Unveiling PAK4 as a Key Biomarker in Adrenocortical Carcinoma: Insights from Bioinformatics and Experimental Evidence
Qiancheng Mao1,1, Ming Liu1,1, Xidong Wang1, Hongquan Liu1, Weiyi Chen2, Shangjing Liu1, Guixin Ding1, Yuanshan Cui1,*, Jitao Wu1,*
1 Department of Urology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, 264000 Yantai, Shandong, China
2 Department of Pathology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, 264000 Yantai, Shandong, China
*Correspondence: yhdcuiyuanshan@163.com (Yuanshan Cui); wjturology@163.com (Jitao Wu)
+ These authors contributed equally. Published: 28 November 2025
Background: Adrenocortical carcinoma (ACC) is a rare and fatal adrenal cortex cancer with a poor prognosis and high mortality rate. Although surgical resection is the primary treatment for ACC, recurrence is still common. p21-activated kinase 4 (PAK4) is linked to tumour development and progression, being overexpressed in various cancers. However, the role of PAK4 in ACC remains unclear.
Methods: In this study, PAK4 expression in ACC was analysed using sequencing data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, assessing its clinical relevance with Kaplan-Meier, Cox regression, receiver operating characteristic (ROC) curve and prognostic nomogram models. Functional enrichment of PAK4-related genes was explored using protein-protein interaction (PPI) networks, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA). The association between PAK4 messenger RNA (mRNA) expression and immune infiltration was examined via Tumor Immune System Interaction Database (TISIDB). Finally, immunohistochemistry was used for tissue validation.
Results: In the GEO and TCGA databases, PAK4 expression was significantly higher in ACC tissues than in normal samples (p < 0.05). High PAK4 levels were associated with poor prognosis, including shorter overall survival, disease-specific survival and progression-free interval (p < 0.05). Elevated PAK4 expression correlated with advanced T, N and M stages (p < 0.05), indicating increased malignancy in ACC. A PPI network predicted associations between PAK4 and its targets, whereas GSEA linked PAK4 to the Hedgehog signalling pathway and cell proliferation (p < 0.05). The upregulation of PAK4 was also connected to immune regulation and tumour-infiltrating immune cells such as T cells, B cells and mast cells (p < 0.05). Immunohistochemistry confirmed high PAK4 expression in ACC (p < 0.001).
Conclusions: PAK4 is significantly overexpressed in ACC, and it may play a carcinogenic role, showing great application potential as a potential therapeutic target and an independent prognostic biomarker of ACC.
Keywords: adrenocortical carcinoma; PAK4; prognostic; immune infiltration; biomarkers
Introduction
Adrenocortical carcinoma (ACC) is a rare adrenal ma- lignancy with an incidence of 1-2 cases per million annu- ally [1,2]. Despite its rarity, ACC has severe symptoms, a poor prognosis, and high mortality [3]. Most patients are di- agnosed at advanced stages, resulting in a five-year survival rate below 15% [4]. ACC is highly invasive and hetero- geneous, causing diverse clinical manifestations. Most pa- tients show signs of excessive adrenocortical hormones [5]. Surgery is the primary treatment, but recurrence is common even after complete tumor removal.
p21-activated kinase 4 (PAK4), a serine/threonine p21-activated kinase family member and key Cdc42/Rac
effector, is linked to tumorigenesis and crucial in signal- ing pathways [6,7]. The PAK family, comprising six pro- teins divided into Group I (PAK1-3) and Group II (PAK4- 6) based on structural and sequence differences [8], has dis- tinct cellular functions. PAK4, a 591-amino acid protein and commonly studied PAK member [9], regulates cell prolif- eration, migration, cytoskeletal organization, survival, mor- phology, and the cell cycle [10,11]. Studies show that PAK4 is overexpressed in various tumors, with its dysregulation being a key factor in cancer progression.
As PAK4 drives tumour progression, its potential as a diagnostic and therapeutic target is gaining attention, with preclinical studies showing promise. PAK4 inhibitors such as LCH-7749944, PF3758309 and KPT-9274/7189 can sup-
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press gastric cancer cell proliferation, lung cancer metas- tasis and pancreatic ductal adenocarcinoma, respectively [12-14]. However, the role of PAK4 in ACC remains un- known. Thus, this study aimed to explore PAK4’s mech- anistic contribution to ACC to validate its diagnostic and therapeutic potential.
Materials and Methods
Data Acquisition
Using the UCSC Xena platform (https://xena.ucsc.ed u/), the information from 79 ACC tumour samples ob- tained from The Cancer Genome Atlas (TCGA) and 128 normal tissue samples obtained from GTEx (http://comm onfund.nih.gov/GTEx) was compared. In addition, expres- sion validation was performed using the datasets GSE90713 and GSE10927 from the Gene Expression Omnibus (GEO) database. Detailed clinical information of the ACC samples was obtained from TCGA.
Construction of a Protein-Protein Interaction (PPI) Network
STRING (http://string-db.org) is a powerful platform dedicated to constructing protein networks. In this study, STRING was utilised to build the PAK4 PPI network, with data sources such as “Textmining”, “Experiments”, “Databases”, “Co-expression”, “Neighborhood”, “Gene Fusion” and “Co-occurrence”. The minimum required in- teraction score was set to medium confidence (0.400), and the maximum number of interactors to show was limited to no more than 10. GeneMANIA (https://genemania.org) was also leveraged to construct an interaction network for the PAK4 gene. This online tool enables us to analyse gene interactions by simply inputting the PAK4 gene.
Exploration of Enrichment Pathways and Functional Mechanisms
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses as well as gene set enrichment analysis (GSEA) of PAK4 gene expression were conducted using clusterProfiler soft- ware (version 3.6.3, an open-source R package distributed through the BioConductor project (https://bioconductor.o rg) which is maintained by the BioConductor Team and Fred Hutchinson Cancer Research Center, Seattle, WA, USA) in R. The samples were divided into high- and low- PAK4-expression groups based on expression levels, with the top 50% as the high-expression group and the bot- tom 50% as the low-expression group. Differentially ex- pressed genes (|logFC| >2, padj <0.01) were selected be- tween these groups for analysis. Using the GSEA func- tion in clusterProfiler, GSEA was performed on the basis of the hallmark and KEGG metabolic gene sets, calculating the normalised enrichment score for each gene set and con- ducting significance and multiple-hypothesis testing. These
tests assessed the enrichment degree of gene sets in the two groups, thereby contributing to the identification of sig- nificantly different biological processes or metabolic path- ways.
