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Gene signatures and immune correlations in Parkinson’s disease Braak stages

European Journal of Medical Research volume 30, Article number: 278 (2025) Cite this article

Abstract

Background

Parkinson's disease (PD), a progressive neurodegenerative disease, still lacks disease-modifying treatment strategies. The formation of Lewy body is the typical pathological feature of PD. Pathological progression can be defined by Braak stages. However, the molecular mechanism for this ascending course of α-synuclein pathology remains unclear.

Methods

In this study, weighted gene co-expression network analysis (WGCNA) was used to screen Braak stage-related gene signatures, followed by the functional enrichment analysis, including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA). The hub genes were screened through CytoHubba and Least Absolute Shrinkage and Selection Operator (LASSO) analysis. The immune cell proportion was predicted by the ImmuCellAI. Furthermore, transcription factors (TFs) and miRNAs targeting the hub genes network were constructed. After verifying hub gene expression level through independent data sets. The validated hub gene was further analyzed to elucidate the potential molecular mechanism.

Results

Total of 388 genes associated with Braak stages were screened out through WGCNA analysis. The KEGG analysis showed that these genes were involved in endocytosis, HIF-1 signaling pathway, synaptic vesicle cycle, dopaminergic synapse, oxytocin signaling pathway, etc. Immune infiltration analysis showed that CD4 + T cells, including nTreg, Th2, and Th17, were obviously different between different Braak stages in PD. Furthermore, eights Braak stages-related hub genes were identified, including CAMK2B, CPLX2, GAPDH, GRIN1, KCNA1, MAPK3, MAPT, and STXBP1 through the cytoHubba plugin and LASSO analysis. After verifying the expression level in three independent data sets, CPLX2 was finally identified as the most reliable Braak stages-associated hub genes in PD.

Conclusions

This study revealed the Braak stage-related gene signatures in PD and identified CPLX2 as a novel Braak stages-related hub gene in PD, which provided a novel target for future therapeutic interventions and disease markers. The specific molecular mechanism of CPLX2 in PD remained to be further clarified.

Introduction

Parkinson’s disease (PD), the second most common neurodegenerative disease, has affected about seven million people in the world [1]. As the population ages, the population of PD patients is expected to be around 10 million globally by 2030 [2]. It is characterized clinically by a combination of motor (e.g., bradykinesia, rest tremor, rigidity, and postural instability) and non-motor signs and symptoms (e.g., hyposmia, constipation, and sleep disorder) [3,4,5]. In recent decades, treatment strategy for PD have been improved, such as medication, surgery, rehabilitation therapy, etc. [6, 7]. However, current treatment strategies only ameliorate symptoms in PD patients. Disease-modifying treatment strategies are still lacking. Ultimately, PD patients will develop into irreversible disability and loss of independence with disease progression [4]. Therefore, effective disease-modifying treatment strategies are currently the most urgent task to modify the occurrence and progression of PD and improve the life quality of patients.

At pathophysiology level, PD is characterized by the death of dopaminergic neurons and formation of Lewy body in substantia nigra [6]. The Lewy body were mainly composed of abnormal aggregates alpha-synuclein (α-syn) [8]. The abnormal aggregation and deposition of α-synuclein is one of the core pathological features of PD. These protein aggregates are highly stable and diffusible, and they can induce normal proteins to transform into the misfolded state, thereby forming larger polymers and ultimately causing neuronal damage and cell death [3]. In 2003, Braak and colleagues proposed the most widely accepted pathological staging system–Braak hypothesis to explain the characteristics of the neuropathological progression of PD [9]. This hypothesis suggests PD starts at dorsal motor nucleus and the olfactory bulb (stages 1) [9]. In stages 1, PD patients exhibit non-motor symptoms, including hyposmia and constipation [10]. After that, the α-synuclein pathology can be detected in the coeruleus–subcoeruleus complex. At stage 3 and 4, α-synuclein pathology diffuses to the substantia nigra, other midbrain and basal forebrain structures, which is related to classic PD motor symptoms. Finally, α-synuclein pathology affects cerebral cortices (stages 5 and 6), leading to cognitive disturbances and hallucinations [9]. However, the molecular mechanism for this ascending course of α-synuclein pathology remains unclear.

