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CDH2 and CDH13 as potential prognostic and therapeutic targets for adrenocortical carcinoma
Yongli Situ Da*, Li Denga*, Ziqing Huangª, Xiaoli Jianga,b, Liubing Zhaoa,b, Juying Zhanga,b, Lingling Lua, Quanyan Lianga,b, Qinying Xuª, Zheng Shaoª, and Meng Lianga
ªDepartment of Parasitology, Guangdong Medical University, Zhanjiang, China; bSchool of Medical Technology, Guangdong Medical University, Dongguan, China
ABSTRACT
Cadherin 2 (CDH2, N-cadherin) and cadherin 13 (CDH13, T-cadherin, H-cadherin) affect the progress and prognoses of many cancers. However, their roles in adrenocortical carcinoma (ACC), a rare endocrine cancer, remain unclear. To decipher the roles of these proteins in ACC and to identify their regulatory targets, we analyzed their expression levels, gene regulatory networks, prognostic value, and targets in ACC, using various bioinformatic analyses. CDH2 was strongly downregulated and CDH13 was strongly upregulated in patients with ACC; the expression levels of these genes affected the prognosis. In 75 patients, the expression of CDH2 and CDH13 was altered by 8% and 5%, respectively. CDH2 and CDH13, as well as their neighboring genes, were predicted to form a complex network of interactions, mainly through coexpression and physical and genetic interactions. CDH2 and its altered neighboring genes (ANGs) mainly affect tumor-related gene expression, cell cycle, and energy metabolism. The regulation of tumor-related integrin function, gene transcription, metabolism, and amide and phospholipid metabo- lism are the main functions of CDH13 and its ANGs. MiRNA and kinase targets of CDH2 and CDH13 in ACC were identified. CDH13 expression in patients with ACC was positively associated with immune cell infiltration. Anti-PD1/CTLA-4/PD-L1 immunotherapy significantly downregulated the expression of CDH13 in patients with ACC. Foretinib and elesclomol were predicted to exert strong inhibitory effects on SW13 cells by inhibiting the expression of CDH2 and CDH13. These data indicate that CDH2 and CDH13 are promising targets for precise treatment of ACC and may serve as new biomarkers for ACC prognosis.
ARTICLE HISTORY
Received 21 May 2023 Revised 29 July 2024 Accepted 7 November 2024
KEYWORDS
Bioinformatics; cadherin; cancer; cell cycle; gene regulatory network; immunotherapy; prognosis; transcription
Introduction
Adrenocortical carcinoma (ACC) is a rare endocrine tumor with a global incidence of 0.7-2.0 cases/million/year.1 Approximately 60% of ACC cases are functional. There is a wide range of clinical syndromes depending on the type of hormones produced.2 The prognosis of patients with ACC is poor, with a 5-year survival rate of < 40%.3 For most patients, there is no effective treatment to prolong survival, and complete surgical resection is the only treatment option.4 Therefore, it is necessary to determine the mechanisms underlying the occurrence and development of ACC, and to identify therapeutic targets.
Cadherin is a tumor suppressor that regulates tissue devel- opment and differentiation. Currently, more than 100 cadherins are identified, which are categorized into four groups, namely classical cadherins, protocadherins, desmosomal cadherins, and cadherin-related proteins.5 Increasing evidence suggests that an imbalance in cadherin expression caused by gene alterations can lead to tumor growth, invasion, and metastasis.6,7 Cadherin 2 (CDH2, N-cadherin) is a member of the classical cadherin group that maintains the integrity of cells and participates in many signal transduction pathways. Abnormal expression of CDH2 has been reported in many cancers, including that of
the lung, breast, and prostate, as well as squamous cell carcinoma.7 Abnormal expression of CDH2 can regulate the progression of malignant tumors by affecting apoptosis, angio- genesis, invasion, and metastasis of tumor cells.8 Therefore, CDH2 may be used as a therapeutic target and prognostic biomarker for multiple tumors.9 Cadherin 13 (CDH13, T-cadherin, H-cadherin) is a new member of the cadherin superfamily that maintains normal tissue structure. Abnormalities in CDH13 have been observed in many types of human malignant tumors.10 Recently, CDH13 has been shown to play a role as an anticancer gene in lung, breast, ovary, bladder, and gastric cancer.11,12 Abnormal expression of CDH13 plays a key role in cancer development by promoting the inactivation of tumor suppressor genes, activation of onco- genes, and increasing chromosome instability.13
The roles of CDH2 and CDH13 in ACC are not well under- stood. Therefore, in this study, we systematically analyzed the expression, gene regulatory network, prognostic value, puta- tive targets, and potential therapeutic agents of CDH2 and CDH13 in patients with ACC. Moreover, we examined the association of ACC with CDH2 and CDH13 and identified potential new targets and drugs for ACC therapy.
