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Identification and validation of
neurotrophic factor-related gene
signatures in glioblastoma and
Parkinson’s disease
Songyun Zhao
1
†
, Hao Chi
2
†
, Qian Yang
3
†
, Shi Chen
3
†
, Chenxi Wu
4
,
Guichuan Lai
5
,KeXu
6
,KeSu
2
, Honghao Luo
7
, Gaoge Peng
2
,
Zhijia Xia
8
*, Chao Cheng
1
*and Peihua Lu
4,9
*
1
Department of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi,
Jiangsu, China,
2
Clinical Medical College, Southwest Medical University, Luzhou, China,
3
Clinical
Molecular Medicine Testing Center, The First Affiliated Hospital of Chongqing Medical University,
Chongqing, China,
4
Department of Oncology, Wuxi People’s Hospital Affiliated to Nanjing Medical
University, Wuxi, Jiangsu, China,
5
Department of Epidemiology and Health Statistics, School of Public
Health, Chongqing Medical University, Chongqing, China,
6
Department of Oncology, Chongqing
General Hospital, Chongqing, China,
7
Department of Radiology, Xichong People’s Hospital,
Nanchong, China,
8
Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-
University Munich, Munich, Germany,
9
Department of Clinical Research Center, Wuxi People’s Hospital
of Nanjing Medical University, Wuxi, Jiangsu, China
Background: Glioblastoma multiforme (GBM) is the most common cancer of the
central nervous system, while Parkinson’s disease (PD) is a degenerative neurological
condition frequently affecting the elderly. Neurotrophic factors are key factors
associated with the progression of degenerative neuropathies and gliomas.
Methods: The 2601 neurotrophic factor-related genes (NFRGs) available in the
Genecards portal were analyzed and 12 NFRGs with potential roles in the
pathogenesis of Parkinson’s disease and the prognosis of GBM were identified.
LASSO regression and random forest algorithms were then used to screen the key
NFRGs. The correlation of the key NFRGs with immune pathways was verified using
GSEA (Gene Set Enrichment Analysis). A prognostic risk scoring system was
constructed using LASSO (Least absolute shrinkage and selection operator) and
multivariate Cox risk regression based on the expression of the 12 NFRGs in the
GBM cohort from The Cancer Genome Atlas (TCGA) database. We also
investigated differences in clinical characteristics, mutational landscape, immune
cell infiltration, and predicted efficacy of immunotherapy between risk groups.
Finally, the accuracy of the model genes was validated using multi-omics mutation
analysis, single-cell sequencing, QT-PCR, and HPA.
Results: We found that 4 NFRGs were more reliable for the diagnosis of Parkinson’s
disease through the use of machine learning techniques. These results were
validated using two external cohorts. We also identified 7 NFRGs that were
highly associated with the prognosis and diagnosis of GBM. Patients in the low-
risk group had a greater overall survival (OS) than those in the high-risk group. The
nomogram generated based on clinical characteristics and risk scores showed
strong prognostic prediction ability. The NFRG signature was an independent
prognostic predictor for GBM. The low-risk group was more likely to benefit from
immunotherapy based on the degree of immune cell infiltration, expression of
immune checkpoints (ICs), and predicted response to immunotherapy. In the end,
Frontiers in Immunology frontiersin.org01
OPEN ACCESS
EDITED BY
Zhijie Han,
Chongqing Medical University, China
REVIEWED BY
Qihang Yuan,
Dalian Medical University, China
Yingjun Zhao,
Xiamen University, China
*CORRESPONDENCE
Zhijia Xia
Zhijia.Xia@med.uni-muenchen.de
Chao Cheng
Mr_chengchao@126.com
Peihua Lu
lphty1_1@njmu.edu.cn
†
These authors have contributed
equally to this work
SPECIALTY SECTION
This article was submitted to
Multiple Sclerosis
and Neuroimmunology,
a section of the journal
Frontiers in Immunology
RECEIVED 04 November 2022
ACCEPTED 17 January 2023
PUBLISHED 07 February 2023
CITATION
Zhao S, Chi H, Yang Q, Chen S, Wu C,
Lai G, Xu K, Su K, Luo H, Peng G, Xia Z,
Cheng C and Lu P (2023) Identification and
validation of neurotrophic factor-related
gene signatures in glioblastoma and
Parkinson’s disease.
Front. Immunol. 14:1090040.
doi: 10.3389/fimmu.2023.1090040
COPYRIGHT
© 2023 Zhao, Chi, Yang, Chen, Wu, Lai, Xu,
Su, Luo, Peng, Xia, Cheng and Lu. 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.
TYPE Original Research
PUBLISHED 07 February 2023
DOI 10.3389/fimmu.2023.1090040
2 NFRGs (EN1 and LOXL1) were identified as crucial for the development of
Parkinson’s disease and the outcome of GBM.
Conclusions: Our study revealed that 4 NFRGs are involved in the progression of
PD. The 7-NFRGs risk score model can predict the prognosis of GBM patients and
help clinicians to classify the GBM patients into high and low risk groups. EN1, and
LOXL1 can be used as therapeutic targets for personalized immunotherapy for
patients with PD and GBM.
KEYWORDS
PD, GBM, NFRG, immune cell infiltration, machine learning
Introduction
Glioblastoma, also known as glioblastoma multiforme (GBM), is
classified as a grade IV glioma by the World Health Organization and
is the most common primary brain tumor, and the most aggressive
form of malignancy (1). Despite the significant advances in molecular
understanding of GBM pathogenesis, such as the IDH mutation
status (2), the median patient survival time is just 14–16 months,
and the 5-year survival rate is only 6.8% (3). The prognosis for GBM
patients is still poor, despite rigorous treatment strategies such as
surgical resection, radiation therapy, and chemotherapy. Most of the
molecular targeted therapies and immunotherapies are in clinical
trials, there is need for the development of more effective treatment
strategies for GBM (4–6).
Parkinson’s disease (PD) is the second most common neurological
disorder after Alzheimer’s disease, which affects roughly 1.2% of
individuals over 65 (7,8). The primary symptom of Parkinson’s
disease is loss of motor coordination brought on by the degradation
of dopamine neurons in the substantia nigra (SN), which is followed by
striatal dopaminergic depletion and the development of Lewy bodies
(PD) (9,10). Factors such as oxidative stress, aging, genetics, and
environmental factors may all have a role in the degenerative loss of
dopaminergic neurons in Parkinson’s disease (11).
Cancer is characterized by unrestrained cell growth and resistance
to cell death, which is in contrast to the excessive neuronal cell death
observed in PD (12). In depth analysis of the pathogenesis of the two
diseases suggests that patients with neurodegenerative diseases such
as PD are less likely to develop cancer (13). Reports from
epidemiological studies also point to a decreased risk of main nerve
center (CNS) tumors in Parkinson’s disease patients (14,15). At the
genomic level, genes such as the p53 tumor suppressor gene and the
epidermal growth factor receptor EGFR that are downregulated in PD
are often upregulated in tumors (16,17). Therefore, there is need for
better understanding of potential pathological mechanisms and
genetic targets of PD and GBM that will help identify possible
shared drug targets to treat both diseases.
Nerve growth factor (NGF), brain-derived growth factor (BDNF),
and other proteins that make up the neurotrophic factors family are
crucial for the growth, survival, and apoptosis of neurons (18).
Neurotrophic factors regulate cell development and apoptosis by
interacting with extracellular receptors and transmitting signals about
neuronal cell survival and apoptosis to the cell interior (19). Several
studies have shown that BNDF expression is reduced in patients with
several neurodegenerative diseases, including Parkinson’sdisease,and
that reduced BDNF levels are an important cause of cognitive
impairment in these patients (20). Additionally, numerous studies
carried out on animal models have demonstrated that raising plasma
BDNF levels may enhance cognition (21–23). On the other hand,
neuronal proliferation in the tumor microenvironment is essential for
the development of cancer, and neurotrophic factors are essential for
the communication between tumor cells and nerves (24). Elevated
plasma levels of BDNF have been found in several types of cancer and
play an important role in tumor proliferation, survival, migration, and
invasion (25). Neurotrophic growth factors generated by cancer cells
can also stimulate the formation of neurons in solid tumors, while the
release of neurotransmitters from nerve endings stimulates tumor
growth and enhances tumor angiogenesis (26,27). NGF regulates
glioma growth and induces cell differentiation through the
involvement of the Promyosin receptor kinase A (TrkA) receptor
(28). Astrocytes mediate paracrine secretion through glial cell-derived
neurotrophic factor (GDNF) and RET (Rearranged during
Transfection) signaling to regulate glioma cell invasion. The
knockdown of GDNF or its receptor in glioma cells significantly
reduces tumor progression in vitro (29,30).