In this study, we attempted to reveal the gene signatures associated with Braak stage in PD. Weighted gene co-expression network analysis (WGCNA) was used to screen Braak stage-related gene signatures in PD basing on the expression profiling from different Braak Lewy body stage groups (GSE216281). After that, the functional enrichment analysis of gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were applied to explore the potential molecular mechanism. The hub genes were screened through CytoHubba, followed by Least Absolute Shrinkage and Selection Operator (LASSO) analysis. The immune cell proportion difference between Braak stages were predicted by the ImmuCellAI tool. Furthermore, transcription factors (TFs) and miRNAs targeting the hub genes network were constructed. After verifying hub gene expression level through independent data sets (GSE99039, GSE7621, and GSE49036), a validated hub gene was further analyzed to elucidate the potential molecular mechanism. This study will provide novel molecular mechanism for exploring PD-related Braak staging, and provide a new target for pathologically delaying progression and biomarkers of PD.

Methods

Data collection and processing

Gene expression profiles (GSE216281, GSE99039, GSE7621, and GSE49036) were downloaded from Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo/) (Figure S1). The GSE216281 data set was frontal cortex gene expression profile containing 84 samples with different Braak stage. The GSE99039 data set was a blood-based gene expression profile containing 233 healthy controls, 205 PD patients, and 48 patients with other neurodegenerative diseases. The GSE7621 data set was a substantia nigra gene expression profile from 9 controls and 16 PD. The GSE49036 data set was a substantia nigra gene expression profile from different Braak stage, including 8 samples of stage 0, 5 samples of stages 1–2, 7 samples of stages 3–4 and 8 samples of stages 5–6. The GSE216281 data set was used as training data set. The GSE99039, GSE7621, and GSE49036 data sets were used as validation data sets.

Weighted gene co-expression network analysis (WGCNA)

To identify Braak stage-related gene signatures in PD, the weighted co-expression network was constructed basing on the expression profile and clinical data from GSE216281 through the “WGCNA” package in R [11]. First of all, outlier samples were excluded through hierarchical cluster analysis in R. After that, the"pickSoftThreshold"function in the R package “WGCNA” was used to select a suitable soft-power threshold β for automatic network construction. After choosing the power of 8, the adjacency matrix was transformed into a topological overlap matrix (TOM). According to the TOM-based dissimilarity measure with a minimum size of 30, genes with similar expression profiles were classified into gene modules. Pearson’s correlation analysis was applied to calculate the correlation between module feature and Braak stage. The most positively and negatively correlation module genes were selected for further analysis.

Functional enrichment of Braak stage-related gene signatures in PD

Gene Ontology (GO) was used to obtain the cellular components (CC), biological processes (BP), and molecular functions (MF) of Braak stage-related gene signatures. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to explore the potential molecular pathway through the “clusterProfiler” package in R [12, 13]. The cutoff value for the GO and KEGG was set as p < 0.05. In addition, Braak stage-related potential molecular pathways were also explored through Gene Set Enrichment Analysis (GSEA). The annotated gene set c2.cp.kegg.v7.1.symbols.gmt was chosen as the reference gene list. The enrichment score (ES), normalized enrichment score (NES) and false discovery rate (FDR) were calculated. The cutoff value for the GSEA was set as p < 0.05.

Screening Braak stage-related hub genes

The hub genes related to Braak stage were screened through CytoHubba plug in Cytoscape [14]. Three different algorithms, including Degree, Maximum Neighborhood Component (MNC), and Edge Percolated Component (EPC) were used. After the overlap of the three algorithms was selected by the Venn diagram, LASSO logistic regression analysis was further performed for obtaining the hub genes associated with Braak stage through “glmnet” package in R.

Immune infiltration analysis

The immune infiltration analysis was performed to detect the difference between different Braak stages through the Immune Cell Abundance Identifier (ImmuCellAI). The ImmuCellAI was an online tool for accurately estimating the abundance of 18 T-cell subtypes and six other important immune cells through the RNA sequencing and microarray data sets [15]. The correlation between Braak stages-related hub genes and immune cell abundance was analyze through Spearman’s correlation analysis.

Identification of transcription factors (TF) and miRNA–hub gene regulatory network

The miRNA–gene interaction was searched basing on the Tarbase database (version 8.0). Similarly, the TF–gene interaction was searched basing on the ENCODE database. TF–gene and miRNA–gene networks was visualized through the NetworkAnalyst database [16].

