omino Vys
IVYSPRING INTERNATIONAL PUBLISHER
Journal of Cancer
2024; 15(17): 5691-5709. doi: 10.7150/jca.98240
Research Paper
Pan-cancer analysis of the role of a2C-adrenergic receptor (ADRA2C) in human tumors and validation in glioblastoma multiforme models
Xiaoxiao Zhang1#, Huitong Chen1#, Chenyang Wang2, Chan Chen1, Liyan Liu1, Shuangfa Nie2, Xiang Gao3, Ning Huang1, Junli Chen1,4x
1. Department of Pathophysiology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China.
2. Department of Gastrointestinal Surgery, the First Affiliated Hospital of Hebei North University, Zhangjiakou 075061, China.
3. Department of Neurosurgery and Institute of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu 610041, China.
4. NHC Key Laboratory of Chronobiology (Sichuan University), Chengdu 610041, China.
# These authors made equal contributions to this work.
☒ Corresponding authors: Ning Huang, Junli Chen, Email: huangpanxiao@sina.com; chenjunli@scu.edu.cn.
@ The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See https://ivyspring.com/terms for full terms and conditions.
Received: 2024.05.08; Accepted: 2024.06.30; Published: 2024.09.09
Abstract
Background: Several studies have reported the relationship between a2C-adrenergic receptor (ADRA2C) and both neoplastic and non-neoplastic diseases. However, a comprehensive pan-cancer analysis is currently lacking.
Methods: Utilizing the RNA sequencing (RNA-seq) datasets from The Cancer Genome Atlas (TCGA) database, the roles of ADRA2C in human pan-cancer were analyzed through a variety of bioinformatics approaches, including R programming language and single-cell sequencing data analysis, et al. Besides, cell migration assay and immunochemistry were employed to further validate the role of ADRA2C in glioblastoma multiforme (GBM) cell lines and GBM mouse model.
Results: A total of 33 cancer types were involved in this study. It was revealed that the expression level of ADRA2C varied across different clinical stages in patients with breast invasive carcinoma (BRCA), esophageal adenocarcinoma (ESCA), kidney renal papillary cell carcinoma (KIRP) and lung squamous cell carcinoma (LUSC). Meanwhile, it was found that ADRA2C may play roles in prognosis of adrenocortical carcinoma (ACC), glioblastoma multiforme and lower grade glioma (GBM-LGG), and uveal melanoma (UVM). Functional enrichment analysis suggested that ADRA2C expression level was highly correlated with neuronal system-related pathways. Moreover, ADRA2C may be a promising diagnostic marker for cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), GBM, GBMLGG, kidney chromophobe (KICH), and KIRP. Additionally, ADRA2C expression level was correlated with the levels of several infiltrating cells and immune checkpoint genes. Besides, the single-cell sequencing data analysis indicated that ADRA2C played a role in multiple tumor development processes in GBM, retinoblastoma (RB), and UVM. Finally, in vitro and in vivo experiments confirmed that the expression level of ADRA2C may be associated with glioma cell migration, apoptosis, and invasion.
Conclusion: ADRA2C exhibited to play a notable role in several cancer types, suggesting that ADRA2C could serve as a promising biomarker or target for cancer diagnosis, prognosis, and treatment, particularly for GBM.
Keywords: a2C-adrenergic receptor, pan-cancer analysis, prognosis, apoptosis, invasion, glioblastoma multiforme
Introduction
Cancer poses a notable global threat to both human health and economic development. Studies have primarily concentrated on identifying key
pathogenic factors, understanding their mechanisms, discovering reliable biomarkers for diagnosis and clinical prognosis, and developing new therapeutic
interventions [1-3]. An increasing number of studies have confirmed that genetic markers could provide new insights into the aforementioned issues [4-6].
According to the GeneCards database (https://www.genecards.org/), a2C-adrenergic receptor (ADRA2C) is a subtype of G protein-coupled receptors (GPCRs), which is located in 4p16.3 genomic region. ADRA2C plays a crucial role in regulating neurotransmitter release from sympathetic nerves and adrenergic neurons in the central nervous system, which is involved in disorders, such as schizophrenia [7], heart failure [8], and renal failure [9]. Nevertheless, prior research demonstrated that ADRA2C is involved in the tumorigenesis of diverse cancer types, such as breast cancer [10] and colorectal cancer [11], suggesting that ADRA2C may be a novel biomarker for the diagnosis or treatment of cancer. However, no study has reported the correlation between ADRA2C and pan-cancer, and the function of ADRA2C in pan-cancer remains elusive.
To assess the role of ADRA2C in pan-cancer development, a comprehensive bioinformatics analysis was conducted to explore the relationship between ADRA2C and pan-cancer using The Cancer Genome Atlas (TCGA), UCLAN, Gene Expression Profiling Interactive Analysis (GEPIA), cBioPortal, and CancerSEA databases. Additionally, the results of the bioinformatics analysis were validated in vivo and in vitro through cell migration assays, Western blotting, and immunohistochemistry (IHC).
Methods
ADRA2C expression level in pan-cancer
The ADRA2C gene expression TPM data were obtained from UCSC XENA (https://xenabrowser .net/datapages/), including RNAseq data from TCGA and GTEx databases. The data were processed through Toil [12]. For paired tumor and normal tissues in TCGA pan-cancer, the RNA-seq datasets were downloaded from TCGA level 3 and converted from FPKM (Fragments Per Kilobase per Million) format into TPM (transcripts per million reads) format. Meanwhile, the data were log2 transformed before analysis [13, 14]. The data were analyzed using R packages (Version 3.6.3), including ggplot2 (Version 3.3.3) and ggradar (Version 0.2) packages.
ADRA2C expression across different clinical stages in pan-cancer
For ADRA2C expression analysis across different clinical stages, the data were downloaded from GEPIA database (http://gepia2.cancer-pku.cn/) based on TCGA database. The log2(TPM+1) was utilized for log-scale transformation. Differential gene
expression analysis was conducted through one-way analysis of variance (ANOVA), using pathological stage as a variable for calculating differential expression.
Survival analysis
Kaplan-Meier (KM) analysis was performed to explore the correlation between ADRA2C expression and prognosis of patients with various human cancer types. The RNA-seq data were processed as described above [14]. Three clinical outcomes were evaluated, including overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). The data were analyzed using R packages (Version 3.6.3), involving survminer (Version 0.4.9) and survival (Version 3.2-10) packages.
Genetic alteration analysis of ADRA2C in pan-cancer
The cBioPortal database (https://www .cbioportal.org/) was utilized to collect alteration frequency, structural variant data, mutation type, mutated site information, copy number alteration (CNA), and three-dimensional (3D) structure of the protein across all TCGA tumors. In addition, promoter DNA methylation levels of ADRA2C in pan-cancer were obtained based on TCGA database from the UCLAN database (http://ualcan.path.uab .edu/tutorial.html). The beta-value indicates the level of DNA methylation, ranging from 0 (unmethylated) to 1 (fully methylated). Different beta cut-off values have been considered to indicate hypermethylation [beta-value: 0.7-0.5] or hypomethylation [beta-value: 0.3-0.25].
Gene set enrichment analysis (GSEA) of ADRA2C
Gene set enrichment analysis (GSEA) was performed using gene set collections from the MSigDB [15]. Data processing involved R packages, such as DESeq2 (Version 1.26.0) for differential gene expression analysis, cluster Profiler (Version 3.14.3) for functional annotation [16], and ggplot2 (Version 2.3.3) for visualization. Enrichment with false discovery rate (FDR)<0.25 and adjusted p-value<0.05 was considered as highly confident and statistically significant. STRING database (https://string- db.org/) was utilized to analyze potential protein interactions with ADRA2C. Then the relevant genes obtained were subjected to protein-protein interaction (PPI) analysis. A confidence score > 0.7 was set as the significance threshold. Then Cytoscape software (v3.7.0) was used for visualization and subsequent analysis.
Receiver operating characteristic (ROC) curve analysis
The TPM-normalized TCGA and GTEx RNAseq data (version 7) were obtained from UCSC XENA (https://xenabrowser.net/datapages/) and processed via the Toil pipeline [12]. Subsequently, the data were log2-transformed, and plotting ROC curves and calculation of area under the curve (AUC) values were conducted using pROC (Version 1.17.0.1) and ggplot2 (Version 3.3.3) R packages.
