Machine learning-based characterization of cuprotosis-related biomarkers and immune infiltration in Parkinson’s disease

Abstract

Background: Parkinson’s disease (PD) is a neurodegenerative disease commonly seen in the elderly. On the other hand, cuprotosis is a new copper-dependent type of cell death that can be observed in various diseases.
Methods: This study aimed to identify potential novel biomarkers of Parkinson’s disease by biomarker analysis and to explore immune cell infiltration during the onset of cuprotosis. Gene expression profiles were retrieved from the GEO database for the GSE8397, GSE7621, GSE20163, and GSE20186 datasets. Three machine learning algorithms: the least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE) were used to screen for signature genes for Parkinson’s disease onset and cuprotosis-related genes (CRG). Immune cell infiltration was estimated by ssGSEA, and cuprotosis-related genes associated with immune cells and immune function were examined using spearman correlation analysis. Nomogram was created to validate the accuracy of these cuprotosis-related genes in predicting PD disease progression. Classification of Parkinson’s specimens using consensus clustering methods.
Result: Three PD datasets from the Gene Expression Omnibus (GEO) database were combined after eliminating batch effects. By ssGSEA, we identified three cuprotosis-related genes ATP7A, SLC31A1, and DBT associated with immune cells or immune function in PD and more accurate for the diagnosis of Parkinson’s disease course. Patients could benefit clinically from a characteristic line graph based on these genes. Consistent clustering analysis identified two subtypes, with the C2 subtype exhibiting higher immune cell infiltration and immune function.
Conclusion: In conclusion, our study reveals that several newly identified cuprotosis-related genes intervene in the progression of Parkinson’s disease through immune cell infiltration.

Full text

Machine learning-based
characterization of
cuprotosis-related biomarkers
and immune infiltration in
Parkinson’s disease
Songyun Zhao
1†
, Li Zhang
2†
, Wei Ji
1†
, Yachen Shi
2
,
Guichuan Lai
3
, Hao Chi
4
, Weiyi Huang
1
and Chao Cheng
1
*
1
Department of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi,
Jiangsu, China,
2
Department of Neurology, Wuxi People’s Hospital Affiliated to Nanjing Medical
University, Wuxi, Jiangsu, China,
3
Department of Epidemiology and Health Statistics, School of Public
Health, Chongqing Medical University, Chongqing, China,
4
Clinical Medicine College, Southwest
Medical University, Luzhou, China
Background: Parkinson’s disease (PD) is a neurodegenerative disease
commonly seen in the elderly. On the other hand, cuprotosis is a new
copper-dependent type of cell death that can be observed in various diseases.
Methods: This study aimed to identify potential novel biomarkers of Parkinson’s
disease by biomarker analysis and to explore immune cell infiltration during the
onset of cuprotosis. Gene expression profiles were retrieved from the GEO
database for the GSE8397, GSE7621, GSE20163, and GSE20186 datasets. Three
machine learning algorithms: the least absolute shrinkage and selection
operator (LASSO), random forest, and support vector machine-recursive
feature elimination (SVM-RFE) were used to screen for signature genes for
Parkinson’s disease onset and cuprotosis-related genes (CRG). Immune cell
infiltration was estimated by ssGSEA, and cuprotosis-related genes associated
with immune cells and immune function were examined using spearman
correlation analysis. Nomogram was created to validate the accuracy of
these cuprotosis-related genes in predicting PD disease progression.
Classification of Parkinson’s specimens using consensus clustering methods.
Result: Three PD datasets from the Gene Expression Omnibus (GEO) database
were combined after eliminating batch effects. By ssGSEA, we identified three
cuprotosis-related genes ATP7A, SLC31A1, and DBT associated with immune
cells or immune function in PD and more accurate for the diagnosis of
Parkinson’s disease course. Patients could benefit clinically from a
characteristic line graph based on these genes. Consistent clustering analysis
identified two subtypes, with the C2 subtype exhibiting higher immune cell
infiltration and immune function.
