Abstract

   The COVID-19 pandemic caused by SARS-CoV-2 infection has placed health
   systems under excessive pressure and especially elderly people with
   cancer. Glioblastoma multiforme (GBM) is a malignant brain tumor with
   an increasing incidence in elderly individuals, and thereby GBM
   patients are a vulnerable population during the COVID-19 outbreak.
   Accumulating studies have implied that SARS-CoV-2 might invade the
   brain directly via coronavirus receptors. However, little is known
   about SARS-CoV-2 infection in the clinical development of GBM. Here, we
   explored the oncogenic roles of six coronavirus receptors (ACE2, DPP4,
   ANPEP, AXL, TMPRSS2, and ENPEP) in GBM using bioinformatics and
   experimental approaches. We found that ANPEP and ENPEP were
   significantly increased at both the mRNA and protein levels in GBM
   compared with normal brain tissue. Kaplan–Meier survival curves and Cox
   regression analysis demonstrated that high expressions of ANPEP and
   ENPEP are associated with poor prognosis and survival. Moreover, all
   receptors are positively correlated with the immune infiltration levels
   of monocyte. Furthermore, we identified 245 genes between COVID-19 and
   coronavirus receptors–correlated genes in GBM and performed a thorough
   analysis of their protein–protein interaction network, functional
   signaling pathway and molecular process. Our work explores for the
   first time the association of coronavirus receptors with GBM and
   suggests ANPEP and ENPEP as potential therapeutic targets of GBM
   irrespective of COVID-19.

   Keywords: SARS-CoV-2, GBM, ACE2, ANPEP, ENPEP

Introduction

   COVID-19 caused by human severe acute respiratory syndrome coronavirus
   2 (SARS-CoV-2) is the most serious pneumonia today and thereby is
   threatening global public health and the economy ([39]1, [40]2). As of
   11 November 2021, 251,584,730 COVID-19 cases and 5,075,809 deaths have
   been identified in 188 countries and regions
   ([41]https://coronavirus.jhu.edu/map.html).

   COVID-19 with the primary symptoms such as fever, dry cough, diarrhea,
   and headache, has substantial morbidity and mortality worldwide
   ([42]3). Although the lung is the major organ of infection, current
   studies indicate that SARS-CoV-2 might invade the central nervous
   system (CNS) region directly, resulting in neurological symptoms, such
   as dizziness, the loss or disruption of smell, taste, muscular
   coordination, autonomic respiratory control, lethargy, depression and
   anxiety ([43]4–[44]9). A case series of 214 hospitalized COVID-19
   patients showed that 78 patients (38.4%) and more common (45.5%) in
   patients with severe infection had neurologic manifestations, namely,
   acute cerebrovascular events and impaired consciousness ([45]5). The
   autopsy results of COVID-19 patients exhibited hyperemic and edematous
   brain tissue and the detection of SARS-CoV-2 RNA in cerebrospinal fluid
   specimens ([46]10). Furthermore, a population-level observation study
   reported that elderly and cancer patients had increased susceptibility
   to virus infection ([47]11). A national analysis of COVID-19
   demonstrated that 20% of COVID-19 deaths had active cancer ([48]12). A
   meta-analysis of 15 studies with 3,019 COVID-19-infected cancer
   patients showed that the overall fatality rate was 22.4% ([49]13).
   Glioblastoma multiforme (GBM) is identified as a fast-growing and
   aggressive brain tumor with an increased incidence in the elderly
   population ([50]14). Therefore, GBM can be considered as the most
   vulnerable disease during the COVID-19 pandemic ([51]15). In
   particular, one cohort of 41 diffuse glioma patients infected by
   SARS-CoV-2 in France showed that 16 patients (39%) died after a median
   delay of 13 days, which is higher than the general and noncancer
   population, although the researcher declared that the mortality rate
   was overestimated and should be taken with caution due to multiple
   limitations ([52]16).