Immune cell infiltration analysis
The TIMER2 database (http://timer.cistrome .org/) was used to analyze the correlation between ADRA2C expression and immune cell infiltration in pan-cancer. Meanwhile, RNA-seq data were obtained from TCGA database and converted from FPKM format into TPM format. Infiltration levels for various immune cell types were evaluated using the single-sample GSEA (ssGSEA), which was performed through the gsva package (Version 1.34.0) in R (version 3.6.3) [14]. Notably, 24 different immune cell types were involved in the analysis [17].
Correlation of ADRA2C expression with immune checkpoint (ICP) genes
The TISIDB database (http://cis.hku.hk/ TISIDB/index.php) was employed to evaluate the correlation between ADRA2C expression and ICP genes.
Analysis of single-cell sequencing data
The CancerSEA database (http://biocc.hrbmu .edu.cn/CancerSEA/home.jsp) was utilized to explore the correlation between ADRA2C expression and different functional states of various cancer cells at a single-cell level [18]. Heatmap was plotted according to the data downloaded from the CancerSEA database using ggplot R package. Moreover, the t-distributed stochastic neighbor embedding (t-SNE) plot was downloaded from the CancerSEA database.
Cell culture and drug treatment
In this experiment, GL261 and U87 cell lines were cultured in a high-glucose Dulbecco’s modified Eagle’s medium (DMEM), containing 10% fetal bovine serum (FBS) (ZETA, NY, USA) and minimum essential medium-non-essential amino acids (MEM-NEAA) (Procell Life Science & Technology Co., Ltd., Wuhan, China) supplemented with 15% FBS, respectively. The cells were incubated at 37 ℃ with 5% carbon dioxide.
For drug treatment, two drugs were utilized in this study: nonselective a2-adrenergic receptor antagonist (phentolamine) and nonselective
a2-adrenergic receptor agonist (noradrenaline, NA). The abovementioned drugs were purchased from MedChemExpress Co. Ltd. (Shanghai, China). Cells were divided into three groups as follows: the control group treated with dimethyl sulfoxide (DMSO), the NA group treated with 10uM of NA for GL261 and 2uM for U87, the phentolamine group treated with 10uM of phentolamine for GL261 and 0.005UM for U87.
An in vivo glioblastoma multiforme (GBM) model
A total of 30 healthy C57BL/6J female mice (6-8 weeks, 17-20 g) were purchased from Laboratory Animal Center of Sichuan University (China). Besides, 1 × 106 GL261 cells were injected into right flank subcutaneously. When the average tumor volume was 100 mm3, mice were divided into 3 groups and injected with drugs intraperitoneally every other day for 14 days as follows: control group received 100uL of corn oil; NA group received 0.5 mg/kg dissolved in 100uL of corn oil; and phentolamine group received 1 mg/kg dissolved in 100uL of corn oil. When the tumor volume reached 2000 mm3, mice were euthanized via cervical dislocation. Tumor tissue samples were subsequently collected, weighed, and fixed in 4% paraformaldehyde for further processing.
Cell migration assay
The migration ability of GL261 and U87 cells was evaluated by scratch wound healing assay. For this purpose, 6 × 105 GL261 and U87 cells were seeded into 6-well plates. On the following day, a scratch was introduced into the cell layer using a 200uL pipette tip when the cells reached approximately 90% confluence. Subsequently, the cells were twice washed with phosphate-buffered saline (PBS), and subsequently treated separately with DMEM containing DMSO, NA, or phentolamine. The same wounded areas were observed and photographed at different time points using an inverted microscope (Olympus, Tokyo, Japan). ImageJ software was used to measure scratched areas, enabling the calculation of cell migration speed. Percentages cell migration was calculated using the following formula: Cell migration rate = (0 h scratch area - 24h scratch area)/0 h scratch area × 100%.
IHC
The tumor tissues from C57BL/6J mice were fixed, processed into paraffin sections, and subjected to IHC. Thereafter, the sections were dewaxed and hydrated before antigen retrieval. The primary antibodies (Bax, BCL-2, and MMP2) and a secondary antibody were incubated. The sections were stained with 3,3’-diaminobenzidine (DAB), followed by
counterstaining with hematoxylin, dehydration, and sealing. Images were thereafter captured using NanoZoomer2.0 HT.
Statistical analysis
Shapiro-Wilk test was used to evaluate normal distribution of data. Correlation was assessed using the Pearson correlation coefficient. p<0.05 was considered statistically significant (ns, p≥0.05; * , p<0.05; ** , p<0.01; *** , p<0.001).
Results
ADRA2C expression in pan-cancer
The expression level of ADRA2C in pan-cancer involving 33 cancer types was analyzed. As illustrated in Figure 1A, compared with normal group, ADRA2C mRNA expression level was higher in cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), rectum adenocarcinoma (READ) and thyroid
A
10
1:
1:
The expression of ADRA2C Log2 (TPM+1)
8
J:
1:
N
1*
ユキ
J:
1:
1
]ま
1:
13
1.
]:
]*
6
Normal
4
J
Tumor
2
0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
B
The expression of ADRA2C Log2 (TPM+1)
8
ns
*
*
*
*
ns
ns
6
ns
*
ns
ns
Normal
4
ns
Tumor
2
0
BLCA
BRCA
CESC
CHOL
COAD
ESCA
HNSC
KICH
KIRC
KIRP
LIHC
LUAD
LUSC
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
THCA
THYM
UCEC
C
D
UCS
ACC
UCEC
BLC/
UCSJVM
ACC
BLCA
THYM
6
BRCA
UCEC
4
BRCA
5
CESC
THYM
CESC
THCA
CHOL
3
CHOL
TGCT
4
THCA
COAD
3
COAD
STAD
TGCT
2
DLBC
2
DLBC
STAD
1
1
ESCA
SKCM
ESCA
0
SKCM
0
GBM
SARC
GBM
SARC
HNSC
READ
HNSC
READ
KICH
PRAD
KICH
PRAD
KIRC
PCPG
KIRC
PCPG
KIRP
PAAD
KIRP
PAAD
LAML
OV
LAML
OV
MES@USC
LUABHC
LGG
LUSQUAD
LIHC-GG
tumor
normal
carcinoma (THCA). Meanwhile, there was a lower ADRA2C mRNA expression level in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), GBM, kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD) and uterine corpus endometrial carcinoma (UCEC).
To further explore the ADRA2C expression level in pan-cancer, the ADRA2C expression level in paired normal and tumor tissues was determined. It was revealed that there was a higher ADRA2C expression level in BLCA, BRCA, CHOL, COAD, HNSC, LIHC, LUAD, and THCA (Figure 1B). Furthermore, ADRA2C expression level was reduced in KICH,
KIRC, KIRP, PRAD and UCEC (Figure 1B). Meanwhile, the normal tissue data from GTEx database indicated a higher ADRA2C mRNA expression level in CESC, KICH, KIRC, KIRP, PRAD, UCEC, and UCS (Figure 1C). For tumor tissue data from TCGA, a higher ADRA2C mRNA expression level was found in CHOL, OV, TGCT, and UCEC (Figure 1D).
The relationship between ADRA2C expression level and clinicopathological stage in pan-cancer
The ADRA2C expression level in different pathological stages in pan-cancer was analyzed. The results indicated that there was a pathological stage-specific expression level of ADRA2C in BRCA, esophageal adenocarcinoma (ESCA), KIRP, and lung squamous cell carcinoma (LUSC) (Figures 2A, 2B, 2C and 2D, p<0.05).
A
B
F value = 3.03 Pr(>F) = 0.0168
F value = 3.97
Pr(>F) = 0.00908
5
4
4
0
3
2
2
-
1
0
0
Stage I
Stage II
Stage III
Stage IV
Stage X
Stage I
Stage II
Stage III
Stage IV
C
D
F value = 4.13 Pr(>F) = 0.00696
F value = 3.9 Pr(>F) = 0.00904
5
6
5
4
+
3
0
2
2
-
-
0
O
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Survival analysis
The correlation between ADRA2C expression level and prognosis in pan-cancer was evaluated. It was revealed that a high ADRA2C expression level was associated with a favorable OS in GBM-LGG (HR= 0.53, p<0.001) and uveal melanoma (UVM) (HR= 0.34, p=0.017) (Figures 3B and 3C), while a high ADRA2C expression level was correlated with a poor OS in adrenocortical carcinoma (ACC) (HR=2.35, p=0.035) (Figure 3A). In addition, a high ADRA2C expression level was associated with a favorable DSS in GBMLGG (HR=0.50, p<0.001) and UVM (HR=0.33, p=0.021) (Figures 3E and 3G), while a high ADRA2C expression level was correlated with a poor DSS in ACC (HR=2.55, p=0.028) and KIRP (HR=2.66, p=0.028) (Figures 3D and 3F).