Immune Infiltration
Tumor Immune System Interaction Database (TISIDB) (http://cis.hku.hk/TISIDB/) and Timer (https://cistrome.shinyapps.io/timer/) are online platforms for systematically analysing tumour-infiltrating immune cells, integrating data from several tumour samples to analyse immune cell content and distribution. GEPIA (http://gepia.cancer-pku.cn/index.html) is a similar tool for analysing gene expression patterns in tumours, exploring gene correlations via large-scale public gene expression data. In this study, TISIDB, Timer and GEPIA were used to explore the relationship of PAK4 with immune-related molecules and cells in ACC. Spearman correlation analysis was also used to compare immune cell infiltration levels between PAK4-high and low-expression subgroups and assess the correlation between PAK4 expression and different immune cell infiltration concentrations.
Immunohistochemical (IHC) Analysis
Human ACC tissues were processed into paraffin- embedded specimens, which were then dewaxed, washed and subjected to antigen retrieval. The tissues were in- cubated overnight with a PAK4 antibody (1:100, Sangon, Shanghai, China) at 4 ℃, followed by incubation with a bi- otinylated secondary antibody. Subsequently, diaminoben- zidine staining was performed for colour development. Af- ter dehydration, the slides were mounted, and microscopic images were acquired. The IHC staining results were inde- pendently assessed by two pathologists.
Statistical Analysis
Bioinformatics data were analysed using R (v4.2.1, R Core Team and the R Foundation for Statistical Computing, Vienna, Austria). Comparisons between two groups were performed using Student’s t-test for normally distributed variables or the Wilcoxon rank sum test for non-normal or heterogeneous distributions. Associations between PAK4 and clinical characteristics were evaluated via Chi-squared test with Yates’ correction, which assesses categorical vari- able relationships by comparing observed and expected fre- quencies. PAK4 expression correlations were analysed us- ing Spearman’s rank correlation. Results with p < 0.05 were considered statistically significant.
Results
Expression Landscape and Expression Pattern of PAK4 in Pan-Cancer Perspective
PAK4 is associated with multiple organs, tissues, cells and diseases. Using the Open Target Platform, the involve-
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prostate carcinoma
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neoplasm
prostate cancer
lung enocarcinor
Familial prostate cancer
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nervous system disease
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prostate cancer
system
Familial prostate cancer
Lor thora
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prostate carcinoma
lung enocarcinor
genetic disorder
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10
12
14
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2
1
0
B
10
12
14
B
Bone marrow Bone
C
Connective Tissue
STACYSAL CIELL
Lung
Adipose tissue
Esophagus
Intestine
Lymphocyte
Digestive System
Liver
HEPATIC STELLATE CELL
Breast/Mammary
Pancreas
Stomach
Granulocytic
GHETINIC EPITHELIAL CELL
H
Fibroblast
H
Lymphoid
Prostate
LACAP
BU143
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Immune System
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Myeloid
Other
Spleen
Thymus
Kidney
ALPHA TREI 298
Integumentary System Skin
Blood
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Skeletal muscle
WHELANOCYTE
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SARRAART SECICHOTTM ROLESCILE
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Lymphoid
Colon
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Nervous System
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ment of PAK4 in various systemic diseases was explored (Fig. 1A). In addition, PAK4 expression was observed in tissues and cells such as liver, lung and bladder cancer cell lines (Fig. 1B,C). To further investigate PAK4 expres- sion across different cancer types, TCGA pan-cancer data were analysed against healthy tissues (Fig. 2A). PAK4 was highly overexpressed in 22 out of 33 cancer types, includ- ing ACC, but downregulated in six cancer types (p < 0.05). This trend indicates abnormal PAK4 expression in many tumours. In particular, PAK4 expression in ACC tissues was significantly higher than that in normal adrenal tissues,
as confirmed by the GTEx + TCGA and external datasets GSE90713 and GSE10927 (p < 0.05, Fig. 2B-D). These findings indicate the potential application of PAK4 as a po- tential biomarker of ACC.
Correlation between PAK4 Expression and Clinical-Pathological Features of ACC
Subgroup analysis was conducted on the relation- ship between the expression of PAK4 and various clinical- pathological features of ACC. In patients with ACC, high PAK4 expression correlates with advanced T, N and M
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10
*
1
The expression of PAK4 Log2 (TPM+1)
*
8
3
6
S Normal
₿ Tumor
4
2
0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
B
C
D
GSE90713
GSE10927
6
The expression of PAK4 Log2 (TPM+1)
11.5
3.5
11.0
4
10.5
3.0
2
10.0
2.5
9.5
0
Normal
Tumor
normal
tumor
normal
tumor
stages, as well as high pathological stages (p < 0.05, Fig. 3A-C,E). Moreover, patients with additional tumours or venous invasion show elevated PAK4 expression (p < 0.001, Fig. 3D,F). This result indicates a significantly pos- itive correlation between PAK4 expression and ACC ma- lignancy. The association between PAK4 expression and clinical-pathological characteristics in patients with ACC is presented in Table 1. The total number of patients was 79; However, some information regarding pathologic T stage, pathologic N stage, clinical M stage, pathologic stage and primary therapy outcome was missing for certain patients.