The recent advancements in molecular biology and microarray
sequencing technologies has led to the identification of new
biomarkers with prognostic and diagnostic potential for various
neurodegenerative diseases and neuro-oncology (31,32). Although
several studies have investigated the role of neurotrophic factors in
various cancers, neurodegenerative diseases, and cerebrovascular
lesions, there is still a gap in identifying neurotrophic factor-related
genes with diagnostic potential for PD and exploring
immunotherapeutic targets affecting the prognosis of GBM. In this
study, GEO and TCGA datasets were used analyze the relationship
between differences in expression of NFRGs and the diagnosis of PD
and the prognosis of GBM. We then analyzed the potential of two
NFRGs—EN1 and LOXL1—as therapeutic targets common to PD
and GBM. We also developed a prognostic model for GBM based on
NFRGs to showcase the value of NFRGs in predicting the prognosis of
GBM patients, enhancing the diagnosis of PD patients, and exploring
more efficient personalized therapeutic regimens through a thorough
analysis of genomic data and clinically relevant data.
Zhao et al. 10.3389/fimmu.2023.1090040
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Materials and methods
Source of raw data
Three PD datasets, GSE7621, GSE20163, and GSE49036, were
downloaded from the NCBI Gene Expression Omnibus (GEO;
https://www.ncbi.nlm.nih.gov/geo/). The GSE7621 and GSE49036
datasets were generated using the GPL570 (HG-U133 Plus 2)
AffymetrixHumanGenomeU133Plus2.0array,whilethe
GSE2016 dataset was generated using GPL96 [HG-U133A]
Affymetrix Human Genome U133A array. GSE7621 dataset
consisted of 16 brain nigrostriatal samples from Parkinson’s disease
patients and 9 normal nigrostriatal samples from controls. GSE20163
dataset, which served as an external validation cohort, consisted of 8
PD brain substantia nigra samples and 9 control samples. GSE49036
was used as a validation cohort for clinical staging and included brain
substantia nigra samples from 8 Braak stage 0, 5 Braak stages 1-2, 7
Braak stages 3-4 and 8 Braak stages 5-6 patients.
RNAseq data, mutation data, and clinicopathological
characteristics of TCGA-GBM, consisting of 169 glioma samples,
were retrieved from the UCSC Xena website (https://xena.ucsc.edu/)
Gene expression data for 249 glioma patients were retrieved from the
China Glioma Genome Atlas (CGGA) data portal (http://www.cgga.
org.cn) and were used to generate a validation model. All expression
data were retrieved in TPM format. Batch correction and integration
of the two sets of gene expression data were carried out using the
“limma”and “sva”(33) packages in R. The detailed flow chart is
shown in Figure 1.
2601 neurotrophic factor-related genes were downloaded from
the GeneCard database (https://www.genecards.org/)(34).
Differential gene expression analysis was performed on the TCGA
cohort using the “limma”package in R, with | log2FC | > 1.0 and FDR
(false discovery rate) < 0.05 as the thresholds. The cutoff p-value of
the differentially expressed NFRGs (DENFRGs) for the GEO cohort
was set to 0.05, which satisfied the condition of |log2FC|>0.5 The
“affy”package in R was used to perform background calibration,
normalization, and log2 conversion on all GEO raw data sets (35,36).
The expression values of multiple probes that matched the same gene
were averaged. Protein interactions and gene enrichment analysis was
carried out using the differentially expressed genes identified from the
GEO cohort. The hub genes in the network were screened and
visualized using “Cytoscape”software following PPI (Protein-
Protein Interaction Networks) analysis on the String online platform.
Characteristic genes in Parkinson’s disease
To identify signature genes, we used two machine learning
methods: LASSO regression analysis and random forest. LASSO is
utilized as a dimensionality reduction approach to perform variable
screening and complexity adjustment when fitting a generalized linear
model. The LASSO analysis was carried out with a penalty parameter
FIGURE 1
A detailed flow chart showing the NFRGs in GBM and PD.
Zhao et al. 10.3389/fimmu.2023.1090040
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and a 10-fold cross-verification using the glmnet program (37). RF
(Random Forest) is a combination of classifiers with a tree-like
structure, and a minimum error regression tree was built to select
key variables using the software package “randomForest”.After
tenfold cross-validation, the eight genes with the highest relative
importance were used to measure predictive performance.
Functional enrichment and gene
set enrichment analysis
The “clusterProfiler”package of R software was used to carry out
the functional enrichment analysis, which included KEGG and GO
analysis (38). We adjusted the P values using the Benjamini -
Hochberg (BH) technique. A computational technique was used in
the gene set enrichment analysis to identify genes that exhibited
statistically significant and consistent changes between two biological
states. 10,000 permutation tests were used to determine the most
important and pertinent signaling pathways. Genes with a corrected
P-value and false discovery rate (FDR) below 0.05 were considered to
be significant. Statistical analysis and ridge mapping were carried out
using the “clusterPro”package in R, which is a non-parametric
unsupervised analytic method that is widely employed to evaluate
gene set enrichment outcomes in microarrays and transcriptomes. It
is primarily used to determine if certain metabolic pathways are
enriched across samples by transforming the expression matrix of
genes across samples into the expression matrix of gene sets (39).
From MSigDB, 50 reference gene sets for hallmark genes were chosen.
Gene set variation analysis (GSVA) using the ‘GSVA’package in R
was performed to provide insight into the heterogeneity of biological
processes between different clusters.
Development and validation of prognostic
features in GBM
Batch effects between TCGA and CGGA data were removed by
creating precise models using the “sva”package in R. Selected NFRGs
underwent Minimum Absolute Shrinkage and Selection Operator
(LASSO) regression analysis, with the “glmnet”package in R being
used to minimize the number of genes in the final risk model. Models
were then built using multivariate Cox regression analysis using the
following equation: risk score = (Expi), where Expi was the expression
value for each NFRG and was the matching regression coefficient (40,
41). The median risk score was used to split all patients into high- and
low-risk groups. The “survminer”and “ggrisk”packages in R were
used to create survival curves and risk maps to display the disparities
in survival and status of each patient. A separate external cohort, the
CGGA cohort, was also employed to evaluate the effectiveness of the
prognostic model.
A nomogram was created using risk score and clinicopathological
features. Calibration charts were internally validated to ensure accuracy
of the models. Decision curve analysis (DCA) was carried out using
“ggDCA”package in R to evaluate the net clinical benefit of the models
(42). We also plotted subject operating characteristic curves using the
“timeROC”package in R to evaluate how well risk scores performed in
predicting 1-year, 3-year, and 5-year OS in LGG patients (43).
Prognostic characteristics of the tumor
immune microenvironment
and mutation landscape
The relative enrichment scores of tumor-infiltrating immune cells
(TIICs) were calculated using the R script ssGSEA (single-sample
genomic enrichment analysis). We utilized CIBERSORT to calculate
and compare the proportion of immune cell types between the low-
and high-risk categories, with the sum of all anticipated immune cell
type scores in each sample being equal to 1 (44). The TICCs data was
downloaded from TIMER 2.0 (http://timer.cistrome.org). The results
from TIMER, CIBERSORT, amounts, MCP-counter, xCELL, and
EPIC algorithms were also compared between the two groups. The
“oncoplot”function in the “maftools”package of the R software was
used to create two waterfall plots to compare the specific mutation
characteristics between the high- and low-risk groups.
Gene set cancer analysis database
The tumor genomic analysis platform GSCALite (http://bioinfo.
life.hust.edu/web/GSCALite/) integrates genomic data for 33 tumor
types from the TCGA library, GDSC (Genomics of Drug Sensitivity in
Cancer), CTRP (The Cancer Therapeutics Response Portal) medication
response data, and normal tissue data from GTEX (Genotype-Tissue
Expression) for comprehensive genomic analysis (45).
Immunotherapeutic response prediction
and drug sensitivity assessment
The Immunological Cell Abundance Identifier (ImmuCellAI) is a
computer program launched in 2020 to predict immunological
checkpoint reactions based on the abundance of TICCs, particularly
certain T cell subpopulations. Comprehensive immunogenomic
analysis findings are provided by the Cancer Immunome Atlas
(TCIA) online software. Using a scale from 0 to 10, the
Immunophenotype Score (IPS) quantifies the immunogenicity of
tumors (46). IPS can be used to predict response to immune
checkpoint inhibitors. The “prophytic”package in R was used to
compute the half-maximal inhibitory concentration (IC50) of
samples from the high and low risk score groups in order to test
the ability of the risk score to predict sensitivity of samples to
chemotherapy and molecular medicines. Zaoqu Liu et al. from the
First Affiliated Hospital of Zhengzhou University developed THE
BEST website (http://rookieutopia.com/). The database contains
sequencing data from a variety of tumors after treatment with
immune checkpoint inhibitors
Tumor Immune Single Cell Hub database
Tumor Immune Single-Cell Hub (TISCH; http://tisch.comp-
genomics.org) is an extensive single-cell RNA-seq database
dedicated to TME. It enables comprehensive analysis of TME
heterogeneity across different datasets and cell types.