Moreover, a high ADRA2C expression level was associated with a favorable PFI in GBMLGG (HR=0.58, p<0.001) and UVM (HR=0.36, p=0.012) (Figures 3J and 3K), while a high ADRA2C expression level was correlated with a poor PFI in ACC (HR=2.39, p=0.009), and esophageal squamous cell carcinoma (ESCC) (HR=2.12, p=0.028) (Figures 3H and 3I). Finally, there was no relationship between ADRA2C expression level and prognosis of BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, esophageal adenocarcinoma (ESAD), GBM, HNSC, KICH, KIRC, LAML, LGG, LIHC, LUAD, LUSC, mesothelioma (MESO), oral squamous cell carcinoma (OSCC), OV, PAAD, PCPG, PRAD, READ, sarcoma (SARC), SKCM, STAD, TGCT, THCA, THYM, UCEC, and UCS. Taken together, we considered that ADRA2C was closely related with the prognosis of ACC, GBMLGG and UVM.
Genetic alteration analysis of ADRA2C in pan-cancer
Different genetic mutations of ADRA2C in pan-cancer samples from cBioPortal database are illustrated in Figure 4. As displayed in Figure 4A, the frequency of ADRA2C alteration (2.2%) was the highest in adrenocortical carcinoma with ‘mutation’ as the primary type, and all adrenocortical carcinoma patients had ADRA2C mutation. The ‘amplification’ type of CNA had the highest incidence (2.4%) in ovarian serous cystadenocarcinoma. Meanwhile, the highest frequency in the ‘deep deletion’ type of ADRA2C was found in cervical squamous cell carcinoma patients. Figures 4B and 4C show mutation types, location sites, and case number of ADRA2C. It was found that missense mutation of ADRA2C was the main type of genetic alteration, which was detected in 60 cases, while truncating mutation was detected in only 3 cases. It was also
revealed that the most frequent copy-number alterations of ADRA2C were amplification, gain function, diploid, shallow deletion, and deep deletion (Figure 4D). As displayed in Figure 4E, the genetic alterations of HTT, RGS12, NSD2, OTOP1, LRPAP, DOK7, STK32B, LINC02171, CYTL1, and FAM86EP were more frequent in the ADRA2C altered group than those in the ADRA2C unaltered group. For ADRA2C promoter methylation level, it was found that ADRA2C hypomethylation was identified in most tumor types. Compared with normal tissues, there was a significantly higher methylation level in BRCA, COAD, HNSC, LUAD, PAAD, and PRAD (Figure 5).
Functional enrichment analysis of ADRA2C in pan-cancer
To explore the molecular mechanism of ADRA2C in different tumors, the pathways in which ADRA2C could be involved in pan-cancer were evaluated using GSEA. The results revealed that ADRA2C was commonly correlated with neuronal system-related pathways, especially in ACC, CESC, GBM, GBMLGG, KIRP, and lower grade glioma (LGG) (Figures 6A, 6C, 6D, 6E, 6G, 6H). Furthermore, using a protein-protein interaction (PPI) network, it was indicated that CD161 was closely associated with ADRA2A, AGT, GNAT11, GNA12, GNA13, GNAQ, GNB3, SLC6A2 and SLC6A3 proteins (Figure 6M).
ROC curve analysis
The diagnostic ability of ADRA2C in pan-cancer was evaluated through ROC curve analysis. It was revealed that the AUC value in CESC, CHOL, GBM, GBMLGG, KICH, and KIRP was higher than 0.9 (Figure 7), suggesting that ADRA2C could be a notable diagnostic marker for these tumors.
Immune cell infiltration analysis
In order to fully explore the correlation between ADRA2C expression and immune cell infiltration in various cancer types, immune cell infiltration analysis was conducted utilizing two sources.
The data from TIMER 2.0 indicated that there was a positive correlation between ADRA2C expression level and cancer-associated fibroblast in BLCA, BRCA-Basal, CESC, ESCA, HNSC, KIRP, LIHC, STAD, and THYM, as well as a negative correlation in TGCT (Figure 8A). For the common lymphoid progenitor, a positive correlation was found in TGCT, while a negative correlation was identified in BLCA, BRCA-Basal, COAD, GBM, HNSC, KICH, KIRC, LGG, LUAD, LUSC, OV, PAAD, PCPG, skin cutaneous melanoma (SKCM), STAD, and UCEC (Figure 8B).
A
ACC
B
GBMLGG
C
1.0
UVM
ADRA2C
1.0
ADRA2C
1.0
ADRA2C
Low
Low
Low
Survival probability
0.8
High
Survival probability
0.8
High
Survival probability
0.8
High
0.6
0.6
0.6
++
0.4
0.4
0.4
H
0.2
Overall Survival
0.2
Overall Survival HR = 0.53 (0.41-0.67)
0.2
Overall Survival
HR = 2.35 (1.06-5.21)
HR = 0.34 (0.14-0.82)
0.0
P = 0.035
0.0
P < 0.001
0.0
P = 0.017
0
50
100
150
0
50
100
150
200
0
20
40
60
80
Time (months)
Time (months)
Time (months)
Low
39
16
5
0
Low
347
37
10
2
0
Low
40
21
8
1
0
High
40
12
3
2
High
348
56
16
5
1
High
40
31
11
2
2
D
E
F
1.0
ACC
ADRA2C
1.0
GBMLGG
ADRA2C
1.0
4
KIRP
ADRA2C
Low
Low
Low
Survival probability
0.8
High
Survival probability
0.8
High
Survival probability
0.8
High
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Disease Specific Survival
0.2
Disease Specific Survival-
0.2
HR = 0.50 (0.38-0.65)
Disease Specific Survival
HR = 2.55 (1.10-5.88)
HR = 2.66 (1.17-6.04)
0.0
P = 0.028
0.0
P < 0.001
0.0
P = 0.02
0
50
100
150
0
50
100
150
200
0
50
100
150
200
Time (months)
Time (months)
Time (months)
Low
38
16
5
0
Low
334
35
9
2
0
Low
140
36
4
0
0
High
G
39
11
3
2
High
340
54
H
15
5
1
High
144
35
8
1
0
1.0
UVM
ADRA2C
1.0
ACC
ADRA2C
1.0
ESCC
ADRA2C
Low
Low
Low
Survival probability
0.8
High
Survival probability
0.8
High
Survival probability
0.8
High
0.6
0.6
0.6
0.4
0.4
0.4
+
0.2
Disease Specific Survival
0.2
Progress Free Interval
#
0.2
Progress Free Interval
HR = 0.33 (0.13-0.84)
HR = 2.39 (1.25-4.56)
HR = 2.12 (1.08-4.14)
0.0
P = 0.021
0.0
P = 0.009
0.0
P = 0.028
0
20
40
60
80
0
50
100
150
0
20
40
60
Time (months)
Time (months)
Time (months)
Low
40
21
8
1
0
Low
39
11
4
0
Low
41
9
3
0
J
High
40
31
11
2
2
High
40
8
2
2
High
41
5
1
1
K
1.0
GBMLGG
ADRA2C
1.0
UVM
ADRA2C
Low
Low
Survival probability
0.8
High
Survival probability
0.8
High
0.6
0.6
0.4
0.4
0.2
Progress Free Interval HR = 0.58 (0.46-0.71)
0.2
Progress Free Interval
+
HR = 0.36 (0.16-0.80)
0.0
P < 0.001
0.0
P = 0.012
0
50
100
150
0
20
40
60
80
Time (months)
Time (months)
Low
347
24
6
0
0
Low
39
15
6
0
0
High
348
31
6
1
0
High
40
29
9
2
1
A
3%-
2.5%-
Alteration Frequency
2%-
1.5%-
1%-
0.5%-
Structural variant data
Mutation data CNA data
Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)
Esophageal Adenocarcinoma (TCGA, PanCancer Atlas)
Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)
Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)
Adrenocortical Carcinoma (TCGA, PanCancer Atlas)
Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)
Lung Adenocarcinoma (TCGA, PanCancer Atlas)
Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)