Correlation between PAK4 Expression and ACC Diagnosis and Prognosis
Receiver operating characteristic (ROC) analysis was conducted to evaluate the ability of PAK4 to distinguish between ACC and normal adrenal tissue. The area un- der the curve (AUC) for PAK4 was 0.821 (Fig. 4A). The AUCs for 1-, 5- and 10-year overall survival (OS) were 0.902, 0.747 and 0.676, respectively (Fig. 4B). Patients were stratified into low-expression and high-expression groups based on PAK4 expression levels. Compared with the low-expression group, patients in the high-expression
group exhibited significantly shorter OS, disease-specific survival and progression-free interval (p < 0.01, Fig. 4C- E). By integrating the TNM stage and PAK4 expression lev- els, a nomogram model was constructed to predict the 2-, 3- and 5-year survival in ACC (Fig. 4F), with a calibra- tion plot confirming its accuracy (Fig. 4G). Survival prob- ability was significantly correlated with PAK4 expression, indicating that PAK4 may serve as a potential diagnostic and prognostic marker for ACC. In addition, a Gene Ex- pression database of Normal and Tumor tissues 2 (GENT2) database-based meta-analysis of the impact of PAK4 on OS of patients with ACC showed most datasets with a hazard ratio of >1, highlighting the prognostic value of PAK4 for ACC outcomes (Fig. 4H).
Identification of Genes Corelated with PAK4 in ACC
To identify key cancer-related protein interactions, we used the STRING database to construct a PAK4-related PPI network, identifying 10 highly correlated interacting genes (Fig. 5A). Further analysis via the GeneMANIA database revealed interactions between PAK4 and 20 can- didate target genes (Fig. 5B). KEGG enrichment analy- sis indicated that PAK4 expression is associated with var-
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B
C
**
*
6
6
6
The expression of PAK4 Log2 (TPM+1)
The expression of PAK4 Log2 (TPM+1)
The expression of PAK4
5
5
Log2 (TPM+1)
5
A
4
4
3
3
3
T
T1&T2
T3&T4
NO
NI
MO
MI
Pathologic T stage
Pathologic N stage
Clinical M stage
D
E
F
MẶC VỊ SE
6
6
6
The expression of PAK4 Log2 (TPM+1)
The expression of PAK4 Log2 (TPM+1)
The expression of PAK4
5
Log2 (TPM+1)
4
5
4
4
4
3
3
3
T
Tumor free
With tumor
Stage I&Stage II
Stage III&Stage IV
Absent
Present
Tumor status
Pathologic stage
Weiss-Venous invasion
A
B
C
D
E
1.0
1.0
1.0
PAK4
1.0
PAK4
1.0
PAK4
Low
Low
0.8
0.8
High
High
Low
High
Sensitivity (TPR)
Sensitivity (TPR)
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.6
0.4
0.4
0.6
0.6
0.2
2
PAK4
0.2
PAK4
1-year (AUC -0.902)
Overall Survival HR = 3.34 (1.48
Disease
Specific
Survival
0.4
AUC: 0.821
Progress HR = 3.63
Free Interval
0.4
7.54)
0.4
HR - 3.47 (1.47-
8.15)
(1.84 - 7.14)
0.0
CI: 0.747-0.896
5-year (AUC-0.747)
0.0
10-year (AUC - 0.676)
= 0.004
P=0.004
< 0.001
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
50
100
150
0
50
100
150
100
1-Specificity (FPR)
1-Specificity (FPR)
Time (months)
Time (months)
0
50
150
Time (months)
F
G
H
Points
₡
20
40
60
80
100
TJ&T4
1.0
Pathologic T stage
T1ST2
Observed fraction survival probability
Study
TE seTE
Hazard Ratio
HR
95%-CI
Weight (fixed)
Weight (random)
Pathologic N stage
NI
0.8
NO
Clinical M stage
M1
GSE10927-GPL570(215326_at)
4.53 3.0749
92.60
GSE33371-GPL570(215326_at)
4.46 3.0928
[0.22;
38371.17]
2.8%
2.8%
MO
.8
GSE33371-GPL570(33814_at)
1.71 1.4897
86.24
[0.20; 37013.02]
2.8%
2.8%
PAK4
High
LOW
GSE10927-GPL570(33814_at)
GSE33371-GPL570(203154_s_at)
1.57 1.4740
5.52
[0.30; 102.28)
12.1%
12.1%
4.81
[0.27;
86.44]
12.4%
12.4%
Total Points
0.4
1.20 0.8858
3.31
[0.58;
18.80]
34.2%
34.2%
40
80
120
180
GSE10927-GPL570(203154_s_at)
1.15 0.8675
3.15
[0.57;
17.23]
35.7%
35.7%
Linear Predictor
1.5
-0.5
0.5
1.5
25
0.2
2-year Survival Probability
2-year
Fixed effect model
4.36 [1.58;
12.05]
100.0%
-
0.9
100.0%
0.8
0.7
0.6 0.5 0.4 0.3
3-year 5-year
Random effects model
O
Heterogeneity: /2 = 0%, +2 = 0, p = 0.82
4.36 [1.58;
12.05]
-
3-year Survival Probability
0.0
Ideal line
0.9
0.7 0.6 0.5 0.4 0.3 02
0.0
0.2
0.4
0.6
0.8
1.0
0.001
0.1 1 10
1000
5-year Survival Probability
Nomogram predicted survival probability
0.8
0.7 0.6 0.5 0.4 0.3 0.2 0.1
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A
B
D
RAC3
LARK1
MMPZ
8
RAF1
FGF1
-
RAC2
INKA1
INKA1
ARHGEE
3
INKA2
-20
22
Category
PAK4.
