy in PD.
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institution Kabale University
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spelling doaj-art-481f6902efff422fa2f893cc33444a812025-08-20T03:46:04ZengBMCHereditas1601-52232025-08-01162112010.1186/s41065-025-00525-1Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson’s diseaseYifo Wei0Xinning Zhang1Rui Zuo2Wenxin Dang3Lu Chen4Fan Liu5Jia Yao6Weizheng Ran7Zhigang Chen8Xiaoyan Wang9Furong Lv10Yue Yu11Xi’an Hospital of Traditional Chinese MedicineXi’an Hospital of Traditional Chinese MedicineXi’an Hospital of Traditional Chinese MedicineXi’an Hospital of Traditional Chinese MedicineDongfang Hospital, Beijing University of Chinese MedicineXi’an Hospital of Traditional Chinese MedicineXi’an Hospital of Traditional Chinese MedicineThe First Medical Center of PLA General HospitalDongfang Hospital, Beijing University of Chinese MedicineXi’an Hospital of Traditional Chinese MedicineXi’an Hospital of Traditional Chinese MedicineThe Eighth Medical Center of PLA General HospitalAbstract Background Parkinson’s disease (PD) is a common neurodegenerative disorder. The role of protein post-translational modifications (PTMs), especially small ubiquitin-like modifier (SUMO) conjugation (SUMOylation), in PD pathogenesis remains unclear. This study aimed to investigate the relationship between SUMOylation and PD. Methods The analysis included the GSE22491 dataset, GSE18838 dataset, and 189 SUMO related genes. Differentially expressed genes (DEGs) between the PD group and the control group were identified in GSE22491; these were then intersected with SUMO related genes to identify candidate genes. Machine learning was used to select biomarkers consistent across both datasets, which were validated in GSE6631. Further analyses included back propagation (BP) neural network analysis, enrichment analysis, immune infiltration analysis, regulatory network construction, drug prediction, and molecular docking. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate the biomarkers. Results An overlap analysis of 3,222 DEGs and 189 SUMO related genes identified 25 candidate genes. Subsequent validation using the GSE22491 and GSE18838 datasets narrowed these biomarkers down to SUMO3 and SEH1L, which are involved in pathways (such as the nuclear pore pathway) associated with PD. Significant positive correlations were observed between specific immune cell subtypes and both biomarkers. Based on these correlations, relevant transcription factors (ZNF394, IRF4, FOXM1, EGR1) and drugs (Cianidanol, Methylmethanesulfonate, Valproic acid) were predicted. Additionally, RT-qPCR results confirmed that SUMO3 is significantly downregulated in PD. Conclusion SUMO3 and SEH1L were identified as novel biomarkers for PD, offering potential targets for early diagnosis and therapy in PD.https://doi.org/10.1186/s41065-025-00525-1Parkinson's diseaseSUMOylationBiomarkerMachine learningSUMO3SEH1L
spellingShingle Yifo Wei
Xinning Zhang
Rui Zuo
Wenxin Dang
Lu Chen
Fan Liu
Jia Yao
Weizheng Ran
Zhigang Chen
Xiaoyan Wang
Furong Lv
Yue Yu
Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson’s disease
Hereditas
Parkinson's disease
SUMOylation
Biomarker
Machine learning
SUMO3
SEH1L
title Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson’s disease
title_full Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson’s disease
title_fullStr Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson’s disease
title_full_unstemmed Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson’s disease
title_short Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson’s disease
title_sort screening identification and experimental validation of sumoylation biomarkers in parkinson s disease
topic Parkinson's disease
SUMOylation
Biomarker
Machine learning
SUMO3
SEH1L
url https://doi.org/10.1186/s41065-025-00525-1
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