Stomach Adenocarcinoma (TCGA, PanCancer Atlas)
Sarcoma (TCGA, PanCancer Atlas)
Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)
Glioblastoma Multiforme (TCGA, PanCancer Atlas) Breast Invasive Carcinoma (TCGA, PanCancer Atlas) Prostate Adenocarcinoma (TCGA, PanCancer Atlas)
Head and Neck Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Brain Lower Grade Glioma (TCGA, PanCancer Atlas)
Kidney Renal Papillary Cell Carcinoma (TCGA, PanCancer Atlas)
Liver Hepatocellular Carcinoma (TCGA, PanCancer Atlas)
Thyroid Carcinoma (TCGA, PanCancer Atlas) Kidney Chromophobe (TCGA, PanCancer Atlas)
Testicular Germ Cell Tumors (TCGA, PanCancer Atlas)
Acute Myeloid Leukemia (TCGA, PanCancer Atlas)
Kidney Renal Clear Cell Carcinoma (TCGA, PanCancer Atlas)
Diffuse Large B-Cell Lymphoma (TCGA, PanCancer Atlas)
Thymoma (TCGA, PanCancer Atlas)
Uveal Melanoma (TCGA, PanCancer Atlas)
Pheochromocytoma and Paraganglioma (TCGA, PanCancer Atlas)
Mesothelioma (TCGA, PanCancer Atlas)
Uterine Carcinosarcoma (TCGA, PanCancer Atlas) Cholangiocarcinoma (TCGA, PanCancer Atlas)
Mutation
Amplification
Deep Deletion
Multiple Alterations
B
ADRA2C
1.2%*
Genetic Alteration
Missense Mutation (unknown significance)
Truncating Mutation (unknown significance)
Amplification
Deep Deletion
C
No alterations
Not profiled
# ADRA2C Mutations
5
R454*/P/Q
0
7tm_1
D
0
100
200
300
400
462aa
ADRA2C: mRNA Expression, RSEM (Batch normalized from Illumina HiSeq_RNASeqV2) (log2(value + 1))
E
14-
12
70-
Alteration event frequency (%)
10
60-
8
50-
Altered group
6-
40-
Unaltered group
4
30-
2
20-
0
10-
Deep Deletion
Shallow Deletion
Diploid
Gain
Amplification
0-
HTT*
RGS12*
NSD2*
OTOP1*
LRPAP1*
DOK7*
STK32B*
LINC02171*
CYTL1*
FAM86EP*
ADRA2C: Putative copy-number alterations from GISTIC
ADRA2C
Truncating (VUS)
Missense (VUS)
Not mutated
Not profiled for mutations
o Amplification
Gain
Diploid
Shallow Deletion
Deep Deletion
A
Promoter methylation level of ADRA2C in BLCA
B
Promoter methylation level of ADRA2C in BRCA
0.6
0.325
0.5
0.3
0.4
0.275
Beta value
Beta value
0.25
0.3
0.225
0.2
0.2
0.1
0.175
0
Normal (n=21)
Primary tumor (n=418)
0.15
Normal (n=97)
Primary tumor (n=793)
TCGA samples
TCGA samples
C
Promoter methylation level of ADRA2C in COAD
D
Promoter methylation level of ADRA2C in HNSC
0.45
0.3
0.4
0.275
0.35
0.25
Beta value
Beta value
0.3
0.225
0.25
0.2
0.2
0.175
0.15
0.15
0.1
0.125
Normal (n=37)
Primary tumor (n=313)
Normal (n=50)
Primary tumor (n=528)
TCGA samples
TCGA samples
E
Promoter methylation level of ADRA2C in KIRP
F
Promoter methylation level of ADRA2C in LUAD
0.23
*
0.6
0.225
0.5
0.22
0.4
Beta value
Beta value
0.215
0.3
0.21
0.2
0.205
0.1
0.2
0
Normal (n=45)
Primary tumor (n=275)
Normal (n=32)
Primary tumor (n=473)
TCGA samples
TCGA samples
G
Promoter methylation level of ADRA2C in LUSC
H
Promoter methylation level of ADRA2C in PAAD
0.35
0.25
0.3
0.24
0.23
0.25
Beta value
Beta value
0.22
0.2
0.21
0.15
0.2
0.1
0.19
0.05
Normal (n=42)
Primary tumor (n=370)
0.18
Normal (n=10)
Primary tumor (n=184)
TCGA samples
TCGA samples
I
Promoter methylation level of ADRA2C in PRAD
0.4
0.35
0.3
Beta value
0.25
0.2
0.15
0.1
Normal (n=50)
Primary tumor (n=502)
TCGA samples
A
ACC
B
BLCA
C
CESC
WP GENES CONTROLLING RENAL
NEPHROGENESIS
REACTOME MUSCLE CONTRACTION
WP DOPAMINERGIC NEUROGENESIS
REACTOME CHOLESTEROL
BIOSYNTHESIS
REACTOME CARDIAC CONDUCTION
REACTOME DOWNSTREAM SIGNALING
OF ACTIVATED FGFR4
WP HEART DEVELOPMENT
REACTOME NEGATIVE REGULATION
OF FGFRA SIGNALING
REACTOME PI 3K CASCADE FGFR4
WP CHOLESTEROL BIOSYNTHESIS
REACTOME SIGNALING BY TYPE 1
P adjust
INSULIN LIKE GROWTH FACTOR 1
P adjust
REACTOME PHASE 0 RAPID
0.0310
RECEPTOR IGF 1R
DEPOLARISATION
P adjust
PATHWAY
REACTOME POTASSIUM CHANNELS
KEGG DILATED CARDIOMYOPATHY
REACTOME NEGATIVE REGULATION
OF FGFRA SIGNALING
0.0305
0.03
O DET
REACTOME VOLTAGE GATED POTASSIUM CHANNELS
REACTOME SIGNALING BY FGFR4
REACTOME PROTEIN PROTEIN
REACTIE SUNNSAN OU VO
0.0300
REACTOME IRS MEDIATED
REACTOME SIGNALING BY TYPE 1
WP DOPAMINERGIC NEUROGENESIS.
SIGNALLING
INSULIN LIKE GROWTH FACTOR 1
REACTOME SIGNALING BY INSULIN
RECEPTION SEUS
REACTOME FRS MEDIATED FGFRA
REACTOME NEURONAL SYSTEM -
RECEPTOR
SIGNALING
REACTOME NEUREXINS AND
REACTOME REGULATION OF BETA
REACTOME DOWNSTREAM SIGNALING
OF ACTIVATED FGFRA
NEUROLIGINS
CELL DEVELOPMENT
WP HEART DEVELOPMENT
REACTOME IRS MEDIATED
WP 22Q112 DELETION SYNDROME
SIGNALLING
0
1
2
3
4
5
0.0
2.5
5.0
7.5
10.0
0
2
4
6
D
GBM
E
GBMLGG
F
KICH
WP SYNAPTIC VESICLE PATHWAY
REACTOME INTERLEUKIN 10
REACTOME NEURONAL SYSTEM -
REACTOME IMMUNOREGULKTORY
REACTOME NEURONAL SYSTEM
REACTOME TRANSMISSION ACROSS
INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID
REACTOME VOLTAGE GATED
CHEMICAL SYNAPSES
POTASSIUM CHANNELS
REACTOME PROTEIN PROTEIN
REACTOME INITIAL TRIGGERING OF
REACTOME PROTEIN PROTEIN
INTERACTIONS AT SYNAPSES
COMPLEMENT
P adjust
REACTOME NEUREXINS AND NEUROLIGINS
P adjust
INTERACTIONS AT SYNAPSES
0.0250
WP TYROBP CAUSAL NETWORK -
P adjust
REACTOME POTASSIUM CHANNELS
0.038
REACTOME POTASSIUM CHANNELS
0.0245
REACTOME CREATION OF C4 AND C2
0.0240
ACTIVATORS
REACTOME NEUREXINS AND
NEUROLIGINS
KEGG NEUROACTIVE LIGAND
0.027
0.0235
KEGG NEUROACTIVE LIGAND
RECEPTOR INTERACTION
0.0230
REACTOME FOGR ACTIVATION
RECEPTOR INTERACTION
WP SYNAPTIC VESICLE PATHWAY
REACTOME CD22 MEDIATED BCR
REACTOME NEUROTRANSMITTER
REACTOME NEUROTRANSMITTER
REGULATION
RELEASE CYCLE
RELEASE CYCLE
REACTOME ROLE OF LAT2 NTAL LAB ON CALCIUM MOBILIZATION
REACTOME TRANSMISSION ACROSS
CHEMICAL SYNAPSES
KEGG CALCIUM SIGNALING PATHWAY
KEGG LEISHMANIA INFECTION
WP GABA RECEPTOR SIGNALING
REACTOME VOLTAGE GATED POTASSIUM CHANNELS
KEGG NOD LIKE RECEPTOR
G
”
2
3
0
1
2
3
4
5
SIGNALING PATHWAY
0
2
4
6
G
KIRP
H
LGG
LUSC
WP GABA RECEPTOR SIGNALING
REACTOME POTASSIUM CHANNELS
KEGG BASAL CELL CARCINOMA
REACTOME CD22 MEDIATED BCR
REGULATION
REACTOME NEURONAL SYSTEM-
WP MECP2 AND ASSOCIATED RETT
WP NEURAL CREST DIFFERENTIATION
REACTOME VOLTAGE GATED
SYNDROME
WP NCRNAS INVOLVED IN WNT
POTASSIUM CHANNELS
SIGNALING IN HEPATOCELLULAR
REACTOME FCGR ACTIVATION
P adjust
REACTOME TRANSMISSION ACROSS
P adjust
CARCINOMA
WP WNT SIGNALING
P adjust
WP CARDIAC PROGENITOR
CHEMICAL SYNAPSES
DIFFERENTIATION
REACTOME NEUROTRANSMITTER
0.038
RELEASE CYCLE
REACTOME NEUROTRANSMITTER
0.03
WP ESC PLURIPOTENCY PATHWAYS
0.026
NABA COLLAGENS
RECEPTORS AND POSTSYNAPTIC SIGNAL TRANSMISSION
REACTOME PROTEIN PROTEIN INTERACTIONS AT SYNAPSES
REACTOME COLLAGEN CHAIN
TRIMERIZATION
REACTOME NEUREXINS AND
NEUROLIGINS
WP OSTEOBLAST DIFFERENTIATION.