-
ARHGEFG
PAKA
BSH
8
CDC42
UMK1
-2.3
-
LIMK1
CHOU
LIMK2
9
2.1
NES
C
E
Pathway
-log10(pvalue)
NEB
Level 3 of KEGG functional Category
Level 2 of KEOG funcional Category
Level 1 of KEGG functional Canegory
R604110
2.13
Cell cycle
8004114
Oocyle meiosis
Cell growth and death
2004115
1.46
1.41
p53 signaling pathway
4004210
-1.68
8004810
1.67
=1.33
Regulation of actin cytoskeleton
Cell motility
Cellular Processes
4004510
-1.34
Focal adhesion
Celular community - eukaryotes
8004144
1.47
4004142
endocytosis
Lysosome
Transport and catabolism
0004340
2
1.73
6504020
Hedgehog signaling pathway
Calcium signaling pathway
8004630
-1.32
0004070
5.09
-1.85
JAK-STAT signaling pathway
Signal transduction
6004518
1,62
Phosphaliddingsitel signaling system
Environmental Information Processing
643
8504060
Cell adhesion molecules
-2.62
Neuroactive ligand-receptor interaction
Signaling molecules and interaction
8004080
1.32
-1.28
ther Types Of O-dlycan Biosynthesis
2:00
1.61
Base excision repair
4.01
DNA replication
Homologous recombination
Replication and repair
leomyan Kanamydin And Granicin Biosynthesis
NO03430
1.60
Mismatch repair
Genetic Information Processing
ko03420
212
Nucleoside excision repair
NO03040
Spiccosome
Transcription
2003010
2.18
Ribosome
Translation
4005200
1.32 216
-1.28
Pathways in cancer
Cancer: overview
3005217
1.62
0505221 8005218
Basal cell carcinoma
1.83
1.50
-1.49
Acute myeloid leukemia
6005215
-147
Melanoma
Cancer: specific types
-1.47
8005416
Prostate cancer
44
Viral myocarditis
Cardiovascular diusados
8004950
1.58
Maturity onset diabetes of the young
6:15
Type 1 diabetes mellitus proglas rejection
Endocrine and metabolic disease
Human Diseases
NES
-
-2.34
Asthma
10-
Autoimmune thyroid diusare
Immune disease
4005332
8.85
Graft-versus-host disease
8005340
5.05
2.00
=1.96
Bacterial invasion of epithelial cells
Infectious disease: bacterial
2009140
9.95
2
Leishmaniasis
Infectious disease: parasitic
-log 10(p.adjust)
4000340
177
Histidine metabolism
4000360
137
Phenylalanine metabolism
Amino acid metabolism
6000350
8000620
2.87
+1.76
Tyrosine metabolism
0000010
Amino sugar and nucleotide sugar metabolism
Glycolysis / Gluconeogenesis
8000040
Pentose and glucuronate interconversions
Carbohydrate metabolism
8500500
281
.
Starch and sucrose metabolism
-166
Nitrogen metabolism
Energy metabolism
1.72
Glycan biosynthesis and metabolism
4000100
43
2.08
Steroid biosynthese
216
-1.50
Metabolism
8000691
=1.54
Linoleic acid metabolism
Lipid metabolism
8000140
1,60
-1.48
1.58
pallone biosynthesis
Folate biosynthesis
-1.06
Porphyrin and chlorophyll metabolism
Retinol metabolism
Metabolism of cofactors and vitamins
000830
4.31
-19
4000900
0000982
Terpenoid backbone biosynthesis
Metabolism of terpenolds and polyketides
2.14
Drug metabolism = cytochrome P450
8000983
371
Drug metabolism - other enzymes
0000980
Xenoblatics biodegradation and metabolism
5.74
2.05
1004950
Metabolism of minobiotics by cytochrome P450
Aldosterone-regulated sodium reabsorption
8004612
8.62
2.24
Antigen processing and presentation
Excretory system
0
-1.78
B cell receptor signaling partey
8004810
6.21
3.61
Complement and coagulation cascades
Grill par sensing panway
1.99
-1.47
8004840
80
+2.4
FG gamma R-mediated phagocytosis
Hemalopontic call lineage
Organismal Systems
4004672
-2.18
Intestinal immune network for ig& production
Immune system
4004670
3.18
Leukocyte transaendothelial migration
4004621
NOD-like receptor signaling pathway
0004650
AD04622
80
2.24
-1.44
Natural killer cell mediated cytotoxicity
8004660
RIG-I-like receptor signaling pathway
637
2.12
T cell receptor signaling pathway
8004620
-
-2.08
Toll-like receptor signaling pathway
RD04721
4.35
Synaptic vesicle cycle
Nervous system
0.0 25 50 7.5100
2-1 6 1 2
OSE143383
GSE 19775
GSE90713-
TCGA_ACC
F
Angiogenesis
Apoptosis
Cell Cycle
Differentiation
DNA damage
DNA repair
EMT
4
2
Oncogenic combined z-scores (zscore)
0
-2
-4
R =- 0.082, p = 0.48
R =- 0.25, p = 0.028
R=0.36, p = 0.0012
R =- 0.3, p = 0.0067
R=0.3, p = 0.0075
R = 0.42, p = 0.00012
R =- 0.085, p = 0.45
Hypoxia
Inflammation
Invasion
Metastasis
Proliferation
Quiescence
Stemness
4
2
0
-2
-4
R =- 0.1, p=0.38
R =- 0.41, p =0.00015
R=0.0019, p=0.99
R =- 0.25, p = 0.027
R=0.01, p=0.93
R =- 0.39, p = 0.00033
R =- 0.1, p=0.36
-2
0
2
-2
0
2
-2
0
2
-2
0
2
-2
0
2
-2
0
2
-2
0
2
PAK4 (zscore)
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[KEGG] DNA Replication
B
[KEGG] Hedgehog Signaling Pathway
C
[KEGG] P53 Signaling Pathway
NES = 2.584
NES =2.349
NES = 1.955
0.6
P.adj < 0.001
P.adj < 0.001
0.4
P.adj = 0.002
Enrichment Score
FDR < 0.001
Enrichment Score
0.4