Conclusion: In conclusion, our study reveals that several newly identified
cuprotosis-related genes intervene in the progression of Parkinson’s disease
through immune cell infiltration.
OPEN ACCESS
EDITED BY
Shahnawaz Imam,
University of Toledo, United States
REVIEWED BY
Rahim Alhamzawi,
University of Al-Qadisiyah, Iraq
Peipei Li,
Hefei University of Technology, China
*CORRESPONDENCE
Chao Cheng,
Mr_chengchao@126.com
†
These authors have contributed equally
to this work
SPECIALTY SECTION
This article was submitted to RNA,
a section of the journal
Frontiers in Genetics
RECEIVED 03 August 2022
ACCEPTED 04 October 2022
PUBLISHED 17 October 2022
CITATION
Zhao S, Zhang L, Ji W, Shi Y, Lai G, Chi H,
Huang W and Cheng C (2022), Machine
learning-based characterization of
cuprotosis-related biomarkers and
immune infiltration in
Parkinson’s disease.
Front. Genet. 13:1010361.
doi: 10.3389/fgene.2022.1010361
COPYRIGHT
© 2022 Zhao, Zhang, Ji, Shi, Lai, Chi,
Huang and Cheng. 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.
Frontiers in Genetics frontiersin.org01
TYPE Original Research
PUBLISHED 17 October 2022
DOI 10.3389/fgene.2022.1010361

KEYWORDS
PD, cuprotosis, immune cell infiltration, consensus clustering, bioinformatics analysis
Introduction
Parkinson’s disease (PD) is the second most common
neurodegenerative disease after Alzheimer’s disease, affecting
1.2% of individuals over the age of 65 (Hickman et al., 2018).
It is more common in older adults, with an average age of onset of
about 60 years, and aging is the greatest risk factor for the
development of Parkinson’s disease (Collier et al., 2011).
Parkinson’s disease (PD) is a debilitating motor coordination
disorder caused by the degeneration of dopamine neurons in the
substantia nigra (SN) (Ballance et al., 2019). The main clinical
manifestations are resting tremors, bradykinesia, myotonia, and
postural gait disturbances (Hammond et al., 2019). Other motor
dysfunctions include gait and postural changes, speech and
swallowing difficulties, and changes in expression (Zhang
et al., 2021). In recent years it has been increasingly noted
that non-motor symptoms such as depression, constipation,
and sleep disturbances are also common complaints in
patients with Parkinson’s disease, and they can have an even
greater impact on a patient’s quality of life than motor symptoms
(Cederroth et al., 2019). More research is needed on how to
prevent motor complications. The exact cause of Parkinson’s
disease remains unclear, and genetic factors, environmental
factors, aging, and oxidative stress may all be involved in the
degenerative death process of PD dopaminergic neurons (Fung
et al., 2017). Therefore, early identification of molecular
biomarkers of PD is crucial to initiate timely treatment before
the onset of motor symptoms.
Copper is an essential trace element that plays an important
role in maintaining human life activities, and mechanisms
involving copper may represent potential therapeutic targets
for different pathologies, and significant changes in its levels
in the body may be a potential pathogenic factor in Parkinson’s
disease (Atrian-Blasco et al., 2017). Reduced binding of copper to
ceruloplasmin in PD patients, resulting in elevated free copper
levels, has been shown to be associated with oxidative stress and
neurodegeneration (Ajsuvakova et al., 2020). A recent study
identified a new mode of cell death that is dependent on and
regulated by copper ions in the cell body: cuprotosis. By directly
binding to the lipid acylated components of the tricarboxylic acid
cycle pathway, copper ions lead to abnormal aggregation of lipid
acylated proteins and loss of iron-sulfur cluster proteins,
resulting in proteotoxic stress and ultimately cell death
(Tsvetkov et al., 2022). Dysregulation of copper homeostasis
may trigger cytotoxicity, and changes in intracellular copper
levels can ultimately affect the development and progression
of neurological diseases (Genoud et al., 2020;Li et al., 2020). In
contrast, inhibition of copper transporter protein attenuated α-
synuclein-mediated pathological changes in Parkinson’s patients
and reduced the increase in proteogenic fibrillation and oxidative
stress (Davies et al., 2014;Gou et al., 2021). Also, abnormal
tricarboxylic acid cycle function is closely associated with the
development of Parkinson’s disease, especially dopamine
neurons are much more dependent on mitochondrial
metabolism than other cell types (Supandi and van Beek,
2018;Cai et al., 2019). This suggests that inhibiting the
occurrence of cuprotosis in neurons through drugs may be a
strategy to combat Parkinson’s disease.