WP GENES CONTROLLING RENAL
NEPHROGENESIS
KEGG NEUROACTIVE LIGAND
RECEPTOR INTERACTION
REACTOME TRANSCRIPTIONAL
REACTOME ECM PROTEOGLYCANS
REACTOME GLUTAMATE
WP LNCHNA INVOLVEMENT IN
NEUROTRANSMITTER RELEASE CYCLE
CANONICAL WNT SIGNALING AND.
COLORECTAL CANCER
WP HEART DEVELOPMENT
WP SYNAPTIC VESICLE PATHWAY
KEGG HEDGEHOG SIGNALING
PATHWAY
0
1
2
3
-1
0
1
2
3
4
5
0
1
2
3
4
5
J
PCPG
K
UCEC
L
UVM
HEAL I UME AL INVAI CU PANI
STIMULATES TRANSCRIPTION OF
REACTOME COMPLEMENT CASCADE
NABA CORE MATRISOME
AR ANDROGEN RECEPTOR REGULATED
REACTOME INITIAL TRIGGERING OF
REACTONE SRNNEUNPOHY
COMPLEMENT
NABA ECM GLYCOPROTEINS
REGULATES RRNA EXPRESSION
WP LNCRNA INVOLVEMENT IN
REACTOME ERCC6 CSB AND EHMT2
REACTOME METABOLISM OF STEROID
HORMONES
CANONICAL WNT SIGNALING AND
GSA POSITIVELY REGULATE RRNA
EXPRESSION
REACTOME CREATION OF C4 AND C2
Paqust
WP NCRRRSONASLA CANGET
Paust
REACTOME HDACS DEACETYLATE
SIGNALING IN HEPATOCELLULAR
HISTONES
P adjust
ACTIVATORS
0.0375
REACTOME CD22 MEDIATED BCR
CARCINOMA
REGULATION
0.029
REACTOME KERATINIZATION
0.0350
REACTOME DNA METHYLATION
0.024
REACTOME FCGR ACTIVATION
REACTOME NEURONAL SYSTEM
0.0325
REACTOME EUKARYOTIC
TRANSLATION ELONGATION
KEGG STEROID HORMONE
0.0300
REACTOME PRC2 METHYLATES
BIOSYNTHESIS
WP WNT SIGNALING
HISTONES AND DNA
KEGG DRUG METABOLISM
REACTOME RHO GTPASES ACTIVATE
CYTOCHROME P450
KEGG BASAL CELL CARCINOMA
PKNS
REACTOME SCAVENGING OF HEME
KEGG ARRHYTHMOGENIC RIGHT
FROM PLASMA
REACTOME METABOLIC DISORDERS
VENTRICULAR CARDIOMYOPATHY
KEGG RIBOSOME
ARVC
OF BIOLOGICAL OXIDATION
WP ARRHYTHMOGENIC RIGHT
REACTOME CONDENSATION OF PROPHASE CHROMOSOMES
ENZYMES
VENTRICULAR CARDIOMYOPATHY
0
2
4
6
0
1
2
3
0
2
4
M
GNA11
ADRA2A
GNA12
SLC6A3
GNA13
SLC6A2
GNB3
ADRA2C
GNAQ
AGT
There was a positive correlation between ADRA2C expression level and the common myeloid progenitor in BLCA, BRCA, LUAD, PAAD, SARC, and UVM, while a negative correlation was found in ACC and KIRP (Figure 8C). For the endothelial cells, there was a significantly positive correlation in BLCA, BRCA, BRCA-Basal, BRCA-lumA, KIRC, LUAD, PAAD, STAD, THYM, and UCEC, whereas there was a negative correlation in TGCT (Figure 8D). In hematopoietic stem cells, there was a positive correlation in BLCA, BRCA, BRCA-Basal, BRCA-lumA, COAD, DLBC, HNSC, KIRC, KIRP, LIHC, LUAD, PAAD, PCPG, PRAD, SARC, SKCM, STAD, THYM, and UCEC, while a negative correlation was identified in TGCT (Figure 8E).
Regarding myeloid-derived suppressor cells, there was a positive correlation in ACC, BRCA-lumA, ESCA, HNSC, KIRC, LUSC, READ, STAD, TGCT, THCA, and THYM, while a negative correlation was identified in BLCA, LUAD, OV, PAAD, SKCM, and SKCM-metastasis (Figure 8F). Previous studies showed that there was a significantly positive correlation between ADRA2C expression level and most of the immune infiltration cell types in ACC, KICH, KIRC, OV, PCPG, and PRAD, and there was a significantly negative correlation between ADRA2C expression level and most of the immune infiltration cell types in LUAD-LUSC, LUSC, OSCC, STAD, and UVM (Figure 8G). It is noteworthy that the data from TIMER2.0 and TCGA exhibited mostly consistent results for B cells, CD8 T cells, dendritic cells (DCs), eosinophils, macrophages, mast cells, neutrophils, natural killer (NK) cells, T follicular helper (TFH) cells, and regulatory T (Treg) cells (data were not shown).
Taken together, all these clues demonstrated ADRA2C expression was associated with several immune cell infiltration and thus regulated the tumor microenvironment.
Correlation of ADRA2C expression level with immunomodulatory genes
It was found that ADRA2C expression level was positively correlated with all immunostimulators in OV (Figure 9A), while it exhibited a negative correlation with nearly all immunoinhibitors in UVM (Figure 9B). Regarding major histocompatibility complex (MHC) molecules, there was a positive correlation between ADRA2C expression level and KICH, as well as OV, while there was a negative correlation between ADRA2C expression level and ACC, BLCA, COAD, HNSC, LGG, LUSC, SARC, SKCM, STAD, and UVM (Figure 9D). These data indicated that ADRA2C expression was related to several immunomodulatory genes associated with
tumor gene therapies.
Analysis of single-cell sequencing data
The analysis of single-cell sequencing data indicated that ADRA2C expression level was significantly positively correlated with angiogenesis and differentiation in GBM and retinoblastoma (RB), as well as inflammation in RB and stemness in UM (Figure 10). However, it was significantly negatively correlated with DNA damage and DNA repair in GBM, RB, and uveal melanoma (UM), as well as cell invasion and metastasis in GBM and UM. Notably, ADRA2C expression level was significantly positively associated with stemness in UM, while negatively with stemness in GBM. Meanwhile, ADRA2C expression level was significantly positively correlated with inflammation in RB, while ADRA2C expression level was significantly negatively associated with apoptosis in UM and cell cycle in RB (Figure 10A). Furthermore, the T-SNE diagram illustrates the ADRA2C expression level in single cells of GBM, RB, and UVM (Figures 10B, 10C, and 10D). For GBM, ADRA2C expression level ranged from 0 to 7.689; for RB, the range was from 0 to 2.005; and for UVM, it was from 0 to 2.346. These findings suggested that ADRA2C could play a role in the progression of GBM, RB, and UVM.