   Traditionally, effective viral entry is the first line of SARS-CoV-2
   infection and determines the range of infected organs. It has been well
   established that the entry of coronaviruses into target tissues
   requires: 1) the binding of the spike (S) protein of coronaviruses to
   cellular receptors, which facilitates virus attachment to the cell
   surface; and 2) the priming of S protein by cellular proteases, which
   undertakes S protein cleavage to fuse cell membranes ([53]17–[54]19).
   Accumulating studies have provided bodies of evidence that the
   following coronavirus receptors play important roles in coronavirus
   cell entry: angiotensin-converting enzyme 2 (ACE2) ([55]20–[56]24), a
   type I transmembrane protein, is characterized as a key determinant
   cellular receptor for SARS-CoV-2 ([57]20–[58]25); TMPRSS2 is identified
   as a type II transmembrane serine protease and employed for S protein
   priming of SARS-CoV-2 ([59]26–[60]29); dipeptidyl-peptidase 4 (DPP4),
   also known as CD26, is a transmembrane glycoprotein and functions as a
   receptor for the Middle East Respiratory Syndrome coronavirus
   (MERS-CoV), which is phylogenetically correlated with SARS-CoV-2
   ([61]30, [62]31). Recent studies have demonstrated that the S1 domain
   of COVID-19 S protein potentially interacts with DPP4 when the virus
   enter cells of the respiratory tract ([63]32–[64]36); Alanyl
   aminopeptidase (ANPEP), also named as CD13, is a receptor for human
   coronavirus-229E ([65]37). A correlation between ANPEP and ACE2 implies
   that ANPEP is relevant in SARS-CoV-2 cell entry ([66]38, [67]39);
   Tyrosine-protein kinase receptor UFO (AXL) specifically interacts with
   the S protein of SARS-CoV-2 according to tandem affinity purification
   (TAP)–mass spectrometry analysis, and its overexpression in HEK293T
   cells promotes viral entry ([68]40); Glutamyl Aminopeptidase (ENPEP), a
   type II integral membrane protein, is identified as a candidate
   co-receptor for SARS-CoV-2 based on the co-expression with ACE2,
   although its involvement in virus infection is not firmly supported
   ([69]38, [70]41). To date, higher ACE2 expression in GBM than in
   GBM-adjacent tissue has been detected in glioma tissues removed from
   one COVID-19 patient ([71]42). The association of ACE2 and other
   coronavirus receptors with the pathogenicity of GBM still needs to be
   explored to better understand how SARS-CoV-2 infection affects the
   clinical characteristics of GBM patients. In this study, we
   investigated the expression profiles of six coronavirus receptors in
   normal brain and GBM tissues by bioinformatics and experimental
   approaches. We also conducted a correlation analysis between
   coronavirus expression and prognosis and immune filtration using
   various web services. Furthermore, to explore the potential molecular
   mechanism of coronavirus receptors in GBM and COVID-19, the
   protein–protein interaction network, functional signaling pathway and
   molecular process regulated by common genes between COVID-19 and
   coronavirus receptor-correlated genes in GBM were analyzed.

Material and Methods

Gene Expression Analysis

   The mRNA expression profiles of ACE2 ([72]NP_001358344.1), DPP4
   ([73]NP_001926.2), ANPEP ([74]NP_001368853.1), AXL ([75]NP_068713.2),
   TMPRSS2 ([76]NP_001128571.1), and ENPEP ([77]NP_001968.3) in human and
   mouse brain were accessed by the Human Protein Atlas database
   ([78]https://www.proteinatlas.org/). The gene expression values in the
   glioblastoma cell lines LN018, LN215, LN229, LN319 and BS149 from
   GDS4468 were downloaded from the Gene Expression Omnibus (GEO) profile
   ([79]https://www.ncbi.nlm.nih.gov/geoprofiles). The normalized RNA-Seq
   data in transcripts per million (TPM) of gene expression from The
   Cancer Genome Atlas (TCGA) datasets based on the clinical features
   (gender, age, race) of GBM were obtained from an interactive web
   resource, UALCAN ([80]http://ualcan.path.uab.edu/analysis-prot.html)
   (normal, n = 5; tumor, n = 156).

Protein Expression Analysis

   The clinical tissue chip of GBM (HBraG090PG01) was purchased from Outdo
   Biotech Co., Ltd. (Shanghai, China) (normal brain, n = 3; GBM, n = 25).
   Immunohistochemistry (IHC) was conducted as follows: the sections were
   dewaxed in xylene, rehydrated in grade alcohol, and then incubated with
   5% bovine serum albumin to block nonspecific antigen binding.
   Afterwards, sections were probed overnight at 4°C with primary
   antibodies (Abcam, Cambridge, USA) against ACE2 (ab108252, 1:6,400),
   TMPRSS2 (ab92323; 1:4,000), DPP4 (ab215711; 1:400), AXL (ab219651;
   1:500), ANPEP (ab108310; 1:1,600), and ENPEP (ab155991; 1:100) and then
   incubated with a secondary antibody against rabbit IgG (ZSGB-BIO,
   Beijing, China) for 120 min at 37°C. The sections were stained with
   diaminobenzidine and counterstained with hematoxylin. The score of
   IHC-based protein expression was calculated by Aipathwell (Servicebio)
   according to the intensity of cytoplasmic staining (no staining = 0;
   weak staining = 1, moderate staining = 2 and strong staining = 3), and
   H-Score (H-SCORE = ∑(I × i) = percentage of weak intensity area × 1) +
   (percentage of moderate intensity area × 2) + (percentage of strong
   intensity area × 3). In addition, representative immunohistochemistry
   images of the cerebral cortex of normal brain were extracted from the
   Human Protein Atlas database.

Survival Analysis

   Kaplan–Meier survival curves and Cox regression analysis were conducted
   using R language to assess the correlation of receptor expression and
   clinical outcomes. Patients were divided into high expression and low
   expression groups based on the median receptor expression levels. The
   log rank test was used to calculate the significance of survival
   differences caused by receptors expression. Univariate and multivariate
   Cox analyses were used to assess the expression of receptors and
   clinical characteristics of GBM patients using the CGGA (Chinese Glioma
   Genome Atlas) ([81]http://www.cgga.org.cn/).

Immune Infiltrate and Subtype Analysis

   The functional heatmap table of the association between coronavirus
   receptors and the infiltration level of monocyte, dendritic cells
   (DCs), natural killer (NK) cells and eosinophil in GBM (n = 153) was
   investigated by the “Immune-Gene” module of the Tumor Immune Estimation
   Resource (TIMER2.0) ([82]http://timer.cistrome.org/) platform, which
   comprised immune infiltrate data from TCGA patients. The red indicates
   a significant positive association, the blue indicates a significant
   negative association, and the gray presents a non-significant result.