In addition, there is growing evidence that the immune
system is allied to neuronal death and PD pathogenesis.
Recent studies have demonstrated that early stages of
Parkinson’s disease progression can be confirmed by detecting
immune cell components in the blood, leading to earlier
detection and confirmation of the disease (Farmen et al.,
2021). Microglia are the brain’s resident immune cells, and
activated microglia correlate directly with the clinical and
pathological severity of Parkinson’s disease (Lanskey et al.,
2018). Current research also includes the function of various
immune cells, such as NK cells (Earls and Lee, 2020) and T cells
(Yeapuri et al., 2022), but there is still a gap in how these cells play
a role in the progression of cuprotosis in PD.
Currently, microarray technology and integrated
bioinformatics analysis have been widely used to identify
potential novel biomarkers and their roles in various diseases
to further explore the pathogenesis and develop potential
therapeutic approaches (Zhao et al., 2021). In contrast, there
have not been any studies on cuprotosis-related forms of
Parkinson’s disease. In this study, four datasets (GSE8397,
GSE7621, GSE20163, and GSE20186) were combined into one
integrated dataset by the SVA method to eliminate batch
differences. To explore the immune cell or immune function
correlation of CRGs with PD, ssGSEA was used to study immune
infiltration in PD, and consistency clustering analysis was
performed to identify pathway differences in cuprotosis-
related gene groupings. We believe our findings will provide
greater insight into the characterization of cuprotosis progression
in PD and provide potential prognostic biomarkers to design
rational therapeutic regimens.
Materials and methods
Raw data acquisition
Five PD datasets (GSE8397, GSE7621, GSE20163, GSE20186,
and GSE42966) were downloaded from the NCBI Gene
Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/
). The above five datasets are all gene expression arrays,
GSE7621 generated using GPL570 (HG-U133_Plus_2)
Affymetrix Human Genome U133 Plus 2.0 Array. GSE8397,
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GSE20164, and GSE20186 generated via GPL96 (HG-U133A)
Affymetrix Human Genome U133A Array was generated.
GSE42966 was generated by GPL4133 Agilent-014850 Whole
Human Genome Microarray 4 × 44K G4112F. The dataset of
GSE8397 included 24 nigrostriatal (SN) samples from PD
patients and 15 nigrostriatal samples from normal subjects;
GSE20163 contained nine nigrostriatal samples from PD and
eight nigrostriatal samples from control subjects; GSE7621 used
nine normal nigrostriatal samples from controls and
16 nigrostriatal samples from 16 Parkinson’s disease patients;
GSE20186 contained 14 PD nigrostriatal samples and five control
samples. GSE42966 served as the validation group and included
four Braak3 nigrostriatal samples from patients and five
Braak4 patient samples.
Selection of characteristic genes
Three machine learning algorithms: LASSO regression
analysis, random forest, and SVM-RFE (Sanz et al., 2018)
were used to screen for eigengenes. LASSO was implemented
as a dimensionality reduction method to perform variable
screening and complexity adjustment while fitting a
generalized linear model. LASSO analysis was implemented
with a penalty parameter utilizing a 10-fold cross-verification
via the “glmnet”package (Engebretsen and Bohlin, 2019).
Recursive feature elimination (RFE) in the random forest
algorithm is a supervised machine learning method for
ranking cuprotosis-associated genes in Parkinson’sdisease.
