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Ferroptosis-Related Immune Genes in Hematological Diagnosis of Parkinson’s Diseases

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Abstract

Emerging evidence suggested that ferroptosis and immune activation, as well as their interactions, played a crucial role in the occurrence and progression of Parkinson’s disease (PD). However, whether this interaction could serve as the basis for a hematological diagnosis of PD remained poorly understood. This study aimed to construct a novel hematological model for PD diagnosis based on the ferroptosis-related immune genes. The brain imaging of PD patients was obtained from the Affiliated Hospital of Nantong University. We used least absolute shrinkage and selection operator (LASSO) to identify the optimal signature ferroptosis-related immune genes based on six gene expression profile datasets of substantia nigra (SN) and peripheral blood of PD patients. Then we used the support vector machine (SVM) classifier to construct the hematological diagnostic model named Ferr.Sig for PD. Gene set enrichment analysis was utilized to execute gene functional annotation. The brain imaging and functional annotation analysis revealed prominent iron deposition and immune activation in the SN region of PD patients. We identified a total of 17 signature ferroptosis-related immune genes using LASSO method and imported them to SVM classifier. The Ferr.Sig model exhibited a high diagnostic accuracy, and its area under the curve (AUC) for distinguishing PD patients from healthy controls in the training and internal validation cohort reached 0.856 and 0.704, respectively. We also used the Ferr.Sig into other external validation cohorts, and a comparable AUC with the internal cohort was obtained, with the AUC of 0.727 in Scherzer’s cohort, 0.745 in Roncagli’s cohort, and 0.778 in Meiklejohn’s cohort. Furthermore, the diagnostic performance of Ferr.Sig was not interfered by the other neurodegenerative diseases. This study revealed the value of ferroptosis-related immune genes in PD diagnosis, which may provide a novel direction and strategy for the development of novel biomarkers with less invasiveness, low cost, and high accuracy for PD screening and diagnosis.

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Data Availability

The data involved in this study were available from the GEO database (GEO; https://www.ncbi.nlm.nih.gov/geo/). A total of six gene expression profile datasets of SN tissue from PD patients and healthy controls were used in this study including GSE7621, GSE20141, GSE20163, GSE20164, GSE20295, and GSE49036, and four gene expression profile datasets of blood were used including GSE6613, GSE72267, GSE80599, and GSE99039. Further data were available from the corresponding author upon request.

Abbreviations

α-syn :

α-synuclein

AUC :

area under the curve

CI :

confidence intervals

GO :

Gene Ontology

GPX4 :

glutathione peroxidase 4

GSEA :

gene set enrichment analysis

LASSO :

least absolute shrinkage and selection operator

NDD :

neurodegenerative disease

PD :

Parkinson’s disease

PPI :

protein–protein interaction

ROC :

receiver operating characteristic

ROS :

reactive oxygen species

SN :

substantia nigra

SVM :

support vector machine

SWI :

susceptibility-weighted imaging

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Acknowledgements

We were very grateful to everyone member for advice and help in this study.

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Author notes

  1. Heyue Lu and Bo Zhang contributed equally to this study.

Authors and Affiliations

  1. Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20, Xisi Road, Nantong, 226001, People’s Republic of China

    Heyue Lu, Tingting Yin, Ye Hua, Chenyang Cao, Min Ge, Dandan Shen & Zhongzheng Jia

  2. Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, NO.20, Xisi Road, Nantong, 226001, People’s Republic of China

    Bo Zhang & You Lang Zhou

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  1. Heyue Lu
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Contributions

HYL, BZ, YLZ, and ZZJ designed this work. HYL, BZ, TTY, YH CYC, and MG conducted data acquisition. HYL and BZ performed the data integration and analysis. HYL, BZ, and ZZJ wrote this manuscript. HYL, BZ, TTY, and DDS were responsible for the manuscript revision. All authors approved this manuscript.

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Correspondence to You Lang Zhou or Zhongzheng Jia.

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This study was approved by the Ethics Committee of the Affiliated Hospital of Nantong University. All subjects have signed informed consent.

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Supplementary Information

ESM 1

Figure S1. The principal component analysis for the combined six gene expression profiles of PD. (A) Before batch correction. (B) After batch correction using ComBat algorithm. (PDF 1414 kb)

ESM 2

Figure S2. Logistic regression for 27 differentially expressed ferroptosis related genes in SN tissues of PD patients. Odds Ratio < 1 represented the protective factor for PD, while Odds Ratio > 1 represented the risk factor for PD. (PDF 163 kb)

ESM 3

Figure S3. The GO enrichment analysis for the differentially expressed genes between peripheral blood of healthy subjects and PD patients. (PDF 169 kb)

ESM 4

Figure S4. Logistic regression for 17 identified signature ferroptosis-related immune genes in SN tissues of PD patients. Odds Ratio < 1 represented the protective factor for PD, while Odds Ratio > 1 represented the risk factor for PD. (PDF 148 kb)

ESM 5

Figure S5. The performance of each signature ferroptosis-related immune gene in PD diagnosis measured by the area under the ROC curve (AUC) in the training cohort. (PDF 879 kb)

ESM 6

(XLSX 360 kb)

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Lu, H., Zhang, B., Yin, T. et al. Ferroptosis-Related Immune Genes in Hematological Diagnosis of Parkinson’s Diseases. Mol Neurobiol 60, 6395–6409 (2023). https://doi.org/10.1007/s12035-023-03468-8

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