CONTACT Zheng Shao shaozheng@gdmu.edu.cn; Meng Liang East Wenming Road, Xiashan District, Zhanjiang 524023, China
Department of Parasitology, Guangdong Medical University, No. 2
*These authors contributed equally: Yongli Situ and Li Deng.
@ 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Results
CDH2 and CDH13 expression, prognosis, and genetic alterations in ACC
The transcript level of CDH2 was significantly downregulated (p < .05; Figure 1a) and that of CDH13 was significantly upre- gulated (p <. 05; Figure la-f) in patients with ACC. CDH2 transcript levels were significantly lower in males than in
females (p <. 05; Figure 1g,h). The transcript levels of both genes were significantly downregulated in older patients (≥65 years) compared with those in younger patients (<65 years) (p < .05; Figure 1i,j). Furthermore, the overall survival was longer for patients with ACC exhibiting low CDH2 expres- sion than for those with high expression (p =. 041; Figure 1k). Disease-free survival was longer in patients with ACC having low CDH2 and CDH13 expression than in those with high
a
b
C
d
e
GSE33371
f
CDH2
CDH13
GSE10927
GSE143383
GSE90713
0
Wilcoxon, p = 0.00061
T-test, p = 0.00046
Wilcoxon, p = 0.00061
T-test, p = 5.20-05
00
4
CDH13
CDH13
·
4
CDH13
2-
2
CDH13
4
CDH13 Expression (z-score)
CDH13 Expression (z-score)
CDH13 Expression (z-score)
CDH13 Expression (z-score)
6
1
1
3
2
2
0
-
0
N
1
0
1
0
2
-
-2
”
-2
-3
0
0
Normal
Tumor
Normal
Tumor
Normal
Tumor
Normal
Tumor
(num(T)-77; num(N)-12B)
ACC
(num(T)-77; num(N)-12B)
ACC
Tissue
Tissue
Tissue
Tissue
09
h
TCGA_ACC
1
TCGA_ACC
j
GSE19775
Expression of CDH2 in ACC based on patient’s gender
Wilcoxon, p = 0.0076
Wilcoxon, p = 0.0014
2
Wilcoxon, p = 0.014
80
2
CDH2
2
CDH2
CDH13
DE
CDH2 Expression (z-score)
CDH2 Expression (z-score)
CDH13 Expression (z-score)
1
.
Transcript per million
60
1
1
0
40
.