ADRA2C expression level was correlated with migration of glioma cell lines
The wound healing assay was performed to explore the role of ADRA2C in glioma cell migration. Compared with the control group, in both GL261 and U87 cells, the migration speed increased in the group treated with the ADRA2C antagonist, phentolamine, while it decreased in the group treated with the ADRA2C agonist, norepinephrine (Figure 11), suggesting that a lower ADRA2C expression level may promote the GBM cell migration.
ADRA2C could play roles in glioma cell apoptosis and invasion
To further evaluate whether ADRA2C could be involved in the process of glioma tumorigenesis, the expression levels of Bax, Bcl-2, and MMP2 in tumor tissues of mouse glioma models were detected using IHC. The results indicated that compared with control group, Bcl2/Bax ratio and MMP2 expression level were elevated in phentolamine-treated group, while decreased in norepinephrine-treated group (Figure 12). This suggested that a lower ADRA2C expression level may promote the glioma cell apoptosis and invasion.
Discussion
Due to high mortality rates of diverse cancer types, the lack of effective biomarkers for diagnosis, prognosis, and treatment should be urgently eliminated. ADRA2C plays a notable role in modulating neurotransmitter release from sympathetic nerves and adrenergic neurons in the central nervous system. Recent research highlights its involvement in non-neoplastic conditions, such as schizophrenia [7], attention deficit hyperactivity disorder (ADHD), and heart failure. However, limited studies have explored its association with cancer development, including breast cancer [10, 17], glioma [20], and colorectal cancer [21]. There is a scarcity of systematic analyses elucidating the role of ADRA2C across different types of cancer. Pan-cancer analysis could be a comprehensive method to investigate the role of ADRA2C in various human cancer types.
Utilizing the TCGA data, GTEx RNA-seq data, and paired tumor and normal tissue data altogether, this pan-cancer analysis revealed that the mRNA expression level was higher in CHOL, COAD, HNSC, LIHC, READ, and THCA. Meanwhile, there was a
lower ADRA2C mRNA expression level in BLCA, BRCA, CESC, GBM, KICH, KIRC, KIRP, LUAD, PRAD, and UCEC. These results were mainly consistent with those reported previously [10, 21], as well as data from other databases, such as The Human Protein Atlas (HPA) database (https://www.protein atlas.org/). KM survival analysis confirmed that a high ADRA2C mRNA level was associated with a favorable prognosis in GBMLGG and UVM, while a high ADRA2C expression level was correlated with a poor prognosis in ACC, suggesting that ADRA2C may be a protective factor in GBMLGG and UVM patients, as well as being a risk factor for ACC. Remarkably, there was no relationship between ADRA2C expression level and the prognosis of breast cancer, which was inconsistent with previous studies [10, 22]. This phenomenon may have multifaceted causes, including variations in sample size, the characteristics of samples tested, and the methods of detection. Alternatively, ADRA2C emerged to be capable of discerning cancer patients from healthy subjects with notable sensitivity and specificity in CESC, CHOL, GBM, GBMLGG, KICH, KIRP.
A
CESC
B
CHOL
C
GBM
1.0
1.0
1.0
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.4
0.4
0.4
0.2
ADRA2C
0.2
ADRA2C
0.2
ADRA2C
AUC: 0.975
AUC: 0.978
AUC: 0.980
0.0
CI: 0.939-1.000
0.0
CI: 0.941-1.000
0.0
CI: 0.956-1.000
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
D
GBMLGG
E
KICH
F
KIRP
1.0
1.0
1.0
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.4
0.4
0.4
0.2
ADRA2C
0.2
ADRA2C
0.2
ADRA2C
AUC: 0.964
AUC: 0.994
AUC: 0.941
0.0
CI: 0.925-1.000
0.0
CI: 0.984-1.000
0.0
CI: 0.914-0.968
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
Cancer associated fibroblast_EPIC Cancer associated fibroblast_MCPCOUNTER
Common lymphoid progenitor_XCELL
Common myeloid progenitor_XCELL
A
Cancer associated fibroblast_XCELL Cancer associated fibroblast_TIDE
B
C
ACC (n=79)
ACC (n=79)
BLCA (n=408)
BLCA (n=408)
BRCA (n=1100)
ACC (n=79)
BRCA-Basal (n=191)
BLCA (n=408)
BRCA (n=1100)
BRCA (n=1100)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
2
BRCA-LumA (n=568)
BRCA-Her2 (n=82)
BRCA-LumA (n=568)
BRCA-LumB (n=219)
BRCA-LumA (n=568)
BRCA-LumB (n=219)
CESC (n=306)
BRCA-LumB (n=219)
CESC (n=306)
CHOL (n=36)
CESC (n=306)
CHOL (n=36)
COAD (n=458)
CHOL (n=36)
COAD (n=458)
DLBC (n=48)
COAD (n=458)
DLBC (n=48)
ESCA (n=185)
DLBC (n=48)
ESCA (n=185)
GBM (n=153)
ESCA (n=185)
GBM (n=153)
HNSC (n=522)
GBM (n=153)
HNSC (n=522)
HNSC-HPV- (n=422)
HNSC (n=522)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
KICH (n=66)
X
HNSC-HPV+ (n=98)
KICH (n=66)
P > 0.05
KIRC (n=533)
p > 0.05
KICH (n=66)
p > 0.05
KIRC (n=533)
KIRP (n=290)
p ≤ 0.05
KIRC (n=533)
KIRP (n=290)
p ≤ 0.05
LGG (n=516)
KIRP (n=290)
p ≤ 0.05
LGG (n=516)
LGG (n=516)
LIHC (n=371)
LIHC (n=371)
LIHC (n=371)
LUAD (n=515)
Partial_Cor
LUAD (n=515)
LUAD (n=515)
LUSC (n=501)
Partial_Cor
Partial_Cor
LUSC (n=501)
1
LUSC (n=501)
1
MESO (n=87)
1
MESO (n=87)
0
MESO (n=87)
OV (n=303)
O
OV (n=303)
OV (n=303)
0
PAAD (n=179)
-1
PAAD (n=179)
-1
PAAD (n=179)
-1
PCPG (n=181)
PCPG (n=181)
PCPG (n=181)
PRAD (n=498)
PRAD (n=498)
PRAD (n=498)
READ (n=166)
READ (n=166)
READ (n=166)
SARC (n=260)
SARC (n=260)
SARC (n=260)
SKCM (n=471)
SKCM (n=471)
SKCM (n=471)
SKCM-Metastasis (n=368)
SKCM-Metastasis (n=368)
SKCM-Metastasis (n=368)
SKCM-Primary (n=103)
SKCM-Primary (n=103)
SKCM-Primary (n=103)
STAD (n=415)
STAD (n=415)