Gene Enrichment Analysis in GBM

   The top 100 receptor-correlated targeting genes were extracted from the
   “similar gene detection” module of Gene Expression Profiling
   Interactive Analysis (GEPIA2, [83]http://gepia2.cancer-pku.cn/#index)
   using GBM tumors from the TCGA dataset and normal brain samples from
   the Genotype-Tissue Expression Project (GTEx) dataset. An intersection
   analysis was performed by E Venn ([84]http://www.ehbio.com/test/venn)
   to compare the top 100 similar genes among each coronavirus receptors.
   The KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment pathways
   and GO (Gene Ontology) analyses were conducted as follows: the top 100
   similar genes of each receptor were uploaded to the Database for
   Annotation, Visualization and Integrated Discovery (DAVID;
   [85]https://david.ncifcrf.gov/) with the settings of the selected
   identifier (“OFFICAL_GENE_SYMBOL”), species (“Homo sapiens”) and
   functional annotation chart. The enriched pathways with P-values <0.05
   were finally visualized with bubble chart and network chart by the R
   language package.

Co-Expression Analysis of Genes in GBM and COVID-19

   The COVID-19 genes were retrieved from the Comparative Toxicogenomics
   Database (CTD) ([86]https://ctdbase.org/) (The downloaded file is
   CTD_D000086382_genes_20210411080946) ([87]43). To compare the common
   genes between COVID-19 and receptor-related genes in GBM, an
   intersection analysis of COVID-19 genes and top 100 similar genes from
   each receptor in GBM was conducted by Jvenn, an interactive Venn
   diagram viewer
   ([88]https://bioinfogp.cnb.csic.es/tools/venny/index.html). The direct
   interacting proteins among common genes and six receptors were further
   determined through the Search Tool with the multiple proteins by
   Names/Identifiers from the STRING server
   ([89]https://cn.string-db.org/) and First Neighbors of Selected Nodes
   from Cytoscape software. The combined score was calculated from STRING
   with the selected parameters of homolog and experimentally determined
   interaction. Furthermore, the hierarchical clustering analysis was
   conducted on both row variables (direct interacting proteins) and
   column variables (coronavirus receptors) by the library pheatmap
   function in the R package. In addition, KEGG enrichment pathways and GO
   functional and molecular processes of common genes were analyzed as
   described in Gene Enrichment Analysis in GBM. The model for the
   regulation of ANPEP and ENPEP in GBM against coronavirus infections was
   drawn by BioRender ([90]https://biorender.com/).

Protein–Protein Docking

   The crystallized structure files of the SARS-CoV-2 receptor binding
   domain (RBD) of S1 subunit of the S protein (isolated from 6M0J), ENPEP
   (4KX7), and ANPEP (4FYQ) were downloaded from the RCSB Protein Data
   Bank (PDB) ([91]https://www.rcsb.org/). The possible binding
   configurations between ligands (SARS-CoV-2 RBD) and the receptor
   candidates (ANPEP and ENPEP) were searched by the ZDOCK server
   ([92]https://zdock.umassmed.edu/), a rigid molecular docking approach.
   The best cluster (Top1 prediction) was selected and then analyzed as
   follows: the binding free energy (kcal mol^−1) and a dissociation
   constant (Kd) were processed using the tools of the PRODIGY web server
   ([93]https://bianca.science.uu.nl/prodigy). The buried surface area
   (BSA) (Å2) was shown with the sum of contacting surface values for each
   protein in the complex using the PDBePISA program
   ([94]https://www.ebi.ac.uk/msd-srv/prot_int/pistart.html). The
   graphical images were generated by PyMOL software.

Results

Summary of SARS-CoV-2 in the Central Nervous System (CNS) and Cancer

   COVID-19 patients have been frequently reported to show neurologic
   manifestations, namely, headache, dizziness, depression, lethargy,
   impaired sense of smell and taste, and loss of muscular coordination
   and autonomic respiratory control ([95]4–[96]9, [97]44, [98]45) ([99]
   Table 1 ). Accumulating evidence from autopsy tissues of COVID-19
   patients has revealed that SARS-CoV-2 RNA is detected in brain tissue,
   cortical neurons, neural and capillary endothelial cells in frontal
   lobe tissue, olfactory nerve, gyrus rectus and brainstem
   ([100]46–[101]49). Human brain organoids also exhibit the neuroinvasive
   capability of SARS-CoV-2 ([102]50, [103]51). However, how SARS-CoV-2
   directly infects the central nervous system (CNS) is still unclear.

Table 1.