Zhao et al. 10.3389/fimmu.2023.1090040
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QT-PCR and immunohistochemistry
Human astrocytes (HA), U87 and A172 glioma cells, obtained
from the Center for Experimental Medicine, Southwestern Medical
University. All cells were grown in 10% fetal bovine serum-
supplemented DMEM. Cells were incubated at 5% CO2 and 37°C.
TRIzol reagent was used to isolate RNA, while PrimeScriptTM RT kit
was used to perform reverse transcription. Quantitative PCR was
carried out using Takara’s SYBR Green PCR Master Mix on the
StepOnePlus system. Ploidy changes at the gene level were
determined using the 2-DDCT method, with GAPDH as the
normalization gene. The primer sequences involved in this study
are as follows.
EN1:FORWARD : GAAGAACGAGAAGGAGGACAAGCG,
REVERSE: CGTGGTGGTGGAGTGGTTGTAC.
LOXL1:FORWARD : GAAGAACCAGGGCACAGCAGAC,
REVERSE ATGTCCGCATTGTAGGTGTCATAGC.
GAPDH:FORWARD : ATGGGGAAGGTGAAGGTCG,
REVERSE : GGGGGTCATTGATGGCAACAATA. Each PCR
reaction was performed in triplicate.
Transcriptomics and proteomics methods were used to study
protein expression at the RNA and protein levels in human tissues
and organs, using data found in Human Protein Atlas (HPA, https://
www.proteinatlas.org/).
Statistical analysis
All analyses were conducted using R version 4.1.1, 64-bit6.
Prognosis and patient survival in various subgroups were compared
using Kaplan-Meier survival analysis and the log-rank test. The
nonparametric Wilcoxon rank sum test was used to compare
continuous variables between the two groups, while Kruskal-Wallis
test was employed for comparisons among more than two groups.
Univariate and multivariate Cox regression (R package “survival”)
analyses were used to identify clinical traits with prognostic potential
in the high- and low-risk groups. Spearman correlation analysis was
used to assess correlation coefficients. P < 0.05 was regarded as
statistically significant in all statistical investigations. The ROC
curves, the nomogram model and the Concordance Index were
generated using the “survivalROC”,“rms”and the “pec”(C-index)
packages in R, respectively. The changes in gene expression between
the two isoforms were determined using principal component
analysis (PCA).
Results
Identification of neurotrophic
factor-related genes associated
with Parkinson’s disease and GBM
The limma package was used for background correction and
normalization of expression data from the GSE7621 dataset. Batch
effects were removed using the sva package, and the box plot
corresponding to the processing results is shown in Figure 2A. The
expression of each of the 145 differentially expressed genes (DEGs) in
the GEO cohort is shown in Figure 2B. GO analysis revealed that the
DEGs were enriched in “positive control of kinase activity”in the
biological process (BP) category, “neuronal cell body”in the cellular
component (CC) category and the “nuclear glucocorticoid”in the
Molecular Function (MF) category (Figure 2D). KEGG pathway
analysis showed that the DEGs were more closely related to
“Neuroactive ligand-receptor interaction”(Figure 2C). We also
constructed PPI networks to investigate the interaction of the
proteins encoded by the DEGs based on betweenness centrality,
with the central genes in the network being marked in
red. (Figure 2E).
We identified 847 DEGs from differential gene expression analysis
of NFRGs between tumor and normal tissues of the TCGA-GBM
cohort. Out of the total DEGs, 489 genes were down-regulated
whereas 358 genes were up-regulated in tumor tissue (Figure 3A).
Univariate Cox regression analysis identified 104 differentially
expressed NFRGs with prognostic potential for GBM in the TCGA
cohort (Figure 3B). We then determined the NFRGs that overlapped
from the univariate cox analysis of the TCGA cohort and the
differentially expressed NFRGs obtained from the GEO cohort. A
total of 12 NFRGs overlapped between the two cohorts indicating that
they were associated with the occurrence of Parkinson’s disease and
prognosis of GBM (Figure 3C). Figure 3D shows the correlation of the
expression of these 12 NFRGs in the TCGA cohort, while Figure 3E
shows the localization of these 12 NFRGs on chromosomes, with EN1
being localized on chromosome 2.
Selection of Parkinson’s disease signature
genes using LASSO regression and random
forest algorithm
Two machine learning algorithms were used to identify key genes
among the 12 NRFGs. The best lambda for the LASSO algorithm was
0.138 after ten cross-validations. Due to higher accuracy in
comparisons, we used the minimum criterion for the LASSO
classifier, and identified 4 key genes, including IRF7, EN1, PLOD3,
and LOXL1 (Figures 4A,B). The influence of the number of decision
trees is shown in Figure 4C. The x-axis shows the number of decision
trees, while the y-axis shows the mistake rate. The top 8 key genes
with relative relevance scores identified using the random forest
technique were PCSK1, S100A4, EN1, CEBPB, IRF7, L1CAM,
PLOD3, and LOXL1 (Figure 4D). Four key genes, IRF7, EN1,
PLOD3, and LOXL1, overlapped from results of lasso regression
and random forest algorithm analysis (Figure 4E). Figure 4F shows
the correlation of these four feature genes in the GEO cohort.
Diagnostic efficacy and enrichment
analysis of characteristic genes
We then estimated the diagnostic performance of the four key
genes. The AUC values of the ROC curves were 0.799 for LOXL1
(Figure 5A), 0.778 for PLOD3 (Figure 5B), 0.861 for IRF7 (Figure 5C),
and 0.819 for EN1 (Figure 5D). GSEA was used to evaluate the
Zhao et al. 10.3389/fimmu.2023.1090040
Frontiers in Immunology frontiersin.org05
signaling pathways associated with the signature genes. Our results
showed that LOXL1 (Figure 5E), PLOD3 (Figure 5F), IRF7
(Figure 5G), and EN1 (Figure 5H) were mainly associated with
functions of the nervous system and the transmission of
neurotransmitters. For example, EN1 was strongly correlated with
spinal cerebellar ataxia and dopaminergic synapse-related pathways.
The GSVA results demonstrated the correlation of the four signature
genes with the HALLMARK pathway (Figure 5I).
Assessment of the microenvironment in PD
We also quantified the ssGSEA enrichment scores for several
immune cell subpopulations, associated PD functions or pathways,
and healthy controls. A heat map was used to display the number of
TIICs and immunological responses in each sample (Supplementary
Figure 1A). Supplementary Figures 1B,Cshow heat maps displaying
the relationship between TIICs and immune function, with darker red
denoting a stronger correlation between the two. The association
between the four key NFRGs and immune-related pathways in the
ssGSEA data was also demonstrated using a heat map
(Supplementary Figure 1D). These results indicated that the four
NFRGs play a role in the immune microenvironment of PD.
Internal and external data validation
of characteristic genes
In the GSE7621 internal validation cohort, the expression of EN1
and LOXL1 was lower, while the expression of IRF7 and PLOD3 was
higher in PD tissues than in normal controls (Figure 6A). The
expression of the four signature genes in the GSE20163 external
validation cohort was similar to that in the internal validation cohort,
except for IRF7 (Figure 6B). The difference in results may be due to the
small sample size. In the GSE49036 dataset consisting of patients with
Parkinson’s disease at different Braak stages, there was significant
difference in the expression of EN1, PLOD3 and LOXL1 in the
different Braak stages 0 to 6 of Parkinson’s disease. However, there
was no significant difference in expression of IRF7 among the different
stages (Figures 6C–F). These results suggest that these NFRGs play a
role in the pathogenesis and progression of Parkinson’s disease.
Construction and validation of predictive
models for NFRGs in GBM
A risk-scoring model was developed based on the 12 NRFGs
obtained in Figure 3 to identify potential prognostic biomarkers for
AB
DE
C
FIGURE 2
Expression of differential NFRGs in Parkinson’s disease, enrichment analysis, and construction of protein interaction network. (A) Box line plot of the
GSE7621 dataset samples corrected for batch-to-batch differences after removal. (B) Heat map showing the expression of all DEGs in Parkinson’s
samples. (C) Network diagram of KEGG enrichment analysis. (D) Circle diagram of GO enrichment analysis. (E) Interaction plots of proteins. Red
represents hub genes.
Zhao et al. 10.3389/fimmu.2023.1090040
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GBM. The NFRGs with prognostic potential were subjected to LASSO
regression analysis to reduce the number of genes in the final risk
model. Ten NFRGs were identified from this step (Figures 7A,B).