Differential expression genes (DEGs) between high and low CPLX2 expression group in PD

The DEGs between high and low CPLX2 expression group was analyzed by the “Limma” package in R software. The threshold was set as |log2 FoldChange|≥ 0.585 and |p value|< 0.05. The volcano plot was used to visualize the high and low CPLX2 expression group through “ggplot2” package in R. The top20 DEGs with the most significant up and downregulation was displayed using “Pheatmap” package in R.

Statistics

The R software, GraphPad Prism 9.0 software and SangerBox online analysis tool (http://vip.sangerbox.com/home.html) were applied to perform statistical analyses and graphs. The receiver operating characteristic (ROC) curve were performed to evaluate the area under curve (AUC) with 95% confidence intervals (CI). The difference analysis between the two groups was completed by two-tailed Student’s t test. ∗ P < 0.05, **P < 0.01, ns: no statistical difference.

Results

Screening Braak stages-related gene sets via WGCNA in PD

To explore the Braak stages-related gene sets, WGCNA method was applied in this study. The top 50% highest variance from the expression profile were screened for WGCNA analysis. After that, we constructed the scale-free network with a β value equal to 8 (Fig. 1A, B). Total 24 modules were classified through the cluster dendrogram (Fig. 1C). The connectivity and cluster analysis between 14 modules were performed (Fig. 1D). Then, the correlation between the module and Braak stages were analyzed through Pearson’s correlation analysis (Fig. 1E). The paleturquoise module, containing 326 genes, was the most negatively related to the Braak stages (r = − 0.26, p = 0.02) (Fig. 1F). The lightsteelblue1, containing 62 genes, module was the most positively associated with the Braak stages (r = 0.38, p = 4.3e- 4) (Fig. 1G). In total, we identified a total of 388 genes associated with Braak stages and used them for further analysis.

Fig. 1
figure 1

Screening Braak stages-related gene sets via WGCNA in PD. A Mean connectivity for different soft-threshold powers (β). B Scale-free index for different soft-threshold powers (β). C Recognition module, each module was given an individual color as identifier, such as 14 different modules. D Topological overlap matrix (TOM). E Correlation heat map of gene modules and Braak stges. F, G Scatter plots for correlations between gene significance and module membership in paleturquoise and lightsteelblue1 module

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Enrichment analysis of Braak stages-related gene sets in PD

To clarify the molecular mechanism of Braak stages-related gene sets in PD, the GO, KEGG and GSEA enrichment analysis were conducted. In GO enrichment analysis, the result showed that cellular components of these Braak stages-related gene sets were located in neuron part, synapse, neuron projection, synapse part, somatodendritic compartment, etc. (Fig. 2A). The biological processes of them were involved in organelle organization, cellular localization, macromolecule localization, protein localization, regulation of cellular component organization, establishment of localization in cell, etc. (Fig. 2B). The molecular functions of Braak stages-related gene sets in PD was related to cytoskeletal protein binding, protein domain specific binding, tubulin binding, phosphatidylinositol binding, GTPase activity, etc. (Fig. 2C). The KEGG enrichment analysis showed that the Braak stages-related gene sets in PD took part in endocytosis, HIF-1 signaling pathway, synaptic vesicle cycle, dopaminergic synapse, oxytocin signaling pathway, etc. (Fig. 2D). In addition, GSEA analysis revealed that the butanoate metabolism, glycerolipid metabolism, alpha linolenic acid metabolism, glycolysis gluconeogenesis, PPAR signaling pathway, pyruvate metabolism, and citrate cycle TCA cycle were enriched in low Braak stages group (Fig. 2E).

Fig. 2
figure 2

Functional enrichment of Braak stage-related gene signatures in PD. A–C GO enrichment analysis (cellular components, biological process, and molecular function). D KEGG enrichment analysis. E GSEA analysis. ES, Enrichment Score; NES, Normalized Enrichment Score; and FDR, false discovery rate

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Screening of Braak stages-related hub genes in PD

CytoHubba plug in Cytoscape was applied to screen the Braak stages-related hub genes in PD through three different algorithms, including MNC, Degree, and EPC (Fig. 3A–C). Finally, 13 overlapped Braak stages-related hub genes were screened out from three algorithms, including MAPT, GAPDH, GRIN1, SNCB, STXNP1, RAB3 A, NRXN2, CPLX2, MAPK3, CAMK2B, KCNA1, GAP3, and CPLX1 (Fig. 3D). Furthermore, the LASSO logistic regression analysis was established basing on the expression profile of the above 13 overlapped Braak stages-related hub genes. Ultimately, eights Braak stages-related hub genes were retained, including CAMK2B, CPLX2, GAPDH, GRIN1, KCNA1, MAPK3, MAPT, and STXBP1 (Fig. 3E, F).