   SARS-CoV-2 infection in the central nervous system and cancer.
   COVID-19 in the central nervous system (CNS) COVID-19 with cancer
   Clinical signs Headache, dizziness, loss or disruption of the sense of
   smell (anosmia/dysosmia), taste (ageusia/dysgeusia), loss of muscular
   coordination (ataxia), loss of autonomic respiratory control, lethargy,
   depression
   and anxiety ([104]4–[105]9, [106]44, [107]45) Higher prevalence of
   chest distress, higher death rate, higher rates of ICU admission,
   higher rates of having at least one severe or critical symptom in
   COVID-19 patients with lung cancer, gastrointestinal cancer, breast
   cancer, hematologic cancer, or metastatic cancer ([108]55, [109]56);
   Increased virus-associated lymphopenia in cancer patients ([110]57).
   Viral detection SARS-CoV2 is detected in brain tissue ([111]46),
   cortical neurons ([112]47), neural and capillary endothelial cells in
   frontal lobe tissue ([113]48), the olfactory nerve, the gyrus rectus
   and the brainstem in autopsy tissue obtained from COVID-19 patients
   ([114]49). The neuroinvasive capability of SARS-CoV-2 is also
   determined in human brain organoids ([115]50, [116]51). Prolonged viral
   shedding and higher viral loads in cancer patients ([117]57, [118]58).
   Receptor expression ACE2 is expressed in the substantia nigra and brain
   ventricles, the piriform cortex, neurons and some nonneuron cells
   (astrocytes and oligodendrocytes from the middle temporal gyrus and
   posterior cingulate cortex) ([119]52–[120]54); TMPRSS2 is present in
   oligodendrocyte precursor cells, astrocytes and microglial cells of the
   neurovascular units ([121]53, [122]54). DPP4 and ANPEP are distributed
   in astrocytes and microglial cells of neurovascular units ([123]54).
   ACE2 is downregulated in hepatocellular carcinoma ([124]59), non-small
   cell lung cancer (NSCLC) ([125]60), breast tumors ([126]61), pancreatic
   ductal adenocarcinoma ([127]62), and gallbladder cancer ([128]63); ACE2
   and TMPRSS2 are both upregulated in colorectal tumor ([129]64) and lung
   cancer ([130]65). TMPRSS2 is downregulated in head and neck cancer
   ([131]66).
   [132]Open in a new tab

   It has been well established that SARS-CoV-2 binds to host cells
   through its S protein to ACE2 ([133]20–[134]25), and subsequently the
   arginine and lysine residues of ACE2 are cleaved by TMPRSS2, which is
   an important step for S protein priming before viral cell entry
   ([135]26–[136]29). In addition to ACE2 and TMPRSS2, several other
   molecules have also been suggested to participate in SARS-CoV-2 cell
   entry, such as DPP4, ANPEP, AXL, and ENPEP ([137]30–[138]41). ACE2 is
   detected in the CNS, namely, substantia nigra and brain ventricles,
   piriform cortex, neurons and some nonneuronal cells (astrocytes and
   oligodendrocytes from the middle temporal gyrus and posterior cingulate
   cortex) ([139]52–[140]54). TMPRSS2 is observed in oligodendrocyte
   precursor cells, astrocytes and microglial cells of the neurovascular
   units ([141]53, [142]54). DPP4 and ANPEP are distributed in astrocytes
   and microglial cells of neurovascular units ([143] Table 1 ) ([144]54).

   COVID-19 patients with lung cancer, gastrointestinal cancer, breast
   cancer, hematologic cancer, or metastatic cancer have experienced a
   higher death rate, ICU admission, and at least one severe or critical
   symptom (e.g., chest distress) ([145]55, [146]56). Meanwhile, an
   increase in virus-associated lymphopenia, prolonged viral shedding and
   higher viral loads has also been observed in cancer patients ([147]57,
   [148]58). Nevertheless, compared with normal tissue, the downregulation
   of ACE2 has been identified in hepatocellular carcinoma ([149]59),
   non-small cell lung cancer (NSCLC) ([150]60), breast tumors ([151]61),
   pancreatic ductal adenocarcinoma ([152]62), and gallbladder cancer
   ([153]63), while the upregulation of ACE2 and TMPRSS2 has been
   identified in colorectal tumors ([154]64) and lung cancer ([155]65).
   TMPRSS2 is decreased in head and neck cancer ([156]66). To date,
   immunohistochemical staining of glioma tissues surgically removed from
   one COVID-19 patient showed that ACE2 expression is higher in GBM than
   in GBM-adjacent tissue ([157]42), but little is known about whether
   other coronavirus receptors are expressed in GBM ([158] Table 1 ). To
   understand the pathogenesis and development of SARS-CoV-2 in GBM, we
   subsequently carried out bioinformatics analysis using various web
   programs to identify oncogenic features of ACE2, DPP4, ANPEP, AXL,
   TMPRSS2 and ENPEP in GBM.

Expression Patterns of Coronavirus Receptors in Normal Brain Tissue/Regions

   We first analyzed the expression pattern of coronavirus receptors mRNA
   in different regions of the brain in humans using Human Protein Atlas
   (HPA) datasets, namely, cerebral cortex, olfactory region, hippocampal
   formation, amygdala, basal ganglia, thalamus, hypothalamus, midbrain,
   pons and medulla, and cerebellum. As shown in [159]Figure 1A , human
   ACE2 was negligibly expressed in all detected regions. Human DPP4 is
   detected in the cerebral cortex, with little distribution in other
   regions. Human ANPEP is distributed in some regions, namely, the
   cerebral cortex, olfactory region, amygdala, midbrain, pons, medulla
   and cerebellum. Human AXL was highly expressed in all detected regions,
   whereas human TMPRSS2 was negligibly expressed in the tested regions.
   Human ENPEP is exhibited in many regions, namely, the cerebral cortex,
   hippocampal formation, basal ganglia, and pons and medulla.

Figure 1.