0
-1
0
20
-2
0
-1
Male
(n=31)
Female (n=48)
-1
TCGA samples
Female
Male
65
-3
>65
$65
,65
Gender
Age
Age
k
1
Overall Survival
Disease Free Survival
m
Overall Survival
n
Disease Free Survival
0
Low CDH2 TPM
0
High CDH2 TPM
Low CDH2 TPM
10
9
High TPM
Low CDH13 TPM
High CDH13 TPM
Low CDH13 TPM
Logrank p=0.0061
Logrank p=0.064
High CDH13 TPM
Logrank p=0.041
HR(high)=2.2
HR(high)=2.6
HR(high)=2.1
Logrank p=0.00027
0.8
p(HR)-0.047
0.8
p(HR)-0.0083
0.8
0g
HR(high)=3.5
p(HR)-0.00059
Percent survival
n(high)=38
Percent survival
n(high)=38
p(HR)-0.07
n (low)=38
n[low)=38
Percent survival
n(high)=38 n(low)=38
Percent survival
n(high)=38 nilow) 30
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
2
2
2
8
CDH2
CDH2
CDH13
8
8
8
CDH13
8
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
Months
0
Altered in 6 (8%) of 75 patients
CHD2
8%
Genetic alteration
Missense Mutation (unknown significance)
Amplification
mRNA High
mRNA Low
No alterations
p
Altered in 4 (5%) of 75 patients
CDH13
5%
Genetic alteration
Deep Deletion
mRNA Low
No alterations
expression of these genes (p =. 0061 and p = . 00027, respec- tively; Figures 11-n). Moreover, CDH2 and CDH13 expression was altered by 8% and 5%, respectively, in patients with ACC (Figure 1o,p).
Interaction network of CDH2 and CDH13 and their altered neighboring genes in ACC
We noted CDH2 and CDH13 altered neighboring gene (ANG) alteration frequencies of ≥ 33.33% and ≥ 25.00%, respectively, among the 50 most frequent ANGs in patients with ACC (Tables 1 and 2). The most frequent ANGs for CDH2 in patients with ACC were PKHD1 (66.67%), PHF20L1 (50.00%), and KCNH7 (50.00%) (Table 1). Furthermore, NT5C3A (50.00%), ANKMY1 (50.00%), and CD1C (50.00%) were the most frequent ANGs of CDH13 in patients with ACC (Table 2). We obtained 43 nodes and 124 edges in the protein - protein interaction (PPI) networks of CDH2 and ANGs (Figure 2a). CDH2 was predicted to be connected to ANGs by coexpression, shared protein domains, colocalization, phy- sical interactions, and genetic interactions in a complex inter- action network (Figure 2b). Moreover, we obtained 40 nodes and 114 edges in the PPI networks of CDH13 and ANGs (Figure 2c). CDH13 was predicted to be connected to ANGs by coexpression, physical interactions, pathways, colocaliza- tion, shared protein domains, and genetic interactions (Figure 2d).
Gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis of CDH2, CDH13, and their ANGs in ACC
The biological processes associated with CDH2 and ANGs in patients with ACC were mainly associated with cell morphogenesis, epithelial cell development, postsynaptic organization, microtubule cytoskeleton organization, sper- matid development, cellular calcium ion homeostasis, brain development, camera-type eye development, and purine ribonucleotide metabolism (Figure 2e). Moreover, glutama- tergic synapses, presynaptic active zones, endoplasmic reti- culum lumen, dendrites, and the mitochondrial matrix were the main cellular components of CDH2 and its ANGs in patients with ACC (Figure 2f). The molecular functions of CDH2 and its ANGs in patients with ACC included struc- tural constituents of synapses, histone deacetylase binding, monoatomic ion transmembrane transporter activity, ATP hydrolysis activity, protein kinase binding, calcium ion binding, and protein homodimerization activity (Figure 2g). The biological processes related to CDH13 and its ANGs in patients with ACC were T-cell activation involved in immune, monocarboxylic acid metabolic pro- cess, regulation of fat cell differentiation, phagocytosis, epithelial cell proliferation, positive regulation of binding, regulation of lipid biosynthetic process, small GTPase- mediated signal transduction, muscle organ development, secretion by cell, plasma membrane-bound cell projection assembly, DNA metabolic process, synaptic signaling, and positive regulation of cell adhesion (Figure 2h). Additionally, the main cellular components of CDH13 and
| Gene | Altered group | Unaltered group | p-Value |
|---|---|---|---|
| DNMT3L | 3 (50.00%) | 0 (0.00%) | 2.96E-04 |