Fig. 3
figure 3

Screening Braak stage-related hub genes in PD. A–C Top 15 Braak stage-related hub genes were screened by the MNC, Degree, and EPC algorithms of the cytoHubba plugin. D Venn diagram showing the common hub genes from three algorithms. E, F LASSO logistic regression analysis

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Immune cell infiltration difference in Braak stages and their correlation with hub genes

Subsequently, we estimated the difference of 18 T-cell subtypes and six other important immune cells in Braak stages by the ImmuCellAI to explore the role of the immune system in the progression of Braak stages in PD. The result showed that the CD4 + T cells, including nTreg, Th2, and Th17, were obviously difference between different Braak stages in PD (Fig. 4A, B). The correlation analysis revealed that the Braak stages were negatively correlated with the abundance of CD4 + T cells (r =  − 025, p = 0.02), nTreg (r = − 0.27, p = 0.01), Th2 (r = − 0.25, p = 0.02), and Th17 (r = − 0.24, p = 0.03) (Fig. 4C–F). After that, we explored the correlation between the expression levels of Braak stages-related hub genes and different immune cells. We found that the abundance of CD4 + T cells were positively correlated with GRIN1 and CPLX2 expression level (Fig. 4G). In addition, the abundance of Th2 cells were positively correlated with GRIN1, STXBP1, CPLX2, and CAMK2B expression level (Fig. 4G). The abundance of Th17 cells were negatively correlated with STXBP1 expression level (Fig. 4G). In general, differences in CD4 + T cells played a crucial role in the Braak stages of PD.

Fig. 4
figure 4

Immune infiltration analysis. A Ten types of immune cell difference between different Braak stages. B 14 CD4 + and CD8 + T cell subtypes difference between different Braak stages. C–F Correlation analysis between CD4 + T cells, nTreg, Th2, Th17 and Braak stages. G Correlation analysis between CD4 + T cells, nTreg, Th2, Th17 and the expression level of Braak stage-related hub genes

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Construction transcription factors (TF) and miRNA–hub gene regulatory network

To explore the regulatory mechanisms of Braak stages-related hub genes, we further predicted the upstream and downstream TFs and miRNAs of them. As for miRNAs, we found that 27 miRNAs could interact with GAPDH, 4 miRNAs could interact with STXBP1, 5 miRNAs could interact with CPLX2, 7 miRNAs could interact with MAPK3, and 23 miRNAs could interact with MAPT (Fig. 5A). In addition, TF hub gene regulatory network is shown in Fig. 5B. These results provided novel insights into the regulatory mechanisms of Braak stages-related hub genes in PD.

Fig. 5
figure 5

Transcription factors (TF) and miRNA–gene regulatory network of Braak stage-related hub genes. A miRNA–hub cross-talk genes regulatory network. B TFs–hub cross-talk genes regulatory network

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Hub genes validation in independent data set

To increase the reliability of the result, we validated the expression level of Braak stages-related hub genes through three independent data sets from GEO database. In the GSE99039 data set, there were decreased CPLX2 and increased GAPDH and MAPK3 expression level in PD group, compared with HC group (Fig. 6A–H). In the GSE7621 data set, we found that there were decreased CPLX2 expression level in PD group, compared with HC group (Fig. 6I–P). In addition, there were decreased CAMK2B, CPLX2, MAPT, and STXBP1 in PD group, compared with HC group (Fig. 6Q–X). In general, CPLX2 was the Braak stages-related hub gene that was significantly decreased in PD samples from different tissue.