   [160]Figure 1
   [161]Open in a new tab

   Expression profile of coronavirus receptors in different regions of the
   brain. (A) Human brain datasets. Heatmap of the expression profiles of
   ACE2, DPP4, ANPEP, AXL, TMPRSS2, and ENPEP extracted from the consensus
   human brain datasets of the Human Protein Atlas (n = 441) (B)
   Representative image of immunohistochemistry images of coronavirus
   receptors in the cerebral cortex of human brain (source: The Human
   Protein Atlas; [162]https://www.proteinatlas.org/humanproteome/brain)
   (n = 2).

   Furthermore, immunohistochemistry analysis of these coronavirus
   receptors expression in the cerebral cortex was extracted from the HPA
   database as follows: ACE2 and TMPRSS2 protein are negligible in the
   cerebral cortex; DPP4 occurs in glial cells and neuronal cells; ANPEP
   is observed in endothelial cells; AXL and ENPEP are present in
   endothelial cells and neuronal cells ([163] Figure 1B ).

Expression Profiles of Coronavirus Receptors in GBM

   We further analyzed the expression of coronavirus receptors in GBM to
   dissect their oncogenic role. RNA expression data available on public
   database were extracted from cell lines, normal and tumor samples.
   Firstly, GEO profile GDS4468 shows the expression profiles of receptors
   in following glioblastoma cell lines (LN018, LN215, LN229, LN319 and
   BS149): human ACE2, AXL, and TMPRSS2 are widely expressed in these five
   cell lines; meanwhile, human DPP4, ANPEP, and ENPEP are highly
   distributed in BS149, followed by LN018, LN215, LN229, and LN319 ([164]
   Figure 2A ). Secondly, we analyzed the expression of coronavirus
   receptor genes in the GBM dataset using the UALCAN program. ACE2, DPP4,
   ANPEP, and ENPEP were significantly upregulated in GBM patient samples,
   while AXL and TMPRSS2 were comparable between normal and GBM samples
   ([165] Figure 2B ). To validate these observations, we performed an
   immunohistochemical analysis to identify the expression of coronavirus
   receptor proteins in pathological GBM tissue chips. As shown in
   [166]Figure 2C , ANPEP and ENPEP protein were markedly increased in GBM
   patients compared with normal people. Possibly due to the small sample
   size, no significant differences in ACE2, DPP4, AXL, and TMPRSS2
   protein were observed between normal and tumor patients.

Figure 2.

   [167]Figure 2
   [168]Open in a new tab

   Expression pattern of coronavirus receptors in glioblastoma multiforme
   (GBM). (A) CoV receptor mRNA in glioblastoma cell lines LN018, LN215,
   LN229, LN319, and BS149 (recurrent glioblastoma) from GDS4468. (B)
   Coronavirus receptor mRNA between normal (n = 5) and GBM (n = 156)
   tissues extracted from the TCGA database by the UALCAN program. (C)
   Representative images of immunohistochemistry images of receptors in
   normal (n = 3) and GBM (n = 27) tissue chips. (D) Histologic scores.
   Mean +SEM. Significance comparison is to normal people. Scale bar = 50
   μm. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001, NS: no
   significance P > 0.05.

   Thirdly, we dissected the relationship between the expression of
   coronavirus receptors and clinical characteristics ([169] Figure S1 ).
   For age, the levels of ACE2 and DPP4 are increased in patients under 60
   years old. The levels of ANPEP and ENPEP were significantly different
   among patients of different ages and peaked at 60–80 years old.
   Nevertheless, the levels of AXL and TMPRSS2 were not significantly
   different in patients of various ages. For gender, the expression
   levels of all six receptors were not significantly different between
   male and female. For race, the levels of ACE2, DPP4, TMPRSS2, and ENPEP
   were comparable among the three races. Nevertheless, the level of AXL
   was higher in Asians than in Caucasian, while ANPEP was lower in Asian
   than in Caucasian.

Survival Analysis of Coronavirus Receptors in GBM

   To evaluate whether coronavirus receptors expression levels are
   associated with tumor prognosis in GBM, Kaplan–Meier survival curves
   were generated with TCGA and CGGA data. As shown in [170]Figure 3A ,
   high levels of ANPEP and AXL were significantly linked to poor
   prognosis for TCGA samples, whereas the correlations of high levels of
   ANPEP and ENPEP with poor prognosis were identified in CGGA cases
   ([171] Figure S2A ). In addition, univariate and multivariate analyses
   were conducted to evaluate the impact of each coronavirus receptors
   expression and other clinicopathological factors using the Cox
   proportional hazard regression model on survival. As shown in
   [172]Figure 3B , the univariate analysis showed that ANPEP (Hazard
   ratio: 1.447; P <0.001), DPP4 (Hazard ratio: 1.399; P <0.001), and
   ENPEP (Hazard ratio: 1.399; P <0.001) were negative predictors of
   survival. Furthermore, multivariate analyses of receptor expression and
   other clinicopathological variables showed as follows: ENPEP (Hazard
   ratio:1.243; P <0.001), PRS type (Hazard ratio: 1.974; P <0.001), grade
   (Hazard ratio:2.688; P <0.001) and age (Hazard ratio: 1.227; P = 0.043)
   were negative predictors of survival; DPP4 (Hazard ratio: 0.883; P =
   0.043), chemo (Hazard ratio: 0.660; P <0.001), IDH_mutation (Hazard
   ratio: 0.567; P <0.001), and 1p19q_codeletion (Hazard ratio: 0.387; P
   <0.001) were positive predictors of survival ([173] Figure S2B ).
   Overall, the Kaplan–Meier survival curves indicate that high expression
   of ANPEP, AXL and ENPEP is correlated with poor prognosis, and the
   further multivariate analysis demonstrates that high expression of
   ENPEP can be a negative predictor of survival.