FDR < 0.001
Enrichment Score
FDR = 0.001
0.3
0.4
0.2
0.2
0.2
0.1
0.0
0.0
0.0
-0.1
Ranked list metric
6
Ranked list metric
6
Ranked list metric
6
3
3
3
0
0
0
-3
-3
-3
-6
-6
-6
0
10000
20000
30000
0
10000
20000
30000
0
10000
20000
30000
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
D
[KEGG] Wnt Signaling Pathway
E
[KEGG] Tgf Beta Signaling Pathway
1
[KEGG] Cell Cycle
0.4
0.3
NES = 1.631
P.adj = 0.004
NES = 1.708
FDR = 0.002
P.adj = 0.004
NES = 2.602
P.adj < 0.001
Enrichment Score
FDR = 0.003
Enrichment Score
FDR < 0.001
0.2
Enrichment Score
0.3
0.4
0.1
0.2
0.0
0.2
0.1
-0.1
0.0
0.0
Ranked list metric
6
Ranked list metric
6
Ranked list metric
6
3
3
3
0
0
0
-3
C
-3
-6
-6
-6
0
10000
20000
30000
0
10000
20000
30000
0
10000
20000
30000
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
[GO:BP] Attachment of Spindle Microtubules
[GO:BP] Mitotic Cell Cycle Checkpoint Signaling
IGO:BP] DNA Replication Initiation
G
To Kinctochore
エ
1.6
NES =2.822
NES =2.780
NES - 2.829
P.adj < 0.001
P.adj < 0.001
P.udj < 0.001
FOR < 0.001
0.6
FDR < 0.001
Enrichment Score
0.6
FDR < 0.001
Enrichment Score
Enrichment Score
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
Ranked list metric
6
Ranked list metric
6
Ranked list metric
6
3
3
3
0
0
0
-3
-3
-3
-6
-6
-6
0
10000
20000
30000
0
10000
20000
30000
0
10000
20000
30000
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Datasel
[GO:BP| Recombinational Repair
K
[GO:MF] DNA Helicase Activity
[GO:BP] Regulation of Chromosome Segregation
NES =2.713
0.6
NES =2.440
L
P.udj < 0.001
Padj < 0.001
0.6
NES =2.885
P.adj < 0.001
Enrichment Score
FDR < 0.001
FOR < 0.001
FDR < 0.001
0.4
Enrichment Score
0.4
Enrichment Score
0.4
0.2
0.2
0.2
0.0
0.0
0.0
Ranked list metric
6
Ranked list metric
6
Ranked list metric
6
3
3
0
0
0
-3
-3
-3
-6
-6
-6
0
10000
20000
30000
0
10000
20000
30000
0
10000
20000
30000
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Datasel
ARCHIVOS ESPAÑOLES DE UROLOGÍA
A
PAK4
Th2 cells
R = 0.427 ***
Tgd
R = 0.055”15
Eosinophils
R =- 0.10718
·
0.8
·
TReg
R =- 0.12618
aDC
R =- 0.197115
Enrichment of B cells
Enrichment of Cytotoxic cells
0.7
Enrichment of Mast cells
0.6
0,3
Tem
R = - 0.2018
0.6
0.5
NK cells
R = - 0.293 **
T helper cells
R = - 0.321”
P value
0.2
0.5
Tem
R = - 0.341
0.6
·
88
0.4
pDC
R = - 0.350 **
0.4
0.3
iDC
R = - 0.411
0.1
-0.526
0.3
Spearman
man
R - - 0.627
CD8 T cells
-@0.502
R = - 0.425
0.2
·
P @ 0.001
<0.001
0.2
P = 0.001
DC
R = - 0.441
3
4
5
6
5
6
4
5
6
Thl cells
R = - 0.446”
|Cor|
The expression of PAK4 Log2 (TPM+1)
The expression of PAK4 Log2 (TPM+1)
The
expression of PAK4 Log2 (TPM+1)
Th17 cells
R = - 0.448
NK CD56dim cells
0.2
0.8
0.7
Enrichment of NK CD56bright cells
0.7
R = - 0.452
Neutrophils
0.4
..
R = - 0.463 ***
0.6
Enrichment of Macrophages
Enrichment of Neutrophils
Mast cells
R = - 0.502
0.6
0.7
Macrophages
R = - 0.515
0.6
B cells
R = - 0.526 ***
0.5
TFH
R =- 0.526” **
0.6
T cells
R = - 0.538
.
0.4
0.5
NK CD56bright cells
0.5
rman
Spearma
Cytotoxic cells
R = - 0.595
R = - 0.627
0.3
= - 0.595
%
P= 0.001
P
<0.001
0.4
B & 0.001
-0.50
-0.25
0.00
0.25
3
4
5
6
3
4
5
6
3
4
5
6
Correlation
The expression of PAK4 LOS2 (TPM+1)
The expression of PAK4 LOS2 (TPM+1)
The expression of PAK4 LOS2 (TPM+1)
Enrichment of NK CD56dim cells
0.5
0.60
0.6
0.6
0.55
.
Enrichment of T cells
0.4
Enrichment of TFH
Enrichment of The cells
Enrichment of Th17 cells
0.55
0.50
P
0.4
0.4
0.50
0.3
.
0.45
0.45
0.2
0,2
0.2
R =- 0.452
0.538
0.40
R = - 0.526
P< 0.001
0.40
0.1
P2 0.001
R = 0.427
0.001
.
P < 0.001
20.001
3
5
6
3
4
5
6
3
4
5
6
3
4
3
S
3
4
$
6
B
The expression of PAK4 Log2 (TPM+1)
The expression of PAK4 Log2 (TPM+1)
The expression of PAK4 Log2 (TPM+1)
The expression of PAK4 Log2 (TPM+1)
The expression of PAK4 Log2 (TPM+1)
**
1.0
Enrichment score
0.8
*
**
**
PAK4
0.6
Low
High
0.4
O
0.2
aDC
B cells
CD8 T cells
Cytotoxic cells
Eosinophils
iDC
Macrophages
Mast cells
Neutrophils
NK CD56bright cells
NK CD56dim cells
NK cells
PDC
T cells
T helper cells
Tcm
Tem
TFH
Tgd
Th1 cells
Th17 cells
Th2 cells
DC
TReg
ious functions, particularly the cell cycle, DNA replication and steroid biosynthesis (Fig. 5C). Fig. 5D shows poten- tial PAK4-related pathways from GSEA analysis. Fig. 5E presents integrated analysis results from multiple databases, highlighting strong correlations with oxidative phosphory- lation, myc target V1 and G2m checkpoint. Finally, analy- sis of the correlation between PAK4 expression and 14 tu- mor signature functional states showed that DNA repair has the most significant positive correlation, while inflamma- tion has the most significant negative correlation (p < 0.01, Fig. 5F).