Predictive performance is estimated by tenfold cross-
validation and genes with relative importance >0.25 are
identified as feature genes. SVM-RFE is a small-sample
learning method that essentially bypasses the traditional
process of induction to deduction and enables efficient
“transductive inference”from training to prediction
samples, simplifying the usual classification and regression
problems.
Data processing and identification of
differentially expressed genes
The four raw datasets were pre-processed by affy in R,
including background calibration, normalization, and
log2 transformation (Irizarry et al., 2003). When multiple
probes correspond to a common gene, their average values
were taken as their expression values. In addition, the R
package “sva”was used to eliminate batch effects (Buus et al.,
2017). The limma package was applied to the four GEO cohorts
as a way to screen for differentially expressed cuprotosis-related
genes. p-values <0.05 and |log2 Fold change (FC)|>0.2 were set
as cut-off points for DEGs (Ritchie et al., 2015). When
performing differential analysis of the two PD subtypes, FDR
values <0.05 and |logFC|>1 of DEGs were considered to be
significantly different.
Functional enrichment analysis
Functional enrichment analysis, including both Kyoto
Encyclopedia of Genes and Genomes (KEGG) and Gene
Ontology (GO) analyses, was performed by the
“clusterProfiler”package in R software. The BH method was
utilized to adjust the p-value. Single-sample gene set enrichment
analysis (ssGSEA) was used to calculate the infiltration score of
16 immune cells and 13 immune-related pathways by the “gsva”
package in R software (Rooney et al., 2015). Finally, we also
examined the correlation between cuprotosis-related genes and
immune cells and immune function in Parkinson’s disease
samples.
Gene set enrichment analysis
Gene set enrichment analysis is a computational method
used to test whether genes show statistically significant and
consistent changes between two biological states. The most
significant relevant signaling pathways are identified by
10,000 alignment tests. A corrected p-value of less than
0.05 and a false discovery rate (FDR) of less than 0.05 was
used as criteria. Finally, we selected the top 5 KEGG pathways for
statistical analysis and ridge mapping using the R package
“clusterPro”.
Consensus clustering
Consensus clustering is used to calculate how many
unsupervised classes there are in a dataset. The consensus
clustering (CC) method was used. Based on the ICI
characteristics, we used the R package “ConsensusClusterPlus”
(Wilkerson and Hayes, 2010) to classify Parkinson’s patients in
GSE8397, GSE7621, GSE20163, and GSE20186 into different ICI
clusters. These results are displayed after being run 1,000 times to
verify the accuracy and reproducibility of the program, and we
use the heat map function of the R language. Consensus matrix
plots, consensus cumulative distribution function (CDF) plots,
the relative change in area under the CDF curve, and trace plots
were used to find the optimal number of clusters.
Gene set variation analysis
GSVA is a non-parametric unsupervised analysis method
that is mainly used to assess the results of gene set enrichment in
microarrays and transcriptomes. It is mainly used to assess
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FIGURE 1
Identification of Parkinson’s onset and cuprotosis-related genes in the combined expression profile of the GEO cohort. (A–D) Heat map
showing differentially expressed CRGs for the GSE8397, GSE7621, GSE20163, and GSE20186 cohorts. (E) PCA plot showing the combinatorial
expression profile of the GEO cohort. (F) PCA plot showing the combined expression profile of the GEO cohort after batch effect.
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whether different metabolic pathways are enriched between
samples by converting the gene expression matrix between
samples into the expression matrix of gene sets between
samples (Hoang et al., 2019). Fifty signature gene sets were
selected from MSigDB as reference sets. The GSVA package
and its ssGSEA function were used to obtain the GSVA score for
each gene set. The GSVA score indicates the absolute enrichment
of each gene set. The Limma package was used to compare the
differences in GSVA scores per genome between subtypes.
Statistical analysis
All analyses were performed using R version 4.1.1, 64-bit6,
and its support package. The nonparametric Wilcoxon rank sum
test was used to test the relationship between two groups of
continuous variables. Correlation coefficients were examined
using spearman correlation analysis. In all statistical
investigations, p<0.05 was considered statistically significant.