Figure 3.

   [174]Figure 3
   [175]Open in a new tab

   Prognosis and survival analysis of coronavirus receptors in GBM. (A)
   Kaplan–Meier survival curves in the TCGA database. Red indicates high
   expression, and blue indicates low expression (n = 168). (B) Forest
   plot for the univariate Cox proportional hazard regression model in the
   CGGA database (n = 216).

Association of Coronavirus Receptors With Immune Infiltration in GBM

   Tumor-infiltrating immune cells are independent predictors of the
   prominent components of the tumor microenvironment and are closely
   linked to the initiation, progression or metastasis of cancer
   ([176]67). We therefore next investigated the relationship between
   coronavirus receptors and immune infiltration levels across different
   immune subtypes in GBM. According to the key survival-related immune
   cells for GBM shown in a gene expression-based study from TCGA
   datasets, monocytes, DCs, NK cells and eosinophils were selected
   ([177]68). As shown in [178]Figures 4A–D , ACE2 expression was found to
   be positively correlated with monocyte immune infiltration. DPP4 showed
   a positive Spearman’s correlation with monocytes, DCs and NK cells in
   some algorithms, but a negative correlation with eosinophils. The ANPEP
   expression level was positively correlated with monocytes, DCs and
   resting NK cells, but negatively correlated with some algorithms of
   monocytes, activated DCs and NK cells. AXL expression levels were
   positively correlated with infiltrating levels of monocytes, DCs and NK
   cells. TMPRSS2 was significantly positively correlated with monocytes,
   but negatively correlated with DCs and NK cells. Compared with the
   negative correlation of ENPEP expression levels with in some algorithms
   of monocytes and DCs, a significant positive association with
   monocytes, DCs and NK cells was observed. These findings strongly
   indicated that coronavirus receptors play a vital role in immune
   infiltration in GBM.

Figure 4.

   [179]Figure 4
   [180]Open in a new tab

   The functional heatmap table of the correlation between coronavirus
   receptor expressions and immune infiltration levels of different cell
   types in GBM by TIMER2.0. (A) Monocyte. (B) DCs. (C) NK cell. (D)
   Eosinophil. n = 153. GBM, glioblastoma.

Enrichment Analysis of Coronavirus Receptor-Related Genes in GBM

   To further investigate the molecular mechanism of the coronavirus
   receptors in GBM tumorigenesis, we obtained the top 100 genes
   correlated with coronavirus receptors utilizing the combination of GBM
   tumors from the TCGA dataset and normal brain samples from the GTEx
   dataset. An intersection analysis among coronavirus receptors revealed
   the following: 1 common gene between ACE2 group and AXL group
   (ZFP36L1); 9 common genes between DPP4 and TMPRSS2; 12 common genes
   between DPP4 and AXL; 16 common genes between DPP4 and ANPEP; 3 common
   genes between ANPEP and TMPRSS2 ([181] Figure 5A ).

Figure 5.

   [182]Figure 5
   [183]Open in a new tab

   Coronavirus receptor-related gene enrichment analysis in GBM. (A) An
   intersection analysis of the top 100 receptor-correlated genes with GBM
   among different receptor groups according to the E Venn diagram. (B)
   Bubble chart for KEGG enrichment pathway analysis based on total
   receptor-related genes. (C) The cnetplot of all genes in the yellow
   module that depicts the linkages of genes and the most important
   signaling pathways. (D) Functional and molecular processes related to
   coronavirus receptor-related genes in GBM.

   Furthermore, we combined the top 100 related genes from each
   coronavirus receptor to conduct KEGG and GO enrichment analyses. The
   enrichment of KEGG pathways revealed that those genes were highly
   associated with the following pathways during GBM tumor pathogenesis:
   the PI3K−Akt signaling pathway, focal adhesion, protein digestion and
   absorption, cytokine–cytokine receptor interaction, proteoglycans in
   cancer, etc. ([184] Figure 5B ). The cnetplot displays the relationship
   of coronavirus receptor-correlated genes in GBM with functional
   signaling pathways ([185] Figure 5C ). Furthermore, the GO enrichment
   analysis of biological process (BP), cellular component (CC) and
   molecular function (MF) revealed that those coronavirus
   receptor-correlated genes in GBM are associated with membrane receptor
   function-related gene terms, namely, signal transduction, integral
   component of membrane and receptor binding ([186] Figure 5D ).

Employment of Coronavirus Receptor-Related Genes in GBM and COVID-19

   To address the potential relationship between COVID-19 and coronavirus
   receptor-associated genes in GBM, an intersection analysis was applied
   using a Venn diagram. As shown in [187]Figure 6A , 245 common genes
   were found between COVID-19 and coronavirus receptor-correlated genes
   in GBM. To further dissect the depth of the disease and predict
   phenotypic–genotypic associations, the direct interacting proteins with
   coronavirus receptors were identified through STING and Cytoscape
   software, resulting in 30 genes shown in [188]Figure 6B . Coronavirus
   receptors can interact with each other, such as the binding of ACE2 to
   TMPRSS2, DPP4, ANPEP or ENPEP; the binding of DPP4 to TMPRSS2, ANPEP or
   ENPEP; and the binding of ANPEP to ENPEP. AXL can bind to many genes
   but not coronavirus receptors. FN1 gene can be recognized by four
   coronavirus receptors, namely, ACE2, DPP4, ANPEP, and AXL. The KEGG
   pathway enrichment analysis showed that 245 common genes were highly
   associated with proteoglycans in cancer, the PI3K−Akt signaling
   pathway, pathways in cancer, focal adhesion, and cytokine–cytokine
   receptor interactions ([189] Figure 6C ). Moreover, the GO enrichment
   analysis showed that 245 common genes were involved in signal
   transduction-related gene terms, namely, protein binding, integral
   component of membrane and signal transduction ([190] Figure 6D ).