Multivariate Cox analysis identified 7 NFRGs, including EN1,
TUBB2A, HSPB1, LOXL1, RGS4, L1CAM, and GPR143 as
independent prognostic factors. Risk scores were calculated
using the following formula: risk score = expression level of EN1*
0.17 + expression level of TUBB2A* 0.09 + expression level of
HSPB1*0.14+ expression level of LOXL1*0.19 + expression level of
RGS4*0.09 + expression level of L1CAM*0.08 + expression level
of GPR143*0.20.
Patients in the TCGA cohort were classified into high-risk and
low-risk groups based on the median risk score. Survival curves
revealed that patients in the high-risk group had lower overall
survival (OS) compared to the low-risk group in the TCGA and
CGGA cohorts (Figures 7C,D, P<0.05). Furthermore, the risk score
was effective at predicting OS in the TCGA cohort. (AUCs for 1-, 3-,
and 5-year OS were 0.734, 0.823, and 0.942, respectively; Figure 7E).
However, since GBM patients have dismal prognosis, the AUC values
in the CGGA sample were not favorable (Figure 7F). In both the
TCGA and CGGA cohorts, the area under the curve (AUC) for
the risk score over three years was greater than the AUC values for the
other clinicopathological features (Figures 7G,H). Risk maps were
used to display survival results from the TCGA and the CGGA
cohorts, while heat maps were used to display variations in the
expression of the seven NFRGs across the various risk groups
(Figures 7I,J).
PCA and t-SNE analyses were then performed using the NFRG
classification in the expression profiles of the seven models. In the
TCGA cohort (Supplementary Figures 2A,B) and the CGGA cohort
(Supplementary Figures 2C,D),oursignaturesyieldedresults
indicating a different distribution between the high-risk and low-
risk groups. These findings imply that prognostic model can
distinguish between high and low risk groups.
A
B
D
E
C
FIGURE 3
Identification of prognosis-related NFRGs in GBM patients in the TCGA cohort. (A) Volcano plot of DEGs. (B) Forest plot of univariate cox analysis.
(C) The intersection of DEGs and univariate cox results for the GEO cohort. (D) Correlation analysis of 12 NFRGs. (E) Chromosomal localization of 12
NFRGs.
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Establishment of a prognostic
nomogram and clinical features
Univariate and multivariate Cox analyses showed that risk scores
were independent prognostic factors for GBM patients compared to
other common clinical characteristics (Figures 8A,B). In the CGGA
cohort, results of both univariate and multivariate cox analyses showed
that risk score was a prognostic factor independent of age, IDH mutation
status, or MGMTp_methylation status (Supplementary Table 1). To
determine the clinical applicationoftheriskmodels,age,sex,IDH
mutation status, and risk score were included in a nomogram used to
predict overall survival in patients with GBM based on the TCGA cohort
(Figure 8C). We found that the risk score had the biggest influence in
predicting OS, an indication that prognosis of GBM could be predicted
using a risk model based on the seven NFRGs. At 1, 1.5, and 2 years, the
calibration curves demonstrated a reasonable agreement between
expected and observed values (Figure 8D). The three-year DCA curves
(Figure 8E) and the temporal c-index values (Figure 8F)indicatedthat
our model has the highest net benefit and that the risk model constructed
based on the 7 NFRGs has more influence in clinical decision-making
than the traditional model. The histogram of the chi-square test showed
that risk grouping was only associated with whether IDH was mutated
(Figure 8G). To validate these findings, we evaluated the relationship
between risk score and clinical characteristic and found that individuals
without IDH mutations were associated with higher risk scores
(Figures 8H–J).
NFRGs risk score predicts
immune cell infiltration
To determine the relationship between risk scores and immune
cells and functions, we measured the enrichment scores of various
immune cell subpopulations, associated activities, or pathways using
the “cibersort”and “ssGSEA”. The low-risk group was associated with
alargerinfiltration of monocytes and M2-type macrophages
(Figure 9A). In addition, the high-risk group showed a higher type
2 interferon response compared to the low-risk group, while the low-
risk group had a higher type 1 interferon response (Figure 9B). The
risk score was associated with the quantity of immune cells in the
GBM tumor microenvironment determined by several methods using
Spearman correlation analysis (Figure 9C). Furthermore, we
discovered that a small number of immune checkpoints, namely
CD48 and IDO1, were substantially expressed in the low-risk group
compared to the high risk group (Figure 9D). These results imply that
although patients in the high-risk group have a worse prognosis, they
may be more responsive to immunotherapy due to their more active
immune function. GSEA was used to investigate potential changes in
biological function between risk groups based on the various
prognoses of patients in the high-risk and low-risk groups. We
chose the top 8 enriched signaling pathways based on normalized
enrichment scores (NES) and p-values (Figure 9E). Surprisingly,
lower risk scores were associated with Alzheimer’sdiseaseand
Parkinson’s disease, which is in line with the theme of our study.
AB
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FIGURE 4
Selection of Parkinson’s disease-related hallmark genes among NFRGs. (A) Ten cross-validations of the LASSO model’s improved parameter selection. Each
curve represents on gene. (B) Construction of linear models (Lasso) and visualization by coefficients. (C) The best lambda is where vertical dashed lines are
drawn. The error rate for random forests with the number of classification trees. (D) Importance ranking of all selected genes. (E) lasso regression analysis
and random forest for the intersection of genes. (F) Spearman correlation analysis of the four NFRGs. *p < 0.05, **p < 0.01, ***p < 0.001.
Zhao et al. 10.3389/fimmu.2023.1090040
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Mutation landscape of risk groupings and
multi-omics mutation analysis of NFRGs
To determine the molecular mechanisms driving the abnormal
expression of these seven NFRGs, we explored the many histological
levels, including genomes and copy numbers. Analysis of single
nucleotide gene variant (SNV) data revealed that missense
mutations in NFRGs were the most frequent variant
categorization in the TCGA-GBM cohort, whereas single
nucleotide polymorphism was the most common variation type.
Among the SNV categories, C>T showed the highest prevalence
(Supplementary Figure 3A). To summarize the ratio of pure and
heterozygous mutations in the sample’s NFRGs, copy number
variation was examined (Supplementary Figure 3B). We found
that 17 GBM patients had mutations, with L1CAM mutations
being the most common (Supplementary Figure 3C). Additionally,
the Spearman’s correlation coefficient analysis of between copy
number variations and gene expression showed that L1CAM copy
number variations were downregulated in GBM whereas TUBB2A,
HSPB1, LOXL1, RGS4, and GPR143 copy number variations were
upregulated (Supplementary Figure 3D). Heterozygous variants of
HSPB1 were present in most samples, and individual analysis
showed that LOXL1 and L1CAM were copy number deletions.
Whereas the pure-sibling mutation of GPR143 is mainly a copy
number reduction (Supplementary Figures 3E,F), HSPB1 and
L1CAM primarily amplified pure heterozygous mutations,
suggesting that abnormal gene expression may be caused by both
copy number variation and single nucleotide variation. The
relationship between NFRGs expression and the activity of
pathways linked to cancer was further examined. The findings
demonstrated that NFRGs contributed to the inhibition of
hormonal pathways in GBM patients and activation of the EMT,
PI-3K-AKT, and TSC-mTOR pathways (Supplementary Figure 3G).
We further explored the differential expression of NFRGs in the
GDSC and Cancer Therapy Response Portal databases, their
corresponding drug sensitivity (Supplementary Figures 3H,I).
This suggested that the expression of the proposed risk profile
genes may be exploited to develop agents for sensitizing drugs as
well as predict chemotherapeutic drug sensitivity in patients.
In further experiments, we examined the correlation between risk
score and tumor mutational load (TMB) (Supplementary Figure 4B)
as well as differences in TMB among different risk subgroups
(Supplementary Figure 4A). Results showed that the TMB was
higher in the low-risk group. Thus, we generated two waterfall plots
to explore the detailed mutational characteristics between high- and
low-risk populations. The results indicated that PTEN, TP53, and
TTN were the most commonly mutated genes in both risk groups
(Supplementary Figures 4C,D).
AB D
EF
GH
I
C
FIGURE 5
Construction, diagnostic efficacy, and enrichment analysis of histograms of characteristic NFRGs. (A–D) ROC curves for calculating the signature genes’
diagnostic performance. (E–H) The main signaling pathways associated with specific genes identified using GSEA. (I) Correlation of signature genes with
pathways using GSVA analysis.