Gene Sets Enriched in the PAK4 Expression Phenotype
GSEA identified pathway alterations between the high and low-PAK4-expression groups. As illustrated in Fig. 6, with regard to KEGG pathways, the cell cycle, DNA repli- cation, Hedgehog signalling pathway, p53 signalling path- way and transforming growth factor-beta (TGF-3) sig- nalling pathway are significantly upregulated in the high-
PAK4-expression group (p < 0.01). Meanwhile, with re- gard to GO terms, the regulation of chromosome segre- gation, the attachment of spindle microtubules to kineto- chore, mitotic cell cycle checkpoint signalling, DNA repli- cation initiation, recombinational repair and DNA helicase activity are all significantly upregulated in this group (p < 0.001).
Correlation between PAK4 Expression and ACC Immune Infiltration
In this study, the correlation of PAK4 expression levels with the infiltration of 24 immune cell types was analysed. PAK4 expression positively correlated with T helper cell type 2 (Th2) cells and negatively correlated with cytotoxic, natural killer (NK) and T cells (p < 0.01, Fig. 7A). Fur- ther research showed significant PAK4 expression differ- ences amongst various immune cells, particularly B cells, cytotoxic cells and macrophages (p < 0.05, Fig. 7B). Our findings also indicated that PAK4 is related to immune stim- ulators such as inducible T-cell costimulator (ICOS), po-
ARCHIVOS ESPAÑOLES DE UROLOGÍA
A Immunostimulator ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
8
:
5.0
2.5
0.0
2.5
C10orf54_exp
6
CD27_exp
0.0
ICOS_exp
IL2RA_exp
-2.5
0.0
4
-2.5
-2.5
-5.0
-5.0
-5.0
2
5
PAK4_exp
6
7
5
Spearman Correlation Test: rho = - 0.451, p = 3.72e-05 ACC (79 samples)
PAK4_exp
6
7
5
6
7
5
6
7
Spearman Correlation Test: rho = - 0.495, p = 4.72e-06 ACC (79 samples)
PAK4_exp
Spearman Correlation Test: rho = - 0.4, p = 0.000287 ACC (79 samples)
PAK4_exp Spearman Correlation Test: rho = - 0.597, p = 1.23e-08 ACC (79 samples)
8
2.5
6
5.0
PVR_exp
7
TMEM173_exp
TNFRSF18_exp
0.0
TNFSF13_exp
4
2.5
-2.5
6
2
0.0
-5.0
5
5
6
PAK4_exp
7
5
PAK4_exp
6
7
5
6
7
5
6
7
Spearman Correlation Test: rho = 0.419, p = 0.000139
Spearman Correlation Test: rho = - 0.426, p = 0.000104
PAK4_exp
Spearman Correlation Test: rho = - 0.417, p = 0.00015
PAK4_exp
Spearman Correlation Test: rho = - 0.461, p = 2.33e-05
B Immunoinhibitor
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
5.0
2
2.5
2.5
4
CD96_exp
CD160_exp
0
CD244_exp
CD274_exp
0.0
0.0
0
-2.5
-2.5
-2
-4
-5.0
-5.0
5
6
7
-4
PAK4_exp
5
PAK4_exp
6
7
5
PAK4_exp
6
7
5
6
7
Spearman Correlation Test: rho = - 0.436, p = 7.1e-05 ACC (79 samples)
Spearman Correlation Test: rho = - 0.287, p = 0.0105 ACC (79 samples)
Spearman Correlation Test: rho = - 0.466, p = 1.86e-05 ACC (79 samples)
PAK4_exp Spearman Correlation Test: rho = - 0.465, p = 1.97e-05 ACC (79 samples)
8
9
2.5
2
KDR_exp
PDCD1LG2_exp
0
.8
0.0
6
PVRL2_exp
TIGIT_exp
-2
7
-2.5
4
-4
-5.0
-6
6
5
6
PAK4_exp
7
5
6
6
6
Spearman Correlation Test: rho = - 0.392, p = 0.000395
PAK4_exp
7
5
7
5
7
Spearman Correlation Test: rho = - 0.492, p = 5.58e-06
PAK4_exp
Spearman Correlation Test: rho = 0.584, p = 3.16e-08
PAK4_exp Spearman Correlation Test: rho = - 0.502, p = 3.38e-06
liovirus receptor (PVR) and TNFRSF18 (Fig. 8A), as well as to immune inhibitors, including cluster of differentiation 160 (CD160), T-cell immunoreceptor with Ig and ITIM do- mains (TIGIT) and poliovirus receptor-related 2 (PVRL2) (p < 0.05, Fig. 8B). Thus, PAK4 is closely related to tumour immunity, and it may promote immune surveillance escape in tumours.
Correlation between PAK4 Expression and Chemokines in ACC
Chemokines and their receptors are important regula- tory molecules in the immune system, playing crucial roles in immune system modulation. PAK4 is significantly asso- ciated with several chemokines and their receptors in ACC. For example, the expression level of PAK4 is significantly
ARCHIVOS ESPAÑOLES DE UROLOGÍA
A Chemokine
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
7.5
7.5
5.0
2.5
5.0
5.0
2.5
CCL2_exp
CCL5_exp
CCL8_exp
0.0
CCL13_exp
2.5
0.0
2.5
-2.5
-2.5
0.0
0.0
-5.0
-5.0
-2.5
-2.5
5
6
7
PAK4_exp Spearman Correlation Test: rho = - 0.433, p = 7.84e-05 ACC (79 samples)
5
7
5
6
7
PAK4_exp
5
6
7
PAK4_exp
6
Spearman Correlation Test: rho = - 0.397, p = 0.000324 ACC (79 samples)
Spearman Correlation Test: rho = - 0.457, p = 2.79e-05 ACC (79 samples)
PAK4_exp
Spearman Correlation Test: rho = - 0.417, p = 0.000149 ACC (79 samples)
2.5
0.0
7.5
0.0
0.0
CCL23_exp
CCL25_exp
CXCL12_exp
5.0
XCL1_exp
-2.5
-2.5
-2.5
2.5
-5.0
-5.0
-5.0
0.0-
5
PAK4_exp
6
7
5
7
7
PAK4_exp
6
5
PAK4_exp
6
5
Spearman Correlation Test: rho = - 0.437, p = 6.76e-05
Spearman Correlation Test: rho = 0.385, p = 0.00051
Spearman Correlation Test: rho = - 0.531, p = 7.52e-07
PAK4_exp
6
7
Spearman Correlation Test: rho = - 0.506, p = 2.82e-06
B Receptor
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
2.5
1
4
2.5
0.0
0
CCR1_exp
CCR2_exp
CCR5_exp
0.0
CCR6_exp
2
-1
2.5
-2.5
-2
0
-3.