Figure 6.

   [191]Figure 6
   [192]Open in a new tab

   Enrichment analysis of COVID-19-related genes and coronavirus
   receptor-related genes in GBM. (A) An intersection analysis between
   7,230 COVID-19 genes downloaded from the Comparative Toxicogenomics
   Database (CTD) and the top 100 related genes of each receptor in GBM. A
   total of 245 common genes were identified between COVID-19 and
   coronavirus receptor-correlated genes in GBM. (B) Hierarchical
   clustering analyses of the direct interaction proteins across 245
   common genes with coronavirus receptors. The color key represents the
   combined score calculated by the selected parameters of the homolog and
   experimentally determined interaction of the STRING program. Red,
   yellow and blue refer to high, medium and low combined scores,
   respectively. (C) Bubble chart for KEGG enrichment pathway analyses of
   common genes. (D) Bubble chart for GO functional and molecular
   processes of common genes.

Discussion

How Does the Virus Enter the Brain?

   SARS-CoV-2 infections had neurologic manifestations in 38.4% of
   patients and 45.5% of severe patients in a case analysis, usually along
   with headache, dizziness, impaired consciousness, and smell and taste
   disorders ([193]5, [194]44, [195]45). Remarkably, SARS-CoV-2 RNA has
   been detected in many different brain tissues, namely, cortical
   neurons, frontal lobe tissue, the olfactory nerve and brainstem
   ([196]46–[197]49), and cerebrospinal fluid specimens ([198]10).
   However, how the virus affects the brain is still obscure. Accumulating
   studies have provided the following hypotheses to explain these viral
   invasions in the brain: (1) through the olfactory route ([199]69,
   [200]70), (2) through retrograde routing from the vagal nerve to the
   medullary cardiorespiratory center in the brainstem ([201]49), and (3)
   through hematogenous routing from the blood–brain barrier (BBB) and
   blood–cerebrospinal fluid barrier (BCSFB) ([202]71). Therefore, tight
   junctions between adjacent endothelial cells form the basic structure
   of the BBB, which plays a critical role in limiting virus paracellular
   trafficking and is therefore thought to be the major route for
   coronavirus entry into the CNS ([203]71).

   Interestingly, ACE2 and TMPRSS2, the two widely accepted receptors in
   SARS-CoV-2 cell entry, have been reported to be relatively low in
   endothelial cells of the human brain in two studies ([204]52, [205]72),
   which is consistent with the results shown in [206]Figure 1 .
   Nevertheless, the coronavirus receptors ANEPE, AXL, and ENPEP were
   detected in the olfactory region and endothelial cells of human brain
   ([207] Figures 1B and [208]2C ), indicating that SARS-CoV-2 cell entry
   in human brain might require these three receptors rather than rely on
   ACE2 and TMPRSS2.

The Association of Elevated ANPEP and ENPEP Levels With GBM Progression

   A majority of studies focus on the immunosuppressive effect of
   anti-cancer therapies, which results in the increased susceptibility of
   cancer patients to COVID-19. However, an increasing number of papers
   have provided evidence that virus can directly interact with tumors,
   such as the upregulation of the coronavirus receptor ACE2
   ([209]73–[210]76), increased SARS-CoV-2-associated lymphopenia,
   prolonged viral shedding and higher viral loads ([211]57, [212]58).
   Therefore, the mechanisms for the increased susceptibility and severity
   of cancer to SARS-CoV-2 remain unclear.

   Immunohistochemical staining of the GBM tissue chip showed that the
   protein levels of ANPEP and ENPEP were significantly increased in GBM
   ([213] Figure 2C ), although the mRNA levels of ACE2, DPP4, ANPEP, and
   ENPEP were upregulated in GBM according to the UALCAN server ([214]
   Figure 2B ). In fact, the BS149 cell line generated from recurrent
   glioblastoma is more malignant than the other four glioblastoma cell
   lines (LN018, LN215, LN229, and LN319) and has higher mRNA levels of
   DPP4, ANPEP, and ENPEP ([215] Figure 2A ), further corroborating the
   potential oncogenic roles of ANPEP and ENPEP in GBM. Moreover, the
   levels of ANPEP and ENPEP were significantly upregulated with
   increasing age and peaked at 60–80 years old ([216] Figure S1A ), which
   agrees with the peak incidence of GBM between 70 and 79 years old
   ([217]77). Kaplan–Meier survival curves and the Cox regression analysis
   demonstrated that high expression of ANPEP and ENPEP was associated
   with poor prognosis and that ENPEP was a negative predictor of
   survival. Furthermore, the direct interacting proteins with ANPEP and
   ENPEP were FN1, CALR, PDGFRB, CD68, CD63, NRP1, SDC1, and SERPINA1, all
   of which are involved in the progression of GBM ([218] Figure 6B )
   ([219]78–[220]85). All of these findings suggest that increasing ANPEP
   and ENPEP levels are associated with GBM progression.