The “rms”package was used to merge the characteristic genes to
create a nomogram. Calibration curves were used to assess the
accuracy of the nomogram. The clinical utility of the column line
graphs was evaluated by decision curve analysis. PCA plots were
described using the ggplot2 package.
Results
Identification of CRGs
First, using the limma package to perform differential
analysis of CRGs in the four GEO cohorts PD and control,
respectively (Figures 1A–D), we found that DLD, DLAT, and
DBT were differentially expressed in GSE7621, NFE2L2, DLD,
MTF1, GLS, DLAT, PDHA1, PDHA1, and LIPT1 were
differentially expressed in GSE8397. SLC31A1, FDX1, and
ATP7A were differentially expressed in GSE20163, while
NLRP3, LIAS, and DBT were differentially expressed in
GSE20186. To investigate the role of cuprotosis-related genes
in the progression of Parkinson’s disease, we combined the
expression profiles of 38 normal brain substantia nigra and
62 brain substantia nigra specimens from the GSE8397,
GSE7621, GSE20163, and GSE20186 cohorts of Parkinson’s
patients (Figure 1E), which were batch processed for
subsequent analysis (Figure 1F).
Assessment of the microenvironment in
Parkinson’s disease
We quantified the ssGSEA enrichment scores for different
immune cell subpopulations, related functions or pathways in
PD, and normal controls. The abundance of immune cells and
immune functions in each sample is shown in the heat map
(Figure 2A). Figures 2B,C show the correlation heat map between
immune cells and immune function, with the darker red color
representing a larger association between the two. We compared
ssGSEA scores between PD and normal groups and showed that
B cells, mast cells, NK cells, and regulatory T cells were more
abundant in normal brain substantia nigra tissue, while
macrophages, pDCs, and Tfh were more abundant in PD
substantia nigra (Figure 2D). Human leukocyte antigen, MHC
class_I, and type II interferon responses were higher in the PD
group (Figure 2E), while APC_co_inhibition,
APC_co_stimulation, and T_cell_co-stimulation were enriched
in the normal group.
We then collected 17 reported cuprotosis-related genes, and
we showed the correlation between these genes and immune
pathways in ssGSEA results using a heat map (Figure 2F). We
found that the vast majority of CRGs act in the immune
microenvironment of PD.
Selection of characteristic genes via least
absolute shrinkage and selection
operator, random forest, and support
vector machine-recursive feature
elimination algorithms
Three machine learning algorithms were applied to select
signature genes among genes associated with Parkinson’s disease
onset and cuprotosis. Five variables, ATP7A, SLC31A1, DLAT,
PDHB, and DBT, were identified as diagnostic markers for PD by
the LASSO regression operation (Figures 3A,B). Figure 3C
represents the effect of the number of decision trees on the
error rate. The x-axis represents the number of decision trees,
while the y-axis represents the error rate. The error rate is usually
stable when we use about 104 decision trees. For the random
forest algorithm, 11 signature genes with relative importance
scores greater than two were identified, including DBT, ATP7A,
NLRP3, LIAS, DLAT, SLC31A1, DLST, PDHA1, ATP7B, LIPT1,
and FDX1 (Figure 3D). For the SVM-RFE algorithm, the error
was minimized when the number of features was 10, including
DBT, ATP7A, LIAS, NLRP3, DLST, SLC31A1, DLAT, ATP7B,
MTF1, and PDHA1 (Figure 3E). After the intersection, four
common signature genes, ATP7A, SLC31A1, DLAT, and DBT,
were finally identified (Figure 3F).