STAD (n=415)
TGCT (n=150)
TGCT (n=150)
TGCT (n=150)
THCA (n=509)
THCA (n=509)
THCA (n=509)
THYM (n=120)
THYM (n=120)
THYM (n=120)
UCEC (n=545)
UCEC (n=545)
UCEC (n=545)
UCS (n=57)
UCS (n=57)
UCS (n=57)
UVM (n=80)
X
UVM (n=80)
UVM (n=80)
D
Endothelial cell_EPIC
Endothelial cell_MCPCOUNTER
Endothelial cell_XCELL
E
Hematopoietic stem cell_XCELL
F
MDSC_TIDE
ACC (n=79)
BLCA (n=408)
BRCA (n=1100)
BRCA-Basal (n=191)
ACC (n=79)
BRCA-Her2 (n=82)
X
ACC (n=79)
BLCA (n=408)
BLCA (n=408)
BRCA (n=1100)
BRCA-LumA (n=568)
BRCA (n=1100)
BRCA-Basal (n=191)
BRCA-LumB (n=219)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
CESC (n=306)
BRCA-Her2 (n=82)
BRCA-LumA (n=568)
CHOL (n=36)
X
BRCA-LumA (n=568)
BRCA-LumB (n=219)
COAD (n=458)
BRCA-LumB (n=219)
CESC (n=306)
DLBC (n=48)
CESC (n=306)
CHOL (n=36)
CHOL (n=36)
ESCA (n=185)
COAD (n=458)
GBM (n=153)
COAD (n=458)
DLBC (n=48)
DLBC (n=48)
HNSC (n=522)
ESCA (n=185)
ESCA (n=185)
HNSC-HPV- (n=422)
GBM (n=153)
GBM (n=153)
HNSC-HPV+ (n=98)
HNSC (n=522)
HNSC (n=522)
HNSC-HPV- (n=422)
KICH (n=66)
X
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
KIRG (n=533)
p > 0.05
HNSC-HPV+ (n=98)
KIRP (n=290)
p ≤ 0.05
KICH (n=66)
KICH (n=66)
KIRC (n=533)
p > 0.05
KIRC (n=533)
p > 0.05
LGG (n=516)
LIHC (n=371)
KIRP (n=290)
p ≤ 0.05
KIRP (n=290)
p ≤ 0.05
LGG (n=516)
LGG (n=516)
LUAD (n=515)
Partial_Cor 1
LIHC (n=371)
LIHC (n=371)
LUSC (n=501)
LUAD (n=515)
Partial_Cor
LUAD (n=515)
Partial_Cor
MESO (n=87)
0
LUSC (n=501)
1
LUSC (n=501)
1
OV (n=303)
-1
MESO (n=87)
MESO (n=87)
OV (n=303)
0
OV (n=303)
O
PAAD (n=179)
-1
PCPG (n=181)
PAAD (n=179)
-1
PAAD (n=179)
PCPG (n=181)
PRAD (n=498)
PCPG (n=181)
X
PRAD (n=498)
PRAD (n=498)
READ (n=166)
SARG (n=260)
X
READ (n=166)
READ (n=166)
SARC (n=260)
SARC (n=260)
SKCM (n=471)
SKCM (n=471)
SKCM (n=471)
SKCM-Metastasis (n=368)
SKCM-Metastasis (n=368)
SKCM-Metastasis (n=368)
SKCM-Primary (n=103)
X
SKCM-Primary (n=103)
SKCM-Primary (n=103)
STAD (n=415)
STAD (n=415)
STAD (n=415)
TGCT (n=150)
TGCT (n=150)
TGCT (n=150)
THICA (n=509)
THCA (n=509)
THCA (n=509)
THYM (n=120)
THYM (n=120)
THYM (n=120)
UCEC (n=545)
UCEC (n=545)
UCEC (n=545)
UCS (n=57)
UCS (n=57)
X
UCS (n=57)
X
UVM (n=80)
G
UVM (n=80)
XI
UVM (n=80)
aDC
**
*
**
* **
**
**
**
** **
*
**
**
**
**
**
**
B cells
**
**
**
**
**
**
**
**
*
**
**
** ** **
**
CD8 T cells
*
**
**
**
** **
**
**
**
*
**
**
*
**
**
Cytotoxic cells **
*
*
**
*
*
*
**
**
**
**
**
**
*
**
**
*
**
**
*
DC
**
**
**
* **
**
*
*
*
*
** **
**
**
*
p < 0.05
Eosinophils
* **
**
*
*
**
**
**
*
**
** **
*
**
iDC *
**
*
*
*
**
*
**
**
** **
* **
*
** p < 0.01
Macrophages
*
**
*
*
**
*
*
*
*
*
** *
**
**
**
** **
**
Mast cells
**
**
**
**
*
**
**
**
**
**
**
*
*
*
**
*
**
**
**
Neutrophils
Correlation
*
*
**
**
*
**
*
**
**
**
**
**
**
**
** **
**
*
**
NK CD56bright cells
*
**
**
**
**
**
*
**
**
** **
**
**
**
*
*
**
*
**
**
*
**
**
1.0
NK CD56dim cells
**
*
**
**
**
**
*
**
**
**
*
NK cells
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
** **
*
**
**
**
**
**
**
**
0.5
PDC
*
**
**
*
*
**
*
**
**
*
**
*
**
**
**
**
**
**
**
*
T cells
*
*
*
**
**
*
* **
*
*
**
**
**
**
**
*
**
* **
**
0.0
T helper cells **
**
**
*
**
**
**
*
**
**
*
**
**
**
**
**
** *
**
*
**
**
*
** * **
Tcm
**
**
**
*
**
**
**
**
*
**
**
**
**
*
*
* **
Tem
*
**
**
**
**
-0.5
*
*
*
TFH
*
**
*
**
**
**
**
**
* **
*
**
**
**
**
Tgd
**
**
**
*
**
**
*
**
** **
**
*
**
-1.0
Th1 cells
* **
**
*
**
*
**
**
*
** *
*
**
*
**
** **
**
**
**
Th17 cells
* **
*
**
**
**
**
*
**
**
**
*
**
Th2 cells
*
**
*
**
**
**
**
**
**
* **
**
**
**
**
**
**
**
**
**
*
TReg
**
ACC BLCA
**
**
*
**
**
**
*
**
*
**
**
** **
BRCA
CESC
CHOL
COAD
COADREAD
DLBC
ESAD
ESCA
ESCC
GBM
GBMLGG
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUADLUSC
LUSC
MESO
OSCC
OV
PAAD
PCPG
PRAD
READ
SARC SKCM
STAD
TGCT
THCA THYM
UCEC
UCS
UVM
A
B
C10orf547
CD27
ADORA2A
CD276
BTLA
CD287
CD40
CD40LG ]
CD160
CD48” CD70
CD244
CD80
CD274
CD86-
CXCL12
CD96
CXCR4-
ENTPD17
CSF1R
HHLA2
ICOS
CTLA4
ICOSLG
IL2RA
HAVCR2
IL6
IL6R
IDO1
KLRC1 ]
IL10
KLRK1
LTA ]
IL10RB
MICB
NTSET
KDR
PVR
RAET1ET
KIR2DL1
TMEM173”
TMIGD2]
KIR2DL3
TNFRSF13B
TNFRSF13CT
1
LAG3
TNFRSF14
TNFRSF17 ]
LGALS9
TNFRSF18
TNFRSF25 ]
PDCD1
TNFRSF4
TNFRSF8
PDCD1LG2
TNFRSF9”
TNFSF13
PVRL2
TNFSF13B”
TNFSF14”
TGFB1
TNFSF15 7
TNFSF187
TGFBR1
TNFSF4
TIGIT
TNFSF9
ULBP1
VTCN1
UVM
C
D
CCL1 ]
B2M
CCL2
CCL3”
HLA-A
CCL47
CCL5
HLA-B
CCL7 ]
CCL8
HLA-C
CCL11 ]
CCL13
HLA-DMA
CCL14”
CCL15 7
HLA-DMB
CCL16
CCL17]
HLA-DOA
CCL18
CCL19-
HLA-DOB
CCL20”
CCL21-
HLA-DPA1
CCL22
CCL23
HLA-DPB1
CCL24 ]
CCL25
HLA-DQA1
CCL26
CCL27 ]
HLA-DQA2
CCL28
CX3CL17
HLA-DQB1
CXCL17
CXCL2
HLA-DRA
CXCL3
CXCL5 ]
HLA-DRB1
CXCL6
CXCL8
HLA-E
CXCL9
CXCL107
HLA-F
CXCL117
CXCL12”
HLA-G
CXCL13”
CXCL147
CXCL16
TAP1
CXCL17
TAP2
XCL1
XCL2
TAPBP
0
MES
UVM
UVM
E
CCR1
CCR2
CCR3
CCR4
CCR5
CCR6
CCR7
CCR8
CCR9
CCR10
CXCR1
CXCR2
CXCR3
CXCR4
CXCR5
CXCR6
XCR1
CX3CR1
0
WVAr
A
B
GBM
Angiogenesis
**
**
geneExp
Apoptosis
**
CellCycle
Correlation
* p < 0.05
Pvalue
**
Invasion
-0.40
Differentiation
*
**
**
DNAdamage
p < 0.01
**
**
**
DNArepair
-0.36
DNArepair
Correlation
**
**
**
1.0
EMT
DNAdamage
-0.34
*** .
Hypoxia
0.5
Expression distribution with t-SNE plot
**
40
Inflammation
**
0.0
7.7
Invasion
**
**
-0.5
20
6.4
5.1
Metastasis
*
*
-1.0
0
tSNE2
3.8
Proliferation
2.6
-20
Quiescence
1.3
-40
0.0
Stemness
*
**
Expression
GBM
RB
UM
-60
-75
-50
-25
0
25
50
75
tSNE1
C
RB
D
UM
geneExp
Correlation
Pvalue
geneExp
Angiogenesis
0.67
…
Correlation
Pvalue
Differentiation
0.66
…
DNAdamage
-0.54
Inflammation
0.46
..
DNArepair
-0.54
DNArepair
-0.71
CellCycle
-0.49
…
Apoptosis
-0.40
*** , .
DNAdamage
-0.37
.. .
