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Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disease associated with age. Early diagnosis of PD is key to preventing the loss of dopamine neurons. Peripheral-blood biomarkers have shown their value in recent years because of their easy access and long-term monitoring advantag...

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Autores principales: Zhang, Wen, Shen, Jiabing, Wang, Yuhui, Cai, Kefu, Zhang, Qi, Cao, Maohong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924528/
https://www.ncbi.nlm.nih.gov/pubmed/35310885
http://dx.doi.org/10.3389/fnmol.2022.762544
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author Zhang, Wen
Shen, Jiabing
Wang, Yuhui
Cai, Kefu
Zhang, Qi
Cao, Maohong
author_facet Zhang, Wen
Shen, Jiabing
Wang, Yuhui
Cai, Kefu
Zhang, Qi
Cao, Maohong
author_sort Zhang, Wen
collection PubMed
description Parkinson’s disease (PD) is the second most common neurodegenerative disease associated with age. Early diagnosis of PD is key to preventing the loss of dopamine neurons. Peripheral-blood biomarkers have shown their value in recent years because of their easy access and long-term monitoring advantages. However, few peripheral-blood biomarkers have proven useful. This study aims to explore potential peripheral-blood biomarkers for the early diagnosis of PD. Three substantia nigra (SN) transcriptome datasets from the Gene Expression Omnibus (GEO) database were divided into a training cohort and a test cohort. We constructed a protein–protein interaction (PPI) network and a weighted gene co-expression network analysis (WGCNA) network, found their overlapping differentially expressed genes and studied them as the key genes. Analysis of the peripheral-blood transcriptome datasets of PD patients from GEO showed that three key genes were upregulated in PD over healthy participants. Analysis of the relationship between their expression and survival and analysis of their brain expression suggested that these key genes could become biomarkers. Then, animal models were studied to validate the expression of the key genes, and only SSR1 (the signal sequence receptor subunit1) was significantly upregulated in both animal models in peripheral blood. Correlation analysis and logistic regression analysis were used to analyze the correlation between brain dopaminergic neurons and SSR1 expression, and it was found that SSR1 expression was negatively correlated with dopaminergic neuron survival. The upregulation of SSR1 expression in peripheral blood was also found to precede the abnormal behavior of animals. In addition, the application of artificial intelligence technology further showed the value of SSR1 in clinical PD prediction. The three classifiers all showed that SSR1 had high predictability for PD. The classifier with the best prediction accuracy was selected through AUC and MCC to construct a prediction model. In short, this research not only provides potential biomarkers for the early diagnosis of PD but also establishes a possible artificial intelligence model for predicting PD.
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spelling pubmed-89245282022-03-17 Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease Zhang, Wen Shen, Jiabing Wang, Yuhui Cai, Kefu Zhang, Qi Cao, Maohong Front Mol Neurosci Molecular Neuroscience Parkinson’s disease (PD) is the second most common neurodegenerative disease associated with age. Early diagnosis of PD is key to preventing the loss of dopamine neurons. Peripheral-blood biomarkers have shown their value in recent years because of their easy access and long-term monitoring advantages. However, few peripheral-blood biomarkers have proven useful. This study aims to explore potential peripheral-blood biomarkers for the early diagnosis of PD. Three substantia nigra (SN) transcriptome datasets from the Gene Expression Omnibus (GEO) database were divided into a training cohort and a test cohort. We constructed a protein–protein interaction (PPI) network and a weighted gene co-expression network analysis (WGCNA) network, found their overlapping differentially expressed genes and studied them as the key genes. Analysis of the peripheral-blood transcriptome datasets of PD patients from GEO showed that three key genes were upregulated in PD over healthy participants. Analysis of the relationship between their expression and survival and analysis of their brain expression suggested that these key genes could become biomarkers. Then, animal models were studied to validate the expression of the key genes, and only SSR1 (the signal sequence receptor subunit1) was significantly upregulated in both animal models in peripheral blood. Correlation analysis and logistic regression analysis were used to analyze the correlation between brain dopaminergic neurons and SSR1 expression, and it was found that SSR1 expression was negatively correlated with dopaminergic neuron survival. The upregulation of SSR1 expression in peripheral blood was also found to precede the abnormal behavior of animals. In addition, the application of artificial intelligence technology further showed the value of SSR1 in clinical PD prediction. The three classifiers all showed that SSR1 had high predictability for PD. The classifier with the best prediction accuracy was selected through AUC and MCC to construct a prediction model. In short, this research not only provides potential biomarkers for the early diagnosis of PD but also establishes a possible artificial intelligence model for predicting PD. Frontiers Media S.A. 2022-03-02 /pmc/articles/PMC8924528/ /pubmed/35310885 http://dx.doi.org/10.3389/fnmol.2022.762544 Text en Copyright © 2022 Zhang, Shen, Wang, Cai, Zhang and Cao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Neuroscience
Zhang, Wen
Shen, Jiabing
Wang, Yuhui
Cai, Kefu
Zhang, Qi
Cao, Maohong
Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease
title Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease
title_full Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease
title_fullStr Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease
title_full_unstemmed Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease
title_short Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease
title_sort blood ssr1: a possible biomarker for early prediction of parkinson’s disease
topic Molecular Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924528/
https://www.ncbi.nlm.nih.gov/pubmed/35310885
http://dx.doi.org/10.3389/fnmol.2022.762544
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