Diagnostic efficacy of characteristic genes
In the four combined GEO cohorts, the expression of the
three characteristic genes ATP7A, SLC31A1, and DBT was lower
in PD than in normal controls (Figure 4A,p<0.05), while DLAT
was not significantly different in the two groups. In contrast, in
the comparison between stage IV and V Parkinson’s disease
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FIGURE 2
Immune cell infiltration analysis. (A) Heat map of immune cells and immune function in PD group and normal control group. (B,C) Correlation
matrix of immune cells and immune function. The red color indicates a positive correlation, the blue color indicates a negative correlation, and the
darker color indicates a stronger correlation. (D,E) Comparison of the degree of immune cell infiltration and immune function between the PD group
and normal control group. (F) Correlation analysis of cuprotosis-related genes and immune cells as well as immune function. *p<0.05, **p<
0.01, ***p<0.001, ns no significance.
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patients, probably due to the small sample size, only ATP7A was
significantly different in the two groups (Figure 4B,p<0.05),
suggesting what seems to indicate their potential role in
Parkinson’s onset and progression. Based on the results of the
analysis of variance, we estimated the diagnostic performance of
the three signature genes. The AUC values of the ROC curves for
the signature genes were 0.683 for ATP7A (Figure 4C), 0.717 for
DBT (Figure 4D), and 0.811 for SLC31A1 (Figure 4E),
respectively. With GSEA, we evaluated the signaling pathways
involved in the signature genes. Our results show that ATP7A
(Figure 4F) is associated with steroid hormones, DBT is mainly
associated with Alzheimer’s disease (Figure 4G), and SLC31A1
(Figure 4H) is associated with axon guidance, calcium signaling
pathways, and Long-term potentiation.
Establishment of nomogram for
predicting Parkinson’s disease
When these three variables were integrated into one
variable, the AUC of the ROC curve was 0.752 (Figure 5A).
This suggests that the three characteristic CRGs have good
diagnostic efficiency in predicting Parkinson’sdisease
progression. Columnar line graphs were constructed to
diagnose Parkinson’s disease by integrating trait genes and
clinical traits (Figure 5B).Inthecolumnlinegraph,each
trait gene corresponds to a score, and the total score is
obtained by summing the scores of all trait genes. The total
score corresponds to the different risks of Parkinson’s. The
calibration curves showed that the column line plot was able to
accurately estimate the prediction of Parkinson’sonset
(Figure 5C). As shown in the decision curve analysis,
patients with Parkinson’scanbenefit from the column line
graph (Figure 5D).
Identification of immune-associated
cuprotosis genes subtypes in parkinson’s
disease
PD samples were clustered by the consensus clustering
method based on the expression profiles of three cuprotosis
FIGURE 3
Selection of signature genes among genes associated with Parkinson’s onset and cuprotosis. (A) Ten cross-validations of adjusted parameter
selection in the LASSO model. Each curve corresponds to one gene. (B) LASSO coefficient analysis. Vertical dashed lines are plotted at the best
lambda. (C) Relationship between the number of random forest trees and error rates. (D) Ranking of the relative importance of genes. (E) SVM-RFE
algorithm for feature gene selection. (F) Venn diagram showing the feature genes shared by LASSO, random forest, and SVM-RFE algorithms.
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signature genes. The optimal number of subtypes was 2 as
determined by consensus matrix plots, CDF plots, relative
changesinregionsundertheCDFcurve,andtraceplots
(Figures 6A–D). The two immune subtypes were named
C1 and C2. PCA demonstrated significant differences
between the subtypes (Figure 6E). The heat map (Figure 6F)
shows the differential gene expression in the two immune
subtypes.
Different immunological characteristics of
the two subtypes
As shown in Figures 7A,B, the C2 subtype had higher
immune functions such as B_cells, DCS, Neutrophils, TIL and
Treg, APC_co_stimulation, CCR, and Check-point than the
C1 subtype. Most of the immune checkpoint genes such as
CTLA4 and CD28 were also expressed more in the
FIGURE 4
Characterized gene expression, diagnostic efficacy, and enrichment analysis. (A) Box line plot depicting trait gene expression in Parkinson’s
disease and normal controls. (B) Box line plot depicting trait gene expression in braak3 and braak4 phases. (C–E) ROC curves for estimating the
diagnostic performance of the signature genes. (F–H) GSEA identifies the major signaling pathways involved in signatu re genes. *p<0.05, **p<0.01,
***p<0.001.
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C2 subtype than in the C1 subtype (Figure 7C). GSVA results
showed that TNFA_SIGNALING signals, G2/M cell cycle
checkpoints, and E2F transcriptional genes (Figure 7D) were
higher in the C2 subtype than in the C1 subtype. Overall,
C2 could be identified as an immune subtype and C1 as a
non-immune subtype.