Fig. 6
figure 6

Hub genes validation in independent data set. A–H Scatter plot showed the expression level of Braak stages-related hub genes in the peripheral blood of HC and PD patients from the GSE99039 data sets. I–P Scatter plot showed the expression level of Braak stages-related hub genes in the substantia nigra of HC and PD patients from the GSE7621 data sets. Q–X Scatter plot showed the expression level of Braak stages-related hub genes in the substantia nigra of HC and PD patients from the GSE49036 data sets. The difference analysis between the two groups was completed by a two-tailed Student’s t test. * p < 0.05; ** p < 0.01; ns, no statistical difference

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Exploration the potential molecular function of CPLX2 in PD

In this part, we further explored the potential molecular mechanism of CPLX2 involvement in Braak stages. To begin, we analyzed the value of CPLX2 as a biomarker for PD through ROC analysis. The AUC basing on the data from GSE7621 and GSE49036 were 0.819 and 0.900, respectively (Fig. 7A, B). After that, we identified the DEGs between high and low CPLX2 expression group from GSE216281 data set. Under the threshold of |log2 FoldChange|≥ 0.585 and |p value|< 0.05, total 26 up-regulated and 921 down-regulated DEGs were identified (Fig. 7C). The top20 DEGs with the most significant up and downregulation is displayed in Fig. 7D. The KEGG enrichment analysis showed that CPLX2-related DEGs were involved in PI3K–AKT signaling pathway, MAPK signaling pathway, Ras signaling pathway, ether lipid metabolism, complement and coagulation cascades, ECM–receptor interaction, Prion diseases, ABC transporters, Taurine and hypotaurine metabolism, and Apoptosis–multiple species (Fig. 7E). Taken it together, CPLX2 was a reliable biomarker for PD and took part in regulate the Braak stages through multiple pathways.

Fig. 7
figure 7

Exploration the potential molecular function of CPLX2 in PD. A, B ROC analysis basing the CPLX2 expression level in the GSE7621 and GSE49036. C DEGs volcano plot. Cutoff: |log2 FoldChange|≥ 0.585 and |p value|< 0.05. D Heat map showed the top 20 DEGs with the most significant differences. E KEGG enrichment analysis of DEGs

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Discussion

PD, the second most common age-related neurodegenerative disease, still lacks disease-modifying treatment strategies. The death of dopaminergic neurons and formation of Lewy body in substantia nigra were the typical pathological feature of PD [6]. Braak hypothesis was the most widely accepted pathological staging system for PD [17]. However, the molecular mechanism for this ascending course of α-synuclein pathology remains unclear.

In this study, we attempted to reveal the gene signatures associated with Braak stage in PD through bioinformatics analysis and verification. Total of 388 genes associated with Braak stages in PD were screened out through WGCNA analysis. The KEGG enrichment analysis showed that these genes were involved in endocytosis, HIF-1 signaling pathway, synaptic vesicle cycle, dopaminergic synapse, oxytocin signaling pathway, etc. These signaling pathways have been reported to be associated with the occurrence and development of PD in a large number of studies [18,19,20,21]. For example, loss of dopaminergic neurons and decreased dopamine content in the substantia nigra were the core pathological feature of PD [18]. We also found these Braak stages-related genes were related to the dopaminergic synapse and synaptic vesicle cycle. In addition, previous studies found that HIF-1 signaling pathway was essential for differentiation and survival of dopaminergic neurons [19]. In addition, reducing HIF- 1 expression level could promote the neuronal death in Parkinsonism [19]. In another study, the activation of HIF-1 complex showed neuroprotective effects through regulating the vascular endothelial growth (VEGF) genes and erythropoetin (EPO) [20]. As for oxytocin signaling pathway, Erbas et al., found that oxytocin treatment can ameliorate dopaminergic cell death in the striatum induced by rotenone [21]. Similarity, oxytocin treatment can alleviate the 1-methyl- 4-phenyl- 1, 2, 3, 6-tetrahydropyridine (MPTP)-induced neurotoxicity through miR- 26a/DAPK1 signaling pathway [22]. Therefore, these genes can take part in the Braak stages in PD through multiplied pathways.