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NRFGs risk score predicts treatment
response assessment
Analysis of the violin plots designed to demonstrate the link
between IPSs and risk groups, showed that high IPSs indicate stronger
responses to PD-1 and CTLA-4 blockers (Figures 10A–D). Using the
“pRRophetic”R package, we explored the potential sensitivity of
clinical agents in the high-risk and low-risk groups. Agents available
for the treatment of gliomas, such as nilotinib (Figure 10E), had a
higher IC50 in patients of the high-risk group, whereas ABT737 and
KU-55933 had a higher IC50 in patients of low-risk groups
(Figures 10F,G). To understand the association between risk scores
of NFRGs and the benefits of immunotherapy, we investigated a
cohort of lung cancer patients treated with PD-1 checkpoint
inhibitors (GSE135222) using the BEST database. The ROC curve
analysis demonstrated that NFRGs was effective in predicting
immunotherapy responsiveness, with low NFRGs expression score
correlating with higher degree of immune response to anti-PD-
L1 (Figure 10H).
7 NFRGs in single-cell RNA sequencing
Using the single-cell dataset GSE141982 from the TISCH
database, we investigated the expression of 7 NFRGs in the GBM
TME. It was observed that the GSE141982 dataset was enriched with
several cell types of 16 cell populations and 4 cell subpopulations
(Supplementary Figure 5A). Most endothelial cells, monocyte
macrophages, and CD8+ T cells expressed HSPB1 and TUBB2A.
The expression of the other NFRGs, which are primarily found in
tumor cell cells, was low (Supplementary Figures 5B,C).
QT-PCR and immunohistochemistry
By analyzing the neurotrophic factor-related genes in PD and
GBM, we found that EN1 and LOXL1 we important players in both
diseases. In the human protein atlas, the protein expression of EN1
(Figures 11A,B) and LOXL1 (Figures 11C,D) were higher in GBM
relative to normal cortical tissue. RT-qPCR results confirmed the
higher expression of EN1 and LOXL1 in both GBM cell lines
(Figures 11E,F).
Discussion
In clinical practice, the diagnosis of PD is mainly based on
neurological examination when patients with PD present with
motor symptoms. Currently, the etiology of PD is still not fully
understood. For this reason, there is no cure or intervention to delay
the progression of the disease, and PD is only treated symptomatically
through medication and rehabilitation (47). Moreover, most patients
have advanced neurological symptoms at the time of diagnosis (48).
Similar to PD, patients with GBM are diagnosed through clinical
AB
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C
FIGURE 6
Expression validation of characteristic NFRGs. (A) Characterization of NRGs expression in the internal validation cohort (GSE7621). (B) Characterization of
NRGs expression in the external validation cohort (GSE20163). (C–F) Expression of characteristic NRGs in different stages of PD (GSE49036).
Zhao et al. 10.3389/fimmu.2023.1090040
Frontiers in Immunology frontiersin.org10
examination and neuroimaging methods. Therefore, it is imperative
to study the underlying pathogenesis of PD and GBM and identified
biomarkers for early identification to promote timely treatment of
neurological symptoms before they appear.
Although the pathways affected in PD and GBM are highly
similar, it has been reported that those that regulate cell
proliferation and metabolism play opposite roles in the two
diseases. For example, p53 inhibits GBM cell proliferation by
blocking cell cycle progression and promoting apoptosis; however,
in PD, increased p53 expression upregulates the expression of a-
synuclein and its subsequent aggregation in which promotes disease
progression (49,50). The PTEN/PI3K/Akt signaling pathway is
down-regulated in PD and up-regulated in GBM (51,52). An
increase in PTEN in PD leads causes inhibition of pro-survival
signaling pathways resulting in neuronal cell death. In mouse
models, was found that depletion of PTEN attenuated the loss of
dopaminergic cells and reduced the symptoms of PD (53).
Overexpression of EGFR activated the PTEN/PI3K/Akt signaling
pathway in GBM, and mutations in PTEN and phosphorylated Akt
have been linked to poor prognosis of GBM patients (52,54).
A
B
DEF
GI
H
J
C
FIGURE 7
Development and validation of prognostic models for GBM patients. (A) The 10-fold cross-validation LASSO analysis found seven prognostic genes. Each
curve represents one gene. (B) Plots illustrating the coefficient profiles for seven prognostic NRGs. The best lambda is where vertical dashed lines are
drawn. (C, D) Survival curves showing the risk stratification ability of TCGA and CGGA cohorts. (E, F) AUC values for TCGA and CGGA cohort risk
groupings at 1, 3, and 5 years. (G, H) AUC values for 3-year clinical characteristics and risk groups for the TCGA and CGGA cohorts. (I, J) The risk plots of
survival status of each sample in the TCGA and CGGA cohorts. Heat map showing the expression of each gene’.
Zhao et al. 10.3389/fimmu.2023.1090040
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Since their discovery, neurotrophic factors have been found to
play important roles in many processes such as survival, growth, and
differentiation of nerve cells in the peripheral and central nervous
systems. It has been found that neurotrophic factors can improve the
survival and function of nigrostriatal dopaminergic neurons. They
can also promote the survival and synaptic plasticity of mature
neurons and protect neurons from damage (55). Neurotrophic
factors have also been widely reported in gliomas. For example,
GDNF which is released by glioma cells can promote tumor
growth, an action that is dependent on the presence of microglia
(56). The development of immunotherapy has triggered an increasing
number of investigations into the clinical efficacy of targeting
immune checkpoints, including early diagnosis, combination
therapy, and treatment prediction in patients with various types of
tumors. Individual neurotrophic factor family members are now
considered to be biomarkers for predicting cancer development and
prognosis (57). Overactivation of the immune system concurrently
can induce or stimulate the onset of neurodegeneration and cancer, as
well as local or systemic inflammatory reactions (58). In GBM,
specific cytokines generated by tumor cells suppress the effects of
immune response and allow tumor cells to evade the immune system.
Elevated cytokine levels induced by cellular stress in PD can result in
neuronal cell death (59). In addition to this, BDNF is thought to
produce anti-tumor immune responses during the development and
differentiation of neurons (60). Currently, few studies have explored
the neurotrophic factors in both PD and GBM and identify factors
driving the pathogenesis of PD, as well as the associated
immune mechanisms.
In this investigation, we first used the analysis of variance and
univariate cox to characterize 12 NFRGs influencing the prognosis of
GBM and PD development. Subsequently, we applied two machine
learning algorithms to analyze the 12 NFRGs and selected four
distinctive NFRGs from two external validation cohorts which were
thought to potentially affect the development of PD. The lasso
regression analysis and multivariate cox analysis were performed on
the 12 NFRGs in the TCGA-GBM cohort, resulting in the creation of
a 7-NFRGs model. A validation investigation was conducted on the
developed NFRGs risk score model and to determine its capacity to
accurately predict the prognosis of GBM patients. Based on
expression levels of the screened 7-NFRGs, a risk score was
generated for each patient, and the patients were classified into high
and low-risk groups based on the median risk score. Columnar plots
containing clinicopathological variables were created. Calibration
curves showed a good correlation between predicted and observed
values. In addition, conventional clinical features including age,
gender, and IDH mutation status were used to predict the
AB
DE F
G IHJ
C
FIGURE 8
Prognostic value of risk scores and clinical characteristics in GBM patients. (A) Univariate and (B) multivariate COX analysis for evaluating the prognostic
signature and clinical features (including age, race, gender, and IDH state). (C) Nomogram of risk groupings and clinical characteristics for predicting
survival at 1, 1.5, and 2 years. (D) Calibration curves for testing the agreement between actual and predicted outcomes at 1, 1.5, and 2 years. (E) DCA
curves of risk scores and clinical characteristics for the TCGA cohort at 3 years. (F) The concordance index (C-index) for the TCGA cohort. (G) Bar charts
of clinical characteristics associated with risk grouping determined by chi-square test. (H–J) Variations in risk scores among the TCGA cohort’s various
clinical characteristic groupings *p < 0.05, **p < 0.01, ***p < 0.001.
Zhao et al. 10.3389/fimmu.2023.1090040
Frontiers in Immunology frontiersin.org12
prognosis of GBM. In conclusion, the constructed model had the
largest net return, showing that the developed NFRGs risk model is
clinically important in decision-making and implementation of
individualized anti-tumor treatment.