Discussion
Parkinson’s disease is a severe neurodegenerative disorder.
The typical pathology of Parkinson’s disease is characterized by
the loss of dopaminergic neurons in the dense substantia nigra
and the aggregation of alpha-synuclein, forming Lewy vesicles
and Lewy synapses. However, the exact pathogenesis of PD is
currently unknown. To our knowledge, no previous studies have
examined the correlation between CRG and the development of
Parkinson’s disease. Surprisingly, many CRGs are differentially
expressed between the nigrostriatal and normal brain tissue in
Parkinson’s disease, and most of these genes are significantly
associated with immune function and likely influence the staging
of Parkinson’s disease, suggesting a potential role of cuprotosis in
Parkinson’s disease.
Investigations have found a higher incidence of Parkinson’s
disease in areas with higher copper emissions. But the role of
copper in Parkinson’s disease is controversial, as some evidence
suggests the need to increase copper levels, while other results
suggest the opposite (Baldari et al., 2020). The main role of
copper is mediated by its ability to trigger, maintain and even
enhance free radical production. In general copper binding to α-
FIGURE 5
Construction of column line graph based on Characteristic CRGs. (A)The ROC curves estimating the diagnostic performance of characteristic
genes. (B) Construction of column line graph integrating Characteristic CRGs for PD. in the column line graph, each variable corresponds to a score,
and the total score can be calculated by summing the scores of all variables. (C) Calibration curves to estimate the prediction accuracy of the column
line graphs. (D) Decision curve analysis showing the clinical benefit of column line graphs.
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synuclein triggers increased proteogenic fibrillation and
oxidative stress (Gou et al., 2021). However, under the
influence of copper cyanobactin (Prohaska, 2011), the
reduction of copper may be associated with iron
accumulation, while iron deposition and consequent
ferroptosis may be an important mechanism of dopaminergic
neuronal death in PD (Wang et al., 2022). In an interesting
in vitro study (Spencer et al., 2011), complexes formed by
dopamine oxidation products with copper caused severe
damage to DNA. By injecting copper sulfate directly into the
substantia nigra of mice, a decrease in dopamine, an increase in
oxidative stress, and a loss of immune response were directly
induced (Yu et al., 2008). This also suggests that the inhibition of
cuprotosis combined with immunotherapy will be the focus of
treatment for Parkinson’s patients.
An investigation pointed out that the enrichment of
senescent cells in tissues is associated with disorders of tissue
homeostasis, including Alzheimer’s and Parkinson’s, and that
copper accumulation is a common feature of senescent cells
in vitro (Masaldan et al., 2018). In addition to this, ferroptosis
inhibitors (iron chelators) have demonstrated good clinical relief
of PD symptoms, whereas the clinical translation of copper
chelators in PD has not progressed (Nunez and Chana-
Cuevas, 2018). Treatment strategies for Parkinson’s disease
must be adopted with caution due to the delicate balance of
copper homeostasis.
Among the 38 PD and 62 normal samples in the GSE8397,
GSE7621, GSE20163, and GSE20186 datasets, we selected three
signature genes (ATP7A, SLC31A1, and DBT) based on three
machine learning algorithms. These three genes were
differentially expressed in the PD and control groups and
most likely influenced the Braak staging of PD. All this
evidence can indicate the role of the signature genes in
Parkinson’s disease. The signature genes involved in this study
include ATP7A, SLC31A1, and DBT. ATP7A is widely
recognized as a copper-transporting ATPase due to mutations
in its gene that cause impaired copper transport and further cause
the neurological genetic disorder Menkes disease (Li et al., 2018).