-5.0
-5.0
-2
-4
5
PAK4_exp
6
7
5
7
7
PAK4_exp
6
5
PAK4_exp
6
5
6
7
Spearman Correlation Test: rho = - 0.436, p = 6.97e-05
Spearman Correlation Test: rho = - 0.496, p = 4.65e-06 ACC (79 samples)
Spearman Correlation Test: rho = - 0.379, p = 0.000633 ACC (79 samples)
PAK4_exp Spearman Correlation Test: rho = - 0.321, p = 0.0041 ACC (79 samples)
ACC (79 samples)
2.5
2.5
2.5
4
0.0
CCR7_exp
CX3CR1_exp
CXCR3_exp
0.0
CXCR6_exp
0.0
-2.5
0
-2.5
-2.5
-5.0
-4-
-5.0
-5.0
5
6
7
5
6
7
5
6
7
5
6
7
PAK4_exp
Spearman Correlation Test:
PAK4_exp
PAK4_exp
PAK4_exp
rho = - 0.476, p = 1.18e-05
Spearman Correlation Test: rho = - 0.243, p = 0.0312
Spearman Correlation Test: rho = - 0.403, p = 0.000257
Spearman Correlation Test: rho = - 0.544, p = 3.47e-07
correlated with chemokines such as C-C motif chemokine ligand 2 (CCL2), CCL25 and X-C motif chemokine lig- and 1 (XCL1) (Fig. 9A), as well as chemokine receptors such as C-C chemokine receptor type 1 (CCR1), CX3CR1 and C-X-C chemokine receptor type 6 (CXCR6) (p < 0.05, Fig. 9B). These results further indicate that PAK 4 may serve as an immunomodulatory factor in ACC.
IHC Validation of PAK4 Expression in ACC Tissues
In verifying the expression of PAK4 at the protein level, IHC staining was performed on normal adrenal tis- sues and ACC tissues. The staining, mainly in the cyto- plasm, revealed higher PAK4 expression in ACC tissues than in normal adrenal tissues (p < 0.001, Fig. 10).
ARCHIVOS ESPAÑOLES DE UROLOGÍA
A
B
C
6
The IHC score of PAK4
4
2
P
0
T
adrenal tissues
ACC tissues
| Characteristics | Low expression of PAK4 | High expression of PAK4 | p value |
|---|---|---|---|
| n | 39 | 40 | |
| Gender, n (%) | 0.030 | ||
| Female | 19 (48.7%) | 29 (72.5%) | |
| Male | 20 (51.3%) | 11 (27.5%) | |
| Age, n (%) | 0.576 | ||
| ≤50 | 19 (48.7%) | 22 (55.0%) | |
| >50 | 20 (51.3%) | 18 (45.0%) | |
| Pathologic T stage, n (%) | (n =38) | (n = 39) | 0.011 |
| T1 | 8 (21.1%) | 1 (2.6%) | |
| T2 | 22 (57.9%) | 20 (51.3%) | |
| T3 | 4 (10.5%) | 4 (10.2%) | |
| T4 | 4 (10.5%) | 14 (35.9%) | |
| Pathologic N stage, n (%) | (n =38) | (n = 39) | 0.037 |
| N0 | 37 (97.4%) | 31 (79.5%) | |
| N1 | 1 (2.6%) | 8 (20.5%) | |
| Clinical M stage, n (%) | (n=38) | (n = 39) | 0.002 |
| M0 | 36 (94.7%) | 26 (66.7%) | |
| M1 | 2 (5.3%) | 13 (33.3%) | |
| Pathologic stage, n (%) | (n = 38) | (n = 39) | 0.002 |
| Stage I | 8 (21.1%) | 1 (2.6%) | |
| Stage II | 21 (55.2%) | 16 (41.0%) | |
| Stage III | 7 (18.4%) | 9 (23.1%) | |
| Stage IV | 2 (5.3%) | 13 (33.3%) | |
| Primary therapy outcome, n (%) | (n = 36) | (n =31) | 0.003 |
| PD | 3 (8.3%) | 15 (48.4%) | |
| SD | 1 (2.8%) | 1 (3.2%) | |
| PR | 1 (2.8%) | 0 (0.0%) | |
| CR | 31 (86.1%) | 15 (48.4%) | |
| OS event, n (%) | 0.023 | ||
| Alive | 30 (76.9%) | 21 (52.5%) | |
| Dead | 9 (23.1%) | 19 (47.5%) |
2
ARCHIVOS ESPAÑOLES DE UROLOGÍA
Discussion
PAK4, the predominantly overexpressed PAK fam- ily member in ACC, is also elevated in multiple malig- nancies probably because of the amplification of its ge- nomic locus (19q13.2), which is commonly observed in cancers such as hepatocellular carcinoma, breast cancer, pancreatic cancer and gastric cancer [15-17]. Its over- expression drives tumour progression by inhibiting anti- tumour immune responses, promoting cell survival, regu- lating adhesion, inducing cytoskeletal remodelling and fa- cilitating oncogenic transformation [6]. PAK4 also con- tributes to tumour invasion and drug resistance [9,18,19]. The oncogenic effects of PAK4 are mediated through multi- ple signalling pathways, including Wnt/3-catenin, extracel- lular signal-regulated kinase (ERK), phosphatidylinositol- 3-kinase (PI3K)/AKT and programmed cell death pro- tein 1 (PD-1)/programmed death-ligand 1 (PD-L1). PAK4 activates Wnt/3-catenin signalling by phosphorylating ß- catenin at Ser675 and enhances PI3K/AKT activity by bind- ing PI3K and promoting AKT phosphorylation [20,21]. In addition, PAK4 modulates the PD-1/PD-L1 axis, thereby in- creasing resistance to PD-1 blockade and positioning it as a potential immunotherapy target [22,23]. Despite these in- sights, the specific role and mechanisms of PAK4 in ACC pathogenesis remain unknown.