In this work, we found that seven NFRGs, EN1, LOXL1, TUBB2A,
HSPB1, RGS4, L1CAM, and GPR143t, together constitute a stable risk
score for GBM. In PD, three NFRGs, EN1, LOXL1, and PLOD3, were
identified to influencethediseasecourseofPDpatients.EN1andLOXL1
have the potential to be targets for immunotherapy in GBM and PD
patients. The EN1 gene encodes homeobox protein engrailed-1 and its
mutations were first discovered to cause abnormal growth and
development in Drosophila (61). In humans, EN1 expression affects
multiple neuronal cell types and can profoundly regulate central nervous
system development (62). Hypermethylation of EN1 has been reported
in many cancers, including colorectal cancer, prostate cancer, and
glioma, and the degree of methylation correlates with tumor grade and
patient prognosis (63–65). In a recent study, Chang et al. found that EN1
can regulate the Hedgehog signaling by modulating Gli1 expression and
levels of primary cilia transport-associated protein TULP3. Therefore, be
used as a diagnostic and prognostic marker for glioblastoma (66). In
addition, it was reported that EN1 participates in the regulation of
maturation and survival of midbrain dopaminergic neurons, and
polymorphisms in the EN1 gene may be a potential genetic risk factor
for sporadic PD (67). In mice models, EN1 and EN2 were found to not
influence the survival of dopaminergic neurons during development but
also regulators of neuroprotective physiological functions of neurons
(68). The LOX family proteins are copper-dependent monoamine
oxidases that are mainly involved in the polymerization of collagen
and elastin in the extracellular matrix (ECM), hence increase the stability
of ECM (69). The expression of LOX family genes is influenced by the
IDH1 status of gliomas (70). LOXL1 increases aggressiveness of gliomas
by affecting the anti-apoptotic ability of Wnt/b-linked protein signaling
(71). Another study found that LOXL1 stabilizes the co-protein BAG2 by
blocking K186 ubiquitination, which enables glioma cells to resist
A
B
D
E
C
FIGURE 9
Prediction of the tumor microenvironment and immune cell infiltration by the 7-NFRGs risk score. (A) Differences in immune cell infiltration levels
between high and low-risk groups. (B) Differences in immune function between high and low-risk groups. (C) Immune cell bubble map. (D) Differences
in immune checkpoint between high- and low-risk groups. (E) GSEA analysis focusing on the differential enrichment of KEGG pathways. *P < 0.05, **P <
0.01, ***P < 0.001, ns ≥0.05.
Zhao et al. 10.3389/fimmu.2023.1090040
Frontiers in Immunology frontiersin.org13
apoptosis under non-adherent conditions (72). In contrast, few studies
have reported the role of LOXL1 in degenerative neurological diseases.
One hypothesis is that LOXL1 protein for aggregates and is actively
cleared by autophagy in cells from patients with shedding syndrome
(XFG), a cellular defect also found in neurodegenerative diseases such as
AD and PD (73).
For GBMs, the mainstay postoperative treatment is the Stupp
regimen, i.e. temozolomide concurrent radiotherapy + temozolomide
adjuvant chemotherapy. However, the extremely heterogeneous and
aggressive nature of GBM results in low survival rate and high
recurrence in a large number of patients (74). Similarly, levodopa
preparations are the most effective and commonly used drugs for PD,
although the disease is incurable and there is no effective drug to delay
the progression of the disease. Immunotherapy has emerged as a
potential treatment for various diseases, especially for neurological
diseases (75). Extensive characterization of the tumor
microenvironment (TME) is essential to the identification of
reliable prognostic markers and immunotherapy targets in GBM.
The high heterogeneity of GBM and the inherent immune evasion
mechanism of tumors lead to poor outcomes of GBM patients
receiving immunotherapy. In addition, GBM patients have poor
prognosis due to the low PD-L1 expression, low tumor mutational
load, and depletion of tumor-infiltrating T cells (76,77).
In the TCGA cohort, several forms of immune infiltration prediction
and immunotherapy prediction models were developed. We found that
patients in the low-risk group had better prognosis and immunotherapy
outcomes. The developed NFRGs risk score model was found to
accurately predict the prognosisofpatientswithGBM,andcolumn
line graphs based on this model can help doctors in developing
customized targeted treatments. Currently, despite many clinical trials
on immunotherapy, there are the efficacy of immunotherapy for GBM is
not well understood. Even though it appears to be the most effective
method of treating Parkinson’s syndrome, immunotherapy for PD is yet
to be clinically applied due to limited evidence. In future, experimental
and clinical cohort studies should explore the associated molecular
pathways based on the present findings. Such studies will have
significant therapeutic value and promote the application of precision
medicine in GBM and PD patients.
AB D
EF G
H
C
FIGURE 10
Prediction of pharmaceutical and immunotherapy for various risk groupings. (A–D) The comparison of the relative distribution of immunophenoscore
(IPS) between high and low-risk groups. (E–G) IC50 values for patients in the high- and low-risk groups based on Nilotinib, ABT737, and KU-55933 to
assess the sensitivity of chemotherapeutic agents. (H) Evaluation of anti-PD-L1 therapy in the GSE135222 cohort by NFRGs.
Zhao et al. 10.3389/fimmu.2023.1090040
Frontiers in Immunology frontiersin.org14
A
B
D
E F
C
FIGURE 11
Immunohistochemistry and QT-PCR. (A, B) Protein expression levels of EN1 in normal cerebral cortex and GBM. (C, D) Protein expression levels of
LOXL1 in normal cerebral cortex and GBM. (E) RT q-PCR analysis of EN1 expression in various types of glioma cells. (F) RT q-PCR analysis of LOXL1
expression in various types of glioma cells. *p < 0.05, **p < 0.01, ***p < 0.001, ns, no significance.
Zhao et al. 10.3389/fimmu.2023.1090040
Frontiers in Immunology frontiersin.org15
Data availability statement
The original contributions presented in the study are included in
the article/Supplementary Material. Further inquiries can be directed
to the corresponding authors.
Author contributions
SZ and CW conceived the study. SZ, CW, HC, QY, SC, KX, KS, GP,
HL and ZX drafted the manuscript. HC, GL, KX and HL performed the
literature search and collected the data. SZ, CW, HC, CC and KS
analyzed and visualized the data. SZ and CW designed and completed
in vitro experiments. HC, GL, PL and ZX helped with the final revision
of this manuscript. All authors contributed to the article and approved
the submitted version.
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.
The handling editor ZH declared a shared parent affiliation with
the author QY, SC and GL at the time of review.
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.
Supplementary material
The Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/fimmu.2023.1090040/
full#supplementary-material
References
1. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee
WK, et al. The 2016 world health organization classification of tumors of the central
nervous system: a summary. Acta Neuropathol (2016) 131(6):803–20. doi: 10.1007/
s00401-016-1545-1
2. Bangalore Yogananda CG, Shah BR, Vejdani-Jahromi M, Nalawade SS, Murugesan
GK, Yu FF, et al. A novel fully automated MRI-based deep-learning method for
classification of IDH mutation status in brain gliomas. Neuro Oncol (2020) 22(3):402–
11. doi: 10.1093/neuonc/noz199
3. Ostrom QT, CioffiG, Gittleman H, Patil N, Waite K, Kruchko C, et al. CBTRUS
statistical report: Primary brain and other central nervous system tumors diagnosed in the
united states in 2012-2016. Neuro Oncol (2019) 21(Suppl 5):v1–v100. doi: 10.1093/
neuonc/noz150
4. Gilbert MR, Dignam JJ, Armstrong TS, Wefel JS, Blumenthal DT, Vogelbaum MA,
et al. A randomized trial of bevacizumab for newly diagnosed glioblastoma. N Engl J Med
(2014) 370(8):699–708. doi: 10.1056/NEJMoa1308573
5. Sandmann T, Bourgon R, Garcia J, Li C, Cloughesy T, Chinot OL, et al. Patients with
proneural glioblastoma may derive overall survival benefit from the addition of
bevacizumab to first-line radiotherapy and temozolomide: Retrospective analysis of the
AVAglio trial. J Clin Oncol (2015) 33(25):2735–44. doi: 10.1200/JCO.2015.61.5005
6. Lai G, Li K, Deng J, Liu H, Xie B, Zhong X. Identification and validation of a gene
signature for lower-grade gliomas based on pyroptosis-related genes to predict survival
and response to immune checkpoint inhibitors. J Healthc Eng 2022 (2022) 2022:8704127.