ATP7A is involved in axonal growth, synaptic integrity, and
neuronal activation and has an important role in the root of
FIGURE 6
Construction of two subpopulations based on cuprotosis-related genes in the GEO cohort. (A) Heat map of the consensus matrix at k = 2. (B)
Consensus CDF at k = 2–9. (C) Relative change in area under the CDF curve. (D) Trace plot of sample classification when k = 2–9. (E) 3DPCA plot
showing that cuprotosis-associated genes effectively classify Parkinson’s patients into two subgroups (C1 and C2). (F) Heat map showing differential
gene expression in the two immune subtypes.
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Zhao et al. 10.3389/fgene.2022.1010361

stability for neurological function (Kaler, 2011). The SLC31A1
(solute carrier family 31 member 1) gene, also known as CTR1
(copper transporter protein 1), encodes a high-affinity copper
transporter protein in cell membranes that act as a homotrimer
to influence dietary copper uptake. Its more studied in tumors,
such as pancreatic cancer (Yu et al., 2019), colorectal cancer
(Barresi et al., 2016), and lung cancer (Barresi et al., 2016), as a
means of copper depletion affecting the prognosis of cancer
patients. DBT is a component of the branched-chain α-keto acid
dehydrogenase complex, and its deficiency allows the
accumulation of branched-chain amino acids and their
harmful derivatives in the body (Podebrad et al., 1999). An
association between Alzheimer’s disease and Parkinson’s
disease and the 2-oxoglutamate dehydrogenase gene has been
reported (Hengeveld et al., 2002).
We constructed two isoforms from three cuprotosis genes
based on machine learning and immune expression profiles. The
C2 subtype exhibited higher immune cell infiltration and
immune function compared to the C1 subtype. Therefore, our
classification reflects the immune status of Parkinson’s disease,
which may help in the diagnosis and treatment of PD. Although
machine learning algorithms can identify cuprotosis-related
genes in the characterization of Parkinson’s immune
progression, experiments are still needed to further elucidate
the mechanisms of the characterized genes.
Conclusion
Our results identified three characteristic cuprotosis-related genes
ATP7A, SLC31A1, and DBT involved in the immune process of
Parkinson’sdisease.Inaddition,Parkinson’s disease samples were
classified into immune and non-immune subtypes by a new
molecular classification. However, little is known about the
relationship between specific genes and PD, and must be
performed in vitro and in vivo to verify our conjectures. This
FIGURE 7
The two subtypes have different immunological features and molecular mechanisms. (A,B) Comparison of the degree of immune cell
infiltration and immune function between the two subtypes. (C) Box plot showing the mRNA expression of signature genes in the two subtypes. (D)
Heat map showing the level of enrichment of the set of signature genes in the two subtypes. *p<0.05; **p<0.01 and ***p<0.001.
Frontiers in Genetics frontiersin.org11
Zhao et al. 10.3389/fgene.2022.1010361

study provides important information to elucidate the physiological
and pathological processes of cuprotosis in PD. Overall, our findings
may contribute to the design of better immunotherapies for
Parkinson’s disease based on the mechanisms of cuprotosis.
Data availability statement
Publicly available datasets were analyzedinthisstudy.Thisdata
can be found here: The datasets analyzed in the current study are
available in the GEO (https://www.ncbi.nlm.nih.gov/geo/). All raw
data and original images can be found in the jianguoyun (https://
www.jianguoyun.com/p/DQEJLh0Q0pH7ChjCutoEIAA).
Author contributions
SZ and CC conceived the study and participated in the study
design, performance, coordination and manuscript writing. SZ, WJ,
and YS performed the literature review and graphics production. LZ,
GL, HC and WH helped with the final revision of this manuscript.
All authors reviewed and approved the final manuscript.
Funding
This work was supported by General project of Wuxi
commission of Health (MS201933, T202120).
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their
affiliated organizations, or those of the publisher, the
editors and the reviewers. Any product that may be
evaluated in this article, or claim that may be made by
its manufacturer, is not guaranteed or endorsed by the
publisher.
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