Our enrichment analysis reveals that PAK4 is posi- tively correlated with several key signalling pathways, in- cluding Hedgehog, p53 and TGF-3. The Hedgehog path- way, which is typically inactive in adult organisms, plays a crucial role in embryonic development and tissue forma- tion. Aberrant activation of this pathway can promote tu- mour growth [24]. The p53 gene, which is a well-known tu- mour suppressor, monitors cellular DNA damage and other stress signals, thereby regulating cell cycle and DNA repair [25]. Mutations in the p53 gene can lead to tumorigene- sis. TGF-3, a pleiotropic secreted cytokine, may induce cancer when highly expressed [26]. A strong association was also found between PAK4 and two critical physiologi- cal processes related to tumour progression, namely oxida- tive phosphorylation and cell cycle. Oxidative phospho- rylation, a central pathway in cellular energy metabolism, efficiently generates adenosine triphosphate (ATP) through the mitochondrial electron transport chain, providing the energy necessary for the rapid proliferation of tumour cells. Dysregulation of the cell cycle, which is a hallmark of can- cer, allows cells to bypass growth restrictions, divide con- tinuously and evade DNA repair or apoptosis [27]. Given these findings, PAK4 is implicated in the proliferation and growth of tumour cells and is considered as an important factor in the progression of ACC. There is growing recogni- tion of the crucial role of immune cell infiltration in tumour development [28-30]. In this study, PAK4 is associated with diverse immune cells, and it may promote ACC devel- opment through immune modulation. PAK4 shows a nega-
tive correlation with tumour-suppressing immune cells such as cytotoxic cells, NK CD56bright cells and T cells, which combat cancer via perforins, apoptosis induction and cy- tokines/chemokines such as interferon-gamma (IFN-y) and TNF-a, whilst activating other immune cells [31-33]. The multifaceted influence of PAK4 on immune cell infiltration can be attributed to several mechanisms. PAK4 might hin- der immune cell entry into the tumour microenvironment or suppress infiltration by altering cytokine and chemokine secretion. Furthermore, PAK4 interacts with various sig- nalling pathways that significantly influence the immune microenvironment, thereby contributing to an immunosup- pressive setting [34]. Studies have identified PAK4 as a key target for tumour immune evasion by blocking cytotoxic T cell infiltration [35]. Recent research has already linked PAK4 to the treatment of cancers such as renal cell carci- noma and prostate cancer. Targeted PAK4 inhibitors have been shown to simultaneously suppress cancer cell prolifer- ation and enhance immune cell responses, thereby improv- ing immune infiltration [36,37]. This dual action indicates that targeting PAK4 could be a promising therapeutic di- rection for ACC. Given that PAK4 expression in ACC is inversely correlated with the infiltration of most immune cells, the development of novel drugs that inhibit PAK4 ac- tivity could enhance immune activation and achieve thera- peutic efficacy. Future research should further validate the translational value of these mechanisms in clinical settings and explore the potential synergies between PAK4-targeted therapies and other immunotherapeutic strategies.
Undoubtedly, this study is not without its limitations, which are primarily attributable to the rarity of ACC. Ex- isting databases have limited cases compared to more com- mon cancers, increasing research uncertainty and data het- erogeneity. This challenges data analysis and result inter- pretation. Additionally, long-term follow-up of ACC pa- tients for survival data is difficult. To address these issues, multi-country medical centers and research institutions can collaborate to collect and share clinical data and biospeci- mens of ACC patients. Standardizing data-collection pro- tocols ensures consistent data quality. Establishing a dedi- cated ACC data-sharing platform would also promote data circulation. It is hoped that more standardised and effective ACC case data can be made available in the future, thereby yielding research findings that are more authentic and cred- ible.
Conclusions
Our findings indicate that PAK4 is overexpressed in ACC, which is associated with higher malignancy, cancer promotion and immune cell infiltration. PAK4 shows great application potential as a therapeutic target and prognos- tic biomarker for ACC. Future research should use updated bio-genomic databases and conduct more experiments for validation.
ARCHIVOS ESPAÑOLES DE UROLOGÍA
Availability of Data and Materials
The datasets used and/or analysed during the current study were available from the corresponding author on rea- sonable request.
Author Contributions
YC and JW-designed the study; XW, SL, GD and HL-collected and analyzed the data; QM, ML and WC- participated in drafting the manuscript. All authors con- ducted the study and contributed to critical revision of the manuscript for important intellectual content. All authors gave final approval of the version to be published. All au- thors participated fully in the work, took public responsibil- ity for appropriate portions of the content, and agreed to be accountable for all aspects of the work in ensuring that ques- tions related to the accuracy or completeness of any part of the work were appropriately investigated and resolved.
Ethics Approval and Consent to Participate
This study was approved by the Ethics Committee of Yuhuangding Hospital (institution review board number: 2024-384) and was performed in accordance with the prin- ciples of the Declaration of Helsinki. All eligible partici- pants signed an informed consent form.
Acknowledgment
Not applicable.
Funding
This work was supported by the National Natural Science Foundation of China (Nos. 82370690, 82303813), Natural Science Foundation of Shandong Province (Nos. ZR2023MH241, ZR2023QH271, ZR2021MH402, ZR2021MH185), Taishan Scholars Program of Shandong Province (Nos. tsqn201909199, tsqn202306403), and Shandong Health Science Innovation Team Building Project.
Conflict of Interest
Given his role as Editorial Board member, Yuanshan Cui had no involvement in the peer-review of this article and has no access to information regarding its peer-review.
References
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[2] Else T, Kim AC, Sabolch A, Raymond VM, Kandathil A, Caoili EM, et al. Adrenocortical Carcinoma. Endocrine Reviews. 2014; 35: 282-326.
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