doi: 10.1155/2022/8704127
7. Parkinson J. An essay on the shaking palsy. 1817. J Neuropsychiatry Clin Neurosci
(2002) 14(2):223–36. doi: 10.1176/jnp.14.2.223
8. Hickman S, Izzy S, Sen P, Morsett L, El Khoury J. Microglia in neurodegeneration.
Nat Neurosci (2018) 21(10):1359–69. doi: 10.1038/s41593-018-0242-x
9. Cao JJ, Li KS, Shen YQ. Activated immune cells in parkinson's disease. J
Neuroimmune Pharmacol (2011) 6(3):323–9. doi: 10.1007/s11481-011-9280-9
10. Kalia LV, Lang AE. Parkinson's disease. Lancet (9996) 2015:896–912:386.
doi: 10.1016/S0140-6736(14)61393-3
11. Fung TC, Olson CA, Hsiao EY. Interactions between the microbiota, immune and
nervous systems in health and disease. Nat Neurosci (2017) 20(2):145–55. doi:10.1038/nn.4476
12. Filippou PS, Outeiro TF. Cancer and parkinson's disease: Common targets,
emerging hopes. Mov Disord (2021) 36(2):340–6. doi: 10.1002/mds.28425
13. Plun-Favreau H, Lewis PA, Hardy J, Martins LM, Wood NW. Cancer and
neurodegeneration: between the devil and the deep blue sea. PloS Genet (2010) 6(12):
e1001257. doi: 10.1371/journal.pgen.1001257
14. Lalonde FM, Myslobodsky M. Are dopamine antagonists a risk factor for breast
cancer? an answer from parkinson's disease. Breast (2003) 12(4):280–2. doi: 10.1016/
S0960-9776(03)00061-4
15. Diamandis P, Sacher AG, Tyers M, Dirks PB. New drugs for brain tumors? insights
from chemical probing of neural stem cells. Med Hypotheses (2009) 72(6):683–7. doi:
10.1016/j.mehy.2008.10.034
16. Ibanez K, Boullosa C, Tabares-Seisdedos R, Baudot A, Valencia A. Molecular
evidence for the inverse comorbidity between central nervous system disorders and
cancers detected by transcriptomic meta-analyses. PloS Genet (2014) 10(2):e1004173.
doi: 10.1371/journal.pgen.1004173
17. Tanji K, Imaizumi T, Yoshida H, Mori F, Yoshimoto M, Satoh K, et al. Expression
of alpha-synuclein in a human glioma cell line and its up-regulation by interleukin-1beta.
Neuroreport (2001) 12(9):1909–12. doi: 10.1097/00001756-200107030-00028
18. Khosla R, Banik A, Kaushal S, Battu P, Gupta D, Anand A. Is brain-derived
neurotrophic factor: A common link between neurodegenerative disorders and cancer?
Curr Alzheimer Res (2019) 16(4):344–52. doi: 10.2174/1567205016666190408123947
19. Trujillo CA, Schwindt TT, Martins AH, Alves JM, Mello LE, Ulrich H. Novel
perspectives of neural stem cell differentiation: from neurotransmitters to therapeutics.
Cytometry A (2009) 75(1):38–53. doi: 10.1002/cyto.a.20666
20. Nagahara AH, Tuszynski MH. Potential therapeutic uses of BDNF in neurological
and psychiatric disorders. Nat Rev Drug Discovery (2011) 10(3):209–19. doi: 10.1038/
nrd3366
21. Blurton-Jones M, Kitazawa M, Martinez-Coria H, Castello NA, Muller FJ, Loring
JF, et al. Neural stem cells improve cognition via BDNF in a transgenic model of
Alzheimer disease. Proc Natl Acad Sci U.S.A. (2009) 106(32):13594–9. doi: 10.1073/
pnas.0901402106
22. Caccamo A, Maldonado MA, Bokov AF, Majumder S, Oddo S. CBP gene transfer
increases BDNF levels and ameliorates learning and memory deficits in a mouse model of
alzheimer's disease. Proc Natl Acad Sci U.S.A. (2010) 107(52):22687–92. doi: 10.1073/
pnas.1012851108
23. Banik A, Prabhakar S, Kalra J, Anand A. Effect of human umbilical cord blood
derived lineage negative stem cells transplanted in amyloid-beta induced cognitive
impaired mice. Behav Brain Res (2015) 291:46–59. doi: 10.1016/j.bbr.2015.05.014
24. Gao F, Griffin N, Faulkner S, Rowe CW, Williams L, Roselli S, et al. The
neurotrophic tyrosine kinase receptor TrkA and its ligand NGF are increased in
squamous cell carcinomas of the lung. Sci Rep (2018) 8(1):8135. doi: 10.1038/s41598-
018-26408-2
25. Radin DP, Patel P. BDNF: An oncogene or tumor suppressor? Anticancer Res
(2017) 37(8):3983–90. doi: 10.21873/anticanres.11783
26. Chopin V, Lagadec C, Toillon RA, Le Bourhis X. Neurotrophin signaling in cancer
stem cells. Cell Mol Life Sci (2016) 73(9):1859–70. doi: 10.1007/s00018-016-2156-7
27. Jobling P, Pundavela J, Oliveira SM, Roselli S, Walker MM, Hondermarck H.
Nerve-cancer cell cross-talk: A novel promoter of tumor progression. Cancer Res (2015)
75(9):1777–81. doi: 10.1158/0008-5472.CAN-14-3180
Zhao et al. 10.3389/fimmu.2023.1090040
Frontiers in Immunology frontiersin.org16
28. Franzese O, Di Francesco AM, Meco D, Graziani G, Cusano G, Levati L, et al.
hTERT transduction extends the lifespan of primary pediatric low-grade glioma cells
while preserving the biological response to NGF. Pathol Oncol Res (2021) 27:612375. doi:
10.3389/pore.2021.612375
29.BeckKD,ValverdeJ,AlexiT,PoulsenK,MoffatB,VandlenRA,etal.
Mesencephalic dopaminergic neurons protected by GDNF from axotomy-induced
degeneration in the adult brain. Nature (1995) 373(6512):339–41. doi: 10.1038/373339a0
30. Wiesenhofer B, Weis C, Humpel C. Glial cell line-derived neurotrophic factor (GDNF)
is a proliferation factor for rat C6 glioma cells: evidence from antisense experiments. Antisense
Nucleic Acid Drug Dev (2000) 10(5):311–21. doi: 10.1089/oli.1.2000.10.311
31. Shen Y, Chi H, Xu K, Li Y, Yin X, Chen S, et al. A novel classification model for
lower-grade glioma patients based on pyroptosis-related genes. Brain Sci (2022) 12
(6):20220528. doi: 10.3390/brainsci12060700
32. Lai G, Liu H, Deng J, Li K, Xie B. A novel 3-gene signature for identifying COVID-
19 patients based on bioinformatics and machine learning. Genes (Basel) (2022) 13
(9):20220908. doi: 10.3390/genes13091602
33. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for
removing batch effects and other unwanted variation in high-throughput experiments.
Bioinformatics (2012) 28(6):882–3. doi: 10.1093/bioinformatics/bts034
34. Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D. GeneCards: integrating
information about genes, proteins and diseases. Trends Genet (1997) 13(4):163.
doi: 10.1016/s0168-9525(97)01103-7
35. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers
differential expression analyses for RNA-sequencing and microarray studies. Nucleic
Acids Res (2015) 43(7):e47. doi: 10.1093/nar/gkv007
36. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al.
Exploration, normalization, and summaries of high density oligonucleotide array probe
level data. Biostatistics (2003) 4(2):249–64. doi: 10.1093/biostatistics/4.2.249
37. Engebretsen S, Bohlin J. Statistical predictions with glmnet. Clin Epigenet (2019) 11
(1):123. doi: 10.1186/s13148-019-0730-1
38. Yuan Q, Zhang W, Shang W. Identification and validation of a prognostic risk-scoring
model based on sphingolipid metabolism-associated cluster in colon adenocarcinoma. Front
Endocrinol (Lausanne) (2022) 13:1045167. doi: 10.3389/fendo.2022.1045167
39. Hoang SA, Oseini A, Feaver RE, Cole BK, Asgharpour A, Vincent R, et al. Gene
expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic
fatty liver disease. Sci Rep (2019) 9(1):12541. doi: 10.1038/s41598-019-48746-5
40. Chen X, Yuan Q, Liu J, Xia S, Shi X, Su Y, et al. Comprehensive characterization of
extracellular matrix-related genes in PAAD identified a novel prognostic panel related to
clinical outcomes and immune microenvironment: A silico analysis with in vivo and vitro
validation. Front Immunol (2022) 13:985911. doi: 10.3389/fimmu.2022.985911
41.RenJ,YuanQ,LiuJ,ZhongL,LiH,WuG,etal.Identifyingtheroleof
transient receptor potential channels (TRPs) in kidney renal clear cell carcinoma and
their potential therapeutic significances using genomic and transcriptome analyses.
BMC Med Genomics (2022) 15(1):156. doi: 10.1186/s12920-022-01312-x
42. Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curve analysis,
a novel method for evaluating diagnostic tests, prediction models and molecular markers.
BMC Med Inform Decis Mak (2008) 8:53. doi: 10.1186/1472-6947-8-53
43. Miao Y, Yuan Q, Wang C, Feng X, Ren J, Wang C. Comprehensive
characterization of RNA-binding proteins in colon adenocarcinoma identifies a novel
prognostic signature for predicting clinical outcomes and immunotherapy responses
based on machine learning. Comb Chem High Throughput Screen (2022) 20220404.
doi: 10.2174/1386207325666220404125228
44. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust
enumeration of cell subsets from tissue expression profiles. Nat Methods (2015) 12
(5):453–7. doi: 10.1038/nmeth.3337
45. LiuCJ, Hu FF, Xia MX,Han L, Zhang Q, GuoAY. GSCALite: a web server for geneset
cancer analysis. Bioinformatics (2018) 34(21):3771–2. doi: 10.1093/ bioinformatics/bty411
46. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al.
Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships
and predictors of response to checkpoint blockade. Cell Rep (2017) 18(1):248–62. doi:
10.1016/j.celrep.2016.12.019
47. Antony PM, Diederich NJ, Kruger R, Balling R. The hallmarks of parkinson's
disease. FEBS J (2013) 280(23):5981–93. doi: 10.1111/febs.12335
48. Davis ME. Glioblastoma: Overview of disease and treatment. Clin J Oncol Nurs
(2016) 20(5 Suppl):S2–8. doi: 10.1188/16.CJON.S1.2-8
49. Vousden KH, Prives C. Blinded by the light: The growing complexity of p53. Cell
(2009) 137(3):413–31. doi: 10.1016/j.cell.2009.04.037
50. Duplan E, Giordano C, Checler F, Alves da Costa C. Direct alpha-synuclein
promoter transactivation by the tumor suppressor p53. Mol Neurodegener (2016) 11:13.
doi: 10.1186/s13024-016-0079-2
51. Cheng CK, Fan QW, Weiss WA. PI3K signaling in glioma–animal models and
therapeutic challenges. Brain Pathol (2009) 19(1):112–20. doi: 10.1111/j.1750-
3639.2008.00233.x
52. Li X, Wu C, Chen N, Gu H, Yen A, Cao L, et al. PI3K/Akt/mTOR signaling
pathway and targeted therapy for glioblastoma. Oncotarget (2016) 7(22):33440–50. doi:
10.18632/oncotarget.7961
53. Domanskyi A, Geissler C, Vinnikov IA, Alter H, Schober A, Vogt MA, et al. Pten
ablation in adult dopaminergic neurons is neuroprotective in parkinson's disease models.
FASEB J (2011) 25(9):2898–910. doi: 10.1096/fj.11-181958
54. Majewska E, Szeliga M. AKT/GSK3beta signaling in glioblastoma. Neurochem Res
(2017) 42(3):918–24. doi: 10.1007/s11064-016-2044-4
55. Rocco ML, Soligo M, Manni L, Aloe L. Nerve growth factor: Early studies and
recent clinical trials. Curr Neuropharmacol (2018) 16(10):1455–65. doi: 10.2174/
1570159X16666180412092859
56. Huang Y, Zhang B, Haneke H, Haage V, Lubas M, Yuan Y, et al. Glial cell line-
derived neurotrophic factor increases matrix metallopeptidase 9 and 14 expression in
microglia and promotes microglia-mediated glioma progression. J Neurosci Res (2021) 99
(4):1048–63. doi: 10.1002/jnr.24768
57. Dong Z, Dai L, Zhang Y, Fang C, Shi G, Chen Y, et al. Hypomethylation of GDNF
family receptor alpha 1 promotes epithelial-mesenchymal transition and predicts
metastasis of colorectal cancer. PloS Genet (2020) 16(11):e1009159. doi: 10.1371/
journal.pgen.1009159
58. Labzin LI, Heneka MT, Latz E. Innate immunity and neurodegeneration. Annu Rev
Med (2018) 69:437–49. doi: 10.1146/annurev-med-050715-104343
59. Mencke P, Hanss Z, Boussaad I, Sugier PE, Elbaz A, Kruger R. Bidirectional
relation between parkinson's disease and glioblastoma multiforme. Front Neurol (2020)
11:898. doi: 10.3389/fneur.2020.00898
60. Mehler MF, Kessler JA. Growth factor regulation of neuronal development. Dev
Neurosci (1994) 16(3-4):180–95. doi: 10.1159/000112105
61. Garcia-Bellido A, Santamaria P. Developmental analysis of the wing disc in the
mutant engrailed of drosophila melanogaster. Genetics (1972) 72(1):87–104. doi: 10.1093/
genetics/72.1.87
62. Zec N, Rowitch DH, Bitgood MJ, Kinney HC. Expression of the homeobox-
containing genes EN1 and EN2 in human fetal midgestational medulla and cerebellum. J
Neuropathol Exp Neurol (1997) 56(3):236–42. doi: 10.1097/00005072-199703000-00002
63. Devaney J, Stirzaker C, Qu W, Song JZ, Statham AL, Patterson KI, et al. Epigenetic
deregulation across chromosome 2q14.2 differentiates normal from prostate cancer and
provides a regional panel of novel DNA methylation cancer biomarkers. Cancer Epidemiol
Biomarkers Prev (2011) 20(1):148–59. doi: 10.1158/1055-9965.EPI-10-0719
64. Carrascosa LG, Sina AA, Palanisamy R, Sepulveda B, Otte MA, Rauf S, et al.
Molecular inversion probe-based SPR biosensing for specific, label-free and real-time
detection of regional DNA methylation. Chem Commun (Camb) (2014) 50(27):3585–8.
doi: 10.1039/C3CC49607D
65. Qin G, Hu B, Li X, Li R, Meng Y, Wang Y, et al. Identification of key differentially
expressed transcription factors in glioblastoma. J Oncol 2020 (2020) 2020:9235101. doi:
10.1155/2020/9235101
66. Chang J, Guo C, Li J, Liang Z, Wang Y, Yu A, et al. EN1 regulates cell growth and
proliferation in human glioma cells via hedgehog signaling. Int J Mol Sci (2022) 23
(3):20220120. doi: 10.3390/ijms23031123
67. Haubenberger D, Reinthaler E, Mueller JC, Pirker W, Katzenschlager R, Froehlich
R, et al. Association of transcription factor polymorphisms PITX3 and EN1 with
parkinson's disease. Neurobiol Aging (2011) 32(2):302–7. doi: 10.1016/
j.neurobiolaging.2009.02.015
68. Rekaik H, Blaudin de The FX, Prochiantz A, Fuchs J, Joshi RL. Dissecting the role
of engrailed in adult dopaminergic neurons–insights into Parkinson disease pathogenesis.
FEBS Lett (2015) 589(24 Pt A):3786–94. doi: 10.1016/j.febslet.2015.10.002
69. Kagan HM, Li W. Lysyl oxidase: properties, specificity, and biological roles inside
and outside of the cell. J Cell Biochem (2003) 88(4):660–72. doi: 10.1002/jcb.10413
70. da Silva R, Uno M, Marie SK, Oba-Shinjo SM. LOX expression and functional
analysis in astrocytomas and impact of IDH1 mutation. PloS One (2015) 10(3):e0119781.
doi: 10.1371/journal.pone.0119781
71. Li H, He X, Peng Y, Shao B, Duan H, Yang F, et al. LOXL1 regulates cell apoptosis
and migration in human neuroglioma U87 and U251cells via wnt/beta-catenin signaling. Int
J Clin Exp Pathol (2018) 11(4):2032–7.
72. Yu H, Ding J, Zhu H, Jing Y, Zhou H, Tian H, et al. LOXL1 confers antiapoptosis
and promotes gliomagenesis through stabilizing BAG2. Cell Death Differ (2020) 27
(11):3021–36. doi: 10.1038/s41418-020-0558-4
73. Bernstein AM, Ritch R, Wolosin JM. LOXL1 folding in exfoliation glaucoma. Adv
Protein Chem Struct Biol (2019) 118:273–88. doi: 10.1016/bs.apcsb.2019.09.005
74. Lin AL, Sum MW, DeAngelis LM. Is there a role for early chemotherapy in the
management of pituitary adenomas? Neuro Oncol (2016) 18(10):1350–6. doi: 10.1093/
neuonc/now059
75. Schwab AD, Thurston MJ, Machhi J, Olson KE, Namminga KL, Gendelman HE,
et al. Immunotherapy for parkinson's disease. Neurobiol Dis (2020) 137:104760. doi:
10.1016/j.nbd.2020.104760
76. Khasraw M, Reardon DA, Weller M, Sampson JH. PD-1 inhibitors: Do they have a
future in the treatment of glioblastoma? Clin Cancer Res (2020) 26(20):5287–96. doi:
10.1158/1078-0432.CCR-20-1135
77. Silver DJ, Sinyuk M, Vogelbaum MA, Ahluwalia MS, Lathia JD. The intersection of
cancer, cancer stem cells, and the immune system: therapeutic opportunities. Neuro Oncol
(2016) 18(2):153–9. doi: 10.1093/neuonc/nov157
Zhao et al. 10.3389/fimmu.2023.1090040
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