Recent years, mounting evidence revealed the peripheral immune compartment contributed the onset and progression of PD [23, 24]. Therefore, we explored the Immune cell Infiltration difference between different Braak stages in PD. We found that the CD4 + T cells, including nTreg, Th2, and Th17, were obviously difference between different Braak stages in PD. What’s more, the Braak stages were negatively correlated with the abundance of CD4 + T cells, nTreg, Th2, and Th17. In previous study, a decreased circulating CD4 + cell numbers were observed in PD [25, 26]. In subtypes, CD4 + T cells can be further categorized into Th1, Th2, Th9, Th17, Th22, Treg cells, and T follicular cells [27, 28]. Th1 and Th17 cells belong to pro-inflammatory subtype. Th2 and Treg cells belong to anti-inflammatory subtype [29]. More important, the decreased CD4 + T cells in the peripheral blood of PD patient was mainly related to decreased Th2, Th17, and Tregs cells [30,31,32], which was consistent with the results of this study. Combing with the results of this study, we speculated that the progression of Braak stage in PD was closely related to reduce abundance of nTreg and Th2, which led to a pro-inflammatory environment in the brain.

Furthermore, eights Braak stages-related hub genes were identified, including CAMK2B, CPLX2, GAPDH, GRIN1, KCNA1, MAPK3, MAPT, and STXBP1 through the cytoHubba plugin and LASSO analysis. After verifying the expression level in three independent data sets, CPLX2 was finally identified as the most reliable Braak stages-associated hub genes in PD. The AUC basing on the CPLX2 expression level from GSE7621 and GSE49036 were 0.819 and 0.900, respectively. Complexin 2 (CPLX2) was first identified in rat brain homogenates by McMahon et al., in 1995 [33]. Human CPLX2 gene contains five exons and encodes for a neuron specific protein, composed of 134 amino acids [34]. Several studies suggested that CPLX2 played a crucial role in exocytosis of synaptic vesicles [34, 35]. In addition, several studies reported that CPLX2 was related to the schizophrenia, lung high grade neuroendocrine tumors, and human hepatocellular carcinoma [36,37,38]. In this study, we identified CPLX2 as novel biomarker for PD. KEGG enrichment analysis revealed that CPLX2 may take part in regulating Braak stages in PD through PI3K–AKT signaling pathway, MAPK signaling pathway, Ras signaling pathway, ether lipid metabolism, etc. More important, the expression level of CPLX2 was positively corrected with the abundance of CD4 + T cells and Th2. In 2019, Tsuru et al., found that CPLX2 can regulate the secretion of immunoglobulin, which indicating its function in immunoregulation [39]. In addition, 5 miRNAs were predicated to interact with CPLX2, which provided a novel direction for regulating CPLX2 expression levels. Overall, as a candidate gene, CPLX2 may provide a brand-new target for the therapeutic strategies of PD.

However, there were some limitations in this study. First of all, the results were constructed basing on the GEO database and lacked of data from our group. In addition, the functions of CPLX2 in PD was still unclear and need to be further improved in future study.

Conclusion

This study revealed the Braak stage-related gene signatures in PD and identified CPLX2 as a novel Braak stages-related hub gene in PD, which provided a novel target for future therapeutic interventions and disease markers.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

PD:

Parkinson's disease

WGCNA:

Weighted gene co-expression network analysis

GO:

Gene ontology

CC:

Cellular components

BP:

Biological processes

MF:

Molecular functions

KEGG:

Kyoto encyclopedia of genes and genomes

GSEA:

Gene set enrichment analysis

NES:

Normalized enrichment score

ES:

Enrichment score

FDR:

False discovery rate

MNC:

Maximum neighborhood component

EPC:

Edge percolated component

LASSO:

Least absolute shrinkage and selection operator

TFs:

Transcription factors

DEGs:

Differential expression genes

CPLX2:

Complexin 2

ROC:

Receiver operating characteristic

AUC:

Area under curve

CI:

Confidence intervals

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Acknowledgements

We would like to thank GEO data set (https://ncbi.nlm.nih.gov/gds/).

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  1. Department of Internal Medicine, Nanjing Luhe District Zhu Town Community Health Service Center, Nanjing, Jiangsu, China

    Rui-xue Sun & Yan Guo

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Rui-xue Sun had the idea for the article. Rui-xue Sun and Yan Guo analyzed the data. Rui-xue Sun wrote the manuscript. All authors have read and approved the final manuscript.

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Sun, Rx., Guo, Y. Gene signatures and immune correlations in Parkinson’s disease Braak stages. Eur J Med Res 30, 278 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40001-025-02554-y

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European Journal of Medical Research

ISSN: 2047-783X

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