The Potential Interactions of SARS-CoV-2 With ANPEP or ENPEP by ZDOCK

   The protein and protein interaction network showed that ANPEP, ACE2,
   DPP4, and ENPEP can form a protein complex ([221] Figure 6B ),
   indicating that ANPEP and ENPEP might be directly involved in
   brain-SARS-CoV-2 communication, similar to ACE2, although no data have
   firmly supported ANPEP and ENPEP as receptors for SARS-CoV-2. To
   predict the potential molecular interactions of SARS-CoV-2 with ANPEP
   or ENPEP, we conducted docking simulations through ZDOCK Server, a
   rigid body computational docking program. The Top1 prediction was
   selected for the following analysis ([222] Supplementary Figure 3 ).
   The complex of SARS-CoV-2 RBD with ENPEP has a △G value of −16.7 kcal
   mol^−1, Kd value of 5.20E−13 and buried interface area of 2,544 Å2,
   which are higher than those observed in the complex with ANPEP. These
   values indicate that ENPEP might have a higher affinity to RBD than
   ANPEP. However, the rigid body assumption by these computational
   docking programs will clearly introduce limitations on accuracy and
   reliability ([223]86). In particular, there are a limited number of
   known homologous protein–protein interactions of ENPEP or ANPEP with
   other viral spike proteins. Therefore, further experiments are needed
   to verify whether ANPEP or ENPEP can truly bind to SARS-CoV-2 RBD.

   Furthermore, as immune responses are critical to SARS-CoV-2 infection,
   immunological aspects mediated by ANPEP and ENPEP cannot be overlooked.
   Our analysis revealed that ANPEP and ENPEP expression is highly
   associated with the immune infiltration of macrophages, monocytes, DCs
   and NK cells ([224] Figure 4 ), suggesting that ANPEP and ENPEP can
   play an important role in cellular immunity by regulating the immune
   infiltrate during GBM-affected by SARS-CoV-2. In fact, increased ANPEP
   expression is a hallmark of inflammation in neurodegenerative disease,
   and impaired ANPEP activity has been investigated as a target for
   anti-inflammatory therapy ([225]87, [226]88).

   Overall, the expression pattern and survival analysis of six receptors
   in GBM demonstrated that the upregulation of ANPEP and ENPEP is
   associated with poor survival of GBM. The distribution of ANPEP and
   ENPEP in endothelial cells of the blood–brain barrier provides the
   place for SARS-CoV-2 cell entry into the brain, and the potential
   binding of ANPEP or ENPEP to RBD by protein–protein docking offers
   tools for SARS-CoV-2 infection, which in turn contributes to the
   increased susceptibility of GBM to SARS-CoV-2 ([227] Figure 2C and
   [228]Supplementary Figure 3 ). Therefore, the overlap of poor survival,
   increased risk of GBM to SARS-CoV-2, and high immune infiltration might
   result in the severity of patients with GBM infected by SARS-CoV-2. The
   possible conclusion is supported by 39% mortality of GBM-SARS-CoV-2 in
   one cohort in France ([229] Figure 7 ) ([230]16). This study uncovers
   the relationship between COVID-19 and GBM. We explored the association
   of coronavirus receptors with GBM and identified ANPEP and ENPEP as
   potential biomarkers and therapies for COVID-19 and GBM.

Figure 7.

   [231]Figure 7
   [232]Open in a new tab

   Model for the regulation of ANPEP and ENPEP in GBM against coronavirus
   infections. Previous studies describe ACE2 and TMPRSS2 as the receptor
   or co-receptor for SARS-CoV and SARS-CoV-2; AXL for SARS-CoV-2; DPP4
   for MERS-CoV; ANPEP for human coronavirus-229E; and DPP4, ENPEP and
   AENPEP as the candidate receptor for SARS-CoV-2. ANPEP and ENPEP are
   distributed in endothelial cells of the blood–brain barrier, through
   which coronaviruses enter the CNS. Protein-protein docking analysis of
   ANPEP or ENPEP to RBD of SARS-CoV-2 combined with the upregulations of
   ANPEP and ENPEP in GBM may cause the increase of susceptibility of GBM
   to SARS-CoV-2. The high levels of ANPEP and ENPEP in GBM is associated
   with poor survival and high immune infiltration. The overlap of
   increased risk of GBM to SARS-CoV-2, poor survival, and high immune
   infiltration may result in the severity of patients with GBM infected
   by SARS-CoV-2.

Data Availability Statement

   The data that support the findings of this study are available from the
   corresponding author upon reasonable request.

Ethics Statement

   The studies involving human participants were reviewed and approved by
   the Shanghai Outdo Biotech Company Ethics Committee. Written informed
   consent for participation was not required for this study in accordance
   with the national legislation and the institutional requirements.

Author Contributions

   JY and AC designed the research. AC, WZ, XLL, GS, ZM, LP, ZS, XGL, and
   JY performed the experiments and analyzed the data for this work. JY
   and AC wrote the manuscript. All authors listed have made a
   substantial, direct, and intellectual contribution to the work and
   approved it for publication.

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

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   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
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Acknowledgments