Expression distribution with t-SNE plot
Expression distribution with t-SNE plot
75
50
2.0
2.3
50
1.7
2.0
25
1.3
1.6
25
tSNE2
1.0
tSNE2
1.2
0
0.7
0.8
0
0.3
0.4
-25
-25
0.0
0.0
Expression
Expression
-50
-75
-50
-25
0
25
50
-50
-60
-40
-20
0
20
40
60
tSNE1
ISNE1
A
GL261
0h
50
migration rate %
40
30
20
24h
10
0
control group
NA 10UM
Phentolamine 10uM
48h
Control group
ΝΑ 10μΜ
Phentolamine 10UM
B
U87
0h
100
migration rate %
80
60
40
12h
20
0
control group
NA 2uM
phentolamine 0.005UM
24h
Control group
NA 2µM
Phentolamine 0.005UM
Control group
NA group
Phentolamine group
2.0-
bax
1.5-
bcl-2/bax
1.0-
50um
50um
50um
0.5-
0.0
control group
NA group
phentolamine group
Bcl-2
50um
50um
50um
80-
60
%Area
40-
MMP2
20-
0
50um
50um
50um
control group
NA group
phentolamine group
Recently, gene mutation-based therapy has become a significant concern in the field of cancer treatment [23, 24]. Previous studies have discovered that global DNA hypomethylation could be recognized as a common hallmark of tumor [25, 26]. Moreover, alterations in DNA methylation level in cancer have been considered as a promising diagnostic, prognostic, predictive and treatment biomarker [27]. According to the analysis of genetic alterations performed in the present study, different ADRA2C mutation types were included and alterations in DNA methylation level appeared in several cancer types, demonstrating that ADRA2C may participate in the tumorigenesis, especially in BLCA, BRCA, COAD, HNSC, KIRP, LUAD, LUSC, PAAD, and PRAD.
According to the GSEA and PPI network analysis of ADRA2C, it was found that it was mainly correlated with neuronal system-related pathways and several GPCR pathway-related proteins. Previous studies have demonstrated that the ADRA2C related proteins were associated with diver cancer types, such as breast cancer [28, 29], acute myeloid leukemia [30], lung cancer [31], and hepatocellular carcinoma [32]. This suggests that ADRA2C may play a role in several processes of tumorigenesis. Thus, single-cell
sequencing was conducted in the present study, and it was found that ADRA2C could play a role in angiogenesis, differentiation, DNA damage and repair, invasion, and metastasis in GBM, RB, and UM. To validate the prediction results by mentioned above, we constructed cell and mouse GBM model to investigate the roles of ADRA2C in cell migration, apoptosis, and invasion. According to KEGG database and previous findings, wound healing assay was applied to assess the key apoptosis-related markers, including Bax and Bcl-2, and invasion-related protein, MMP2, for validation the anti-cancer effect and mechanism of ADRA2C drugs in glioma [33-36]. It was demonstrated that a lower ADRA2C expression level could accelerate migration, apoptosis, and invasion, further promoting worse prognosis. This conclusion is consistent with that of the bioinformatics analysis as described above.
Over the last decade, researchers have confirmed that the tumor microenvironment (TME) is essential to tumor initiation, progression, metastasis, and immunotherapy [37]. On the one hand, infiltration of immune cells is crucial to tumorigenesis, prognosis, drug discovery, and the development of therapeutic strategies for tumors [38-40]. The present study revealed that ADRA2C was closely associated with
infiltration of multiple immune cells, such as B cells, CD4+T cells, CD8+T cells, NK cells, cytotoxic cells, etc. These findings may provide new insights for the application of ADRA2C in cancer immunotherapy, especially in ACC, BLCA, BRCA, OV, PAAD, STAD, TGCT, THYM, UVM, etc. On the other hand, investigations into immunoregulatory genes, such as CTLA-4, PD-1, PD-L1, and MHC molecules may provide novel insights into the discovery of immunotherapeutic agents for combating tumors [41]. The application of immune checkpoint inhibitors (ICI) has exhibited a notable efficacy in numerous cancer types, such as colon cancer, gastric cancer, non-small cell lung cancer, and clear cell renal carcinoma [42]. Hence, ADRA2C exhibits correlations with various immunostimulators, immunoinhibitors, MHC molecules, cytokines, and receptors. These correlations suggest that ADRA2C might serve as a co-factor in the function of immune checkpoint agents in tumor immunotherapy, especially in ACC, BLCA, COAD, HNSC, KICH, LGG, LUSC, OV, SARC, SKCM, STAD, and UVM. Besides, GPCRs represent the largest class of drug targets currently on the market [43, 44]. Consequently, ADRA2C may be a promising target for cancer immunotherapy.
There are some limitations of this study. Firstly, the majority of data were obtained from databases that undergo continuous updates, thereby imposing limitations on the conclusiveness of the findings. Secondly, validation of the role of ADRA2C in the GBM model was primarily based on preliminary experiments. Further research is necessary to authenticate the precision of the outcomes of bioinformatics analysis and to explore the molecular mechanisms involving ADRA2C in GBM and other cancer types. Lastly, the clinical applications of ADRA2C-related products remain elusive and warrant confirmation through more comprehensive in vivo and in vitro experiments.
Conclusions
In conclusion, the role of ADRA2C in pan-cancer was systematically evaluated through multiple bioinformatics methods and preliminary experiments. Lower ADRA2C expression level is correlated with GBM patients’ poor prognosis. ADRA2C is involved in various processes of tumorigenesis and could serve as a notable target for cancer diagnosis and immunotherapy.
Abbreviations
ADRA2C: a2C-adrenergic receptor; TCGA: The Cancer Genome Atlas; BRCA: breast invasive carcinoma; ESCA: esophageal adenocarcinoma; KIRP: kidney renal papillary cell carcinoma; LUSC: lung
squamous cell carcinoma; ACC: adrenocortical carcinoma; ESCC: esophageal squamous cell carci- noma; GBM: glioblastoma multiforme; GBM-LGG: glioblastoma multiforme and lower grade glioma; THYM: thymoma; UVM: uveal melanoma; RB: retinoblastoma; GPCRs: G protein-coupled receptors; AUC: Area Under the Curve; ROC: Receiver Operating Characteristic; GBM: Glioblastoma Multiforme; LGG: Low-Grade Glioma; OS: overall survival; DSS: disease-specific survival; PFI: progression-free interval; IHC: Immunohisto- chemistry; CNA: copy number alteration; GSEA: Gene set enrichment analysis; NA: noradrenaline; CHOL: cholangiocarcinoma; DLBC: diffuse large B-cell lymphoma; HNSC: head and neck squamous cell carcinoma; LIHC: liver hepatocellular carcinoma; OV: ovarian carcinoma; PAAD: pancreatic adenocarcinoma; STAD: stomach adenocarcinoma; TGCT: testicular germ cell tumors; THCA: thyroid carcinoma; THYM: thymoma; BLCA: bladder urothelial carcinoma; CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD: colon adenocarcinoma; KICH: kidney chromophobe; KIRC: kidney renal clear cell carcinoma; AML: acute myeloid leukemia; LUAD: lung adenocarcinoma; PCPG: pheochromocytoma and paraganglioma; PRAD: prostate adenocarcinoma; READ: rectum adenocarcinoma; SKCM: skin cutaneous melanoma; UCEC: uterine corpus endometrial carcinoma; UCS: uterine carcinosarcoma; ESAD: esophageal adenocarcinoma; MESO: mesothelioma; OSCC: oral squamous cell carcinoma.
Acknowledgments
We would like to thank Qiang Fu, Jinhu Zhang and Hui Wang for their instrument technical supports who are members of Pub-Lab, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University. We would like to thank Pub-Lab, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University for providing valuable instruments.
Funding
This work was supported by the Sichuan Science and Technology Program (Grant No. 2022NSF SC0793), Opening Fund of NHC Key Laboratory of Chronobiology (Sichuan University)) (Grant No. NHCC-2023-04) and the Hebei Provincial Department of Finance and Hebei Provincial Health Commission (Grant No. ZF2023242).
Author contributions
Xiaoxiao Zhang contributed to design the study, all animal work and experiments, data analysis, and
wrote this paper; Huitong Chen, Chenyang Wang, Chan Chen and Liyan Liu participated in cell and animal experiments. Shuangfa Nie, Xiang Gao, Junli Chen and Ning Huang directed the project and revised the manuscript. All authors approved the final manuscript.
Data availability
The relevant original datasets generated during and/or analyzed during the current study are available from the first or corresponding author on reasonable request.
Ethics approval
Ethics approval was obtained from the Medical Ethics Committee of Sichuan University.
Competing Interests
The authors have declared that no competing interest exists.
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