| PKHD1 | 4 (66.67%) | 5 (7.25%) | 1.38E-03 |
| PHF20L1 | 3 (50.00%) | 0 (0.00%) | 2.96E-04 |
| KCNH7 | 3 (50.00%) | 1 (1.45%) | 1.15E-03 |
| CHD5 | 3 (50.00%) | 2 (2.90%) | 2.78E-03 |
| DROSHA | 3 (50.00%) | 2 (2.90%) | 2.78E-03 |
| ZSWIM6 | 3 (50.00%) | 2 (2.90%) | 2.78E-03 |
| ADAMTSL4 | 3 (50.00%) | 3 (4.35%) | 5.38E-03 |
| ATP2B3 | 3 (50.00%) | 3 (4.35%) | 5.38E-03 |
| MAGEA12 | 3 (50.00%) | 3 (4.35%) | 5.38E-03 |
| MAGEA2 | 3 (50.00%) | 3 (4.35%) | 5.38E-03 |
| MAGEA2B | 3 (50.00%) | 3 (4.35%) | 5.38E-03 |
| OPLAH | 3 (50.00%) | 3 (4.35%) | 5.38E-03 |
| TRIO | 3 (50.00%) | 3 (4.35%) | 5.38E-03 |
| ACTN1 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| AMTN | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| ANKRD18DP | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| ANXA13 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| ARTN | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| ATP13A5 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| ATP5PB | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| BDH1 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| BSN | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| C8B | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| C8ORF76 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| CCDC39 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| CHPT1 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| CHRD | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| CLCN2 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| COPB2 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| DLG1 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| DRGX | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| ECHS1 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| ELAVL2 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| ENAM | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| FAM157A | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| FAM83A | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| FAM91A1 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| FAT3 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| FBXO32 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| FGGY | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| FRG2B | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| FYTTD1 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| HJURP | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| HOOK1 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| HSD17B4 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| IFI44L | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| IGF2BP2 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| IL18R1 | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
| IQCG | 2 (33.33%) | 0 (0.00%) | 5.41E-03 |
its top 50 ANGs in patients with ACC were the transcrip- tion repressor complex, synaptic membrane, actin-based cell projection, extracellular matrix, plasma membrane protein complex, and transporter complex (Figure 2i). The molecu- lar functions of CDH13 and its ANGs in patients with ACC included integrin binding, transcription corepressor activity, ATP-dependent activity, amide binding, and phospholipid binding (Figure 2j).
MiRNA and kinase targets of CDH2 and CDH13 in patients with ACC
Using LinkedOmics, we found the miRNA targets of CDH2 and CDH13 (Table 3). MiR-331, miR-486, and miR-24 were the targets of CDH2 in ACC (p <. 001) (Table 3). The miRNA targets of CDH13 in ACC were miR-101, miR-142-3P, and miR-527 (p <. 001) (Table 3). Moreover, we found that
| Gene | Altered group | Unaltered group | p-Value |
|---|---|---|---|
| NT5C3A | 2 (50.00%) | 0 (0.00%) | 2.16E-03 |
| ANKMY1 | 2 (50.00%) | 1 (1.41%) | 6.37E-03 |
| CD1C | 2 (50.00%) | 1 (1.41%) | 6.37E-03 |
| CHD2 | 2 (50.00%) | 1 (1.41%) | 6.37E-03 |
| CTBP2 | 2 (50.00%) | 1 (1.41%) | 6.37E-03 |
| FCRL2 | 2 (50.00%) | 1 (1.41%) | 6.37E-03 |
| TRIB3 | 2 (50.00%) | 1 (1.41%) | 6.37E-03 |
| ECHDC3 | 2 (50.00%) | 2 (2.82%) | 0.0125 |
| NDUFA10 | 2 (50.00%) | 2 (2.82%) | 0.0125 |
| PTPDC1 | 2 (50.00%) | 2 (2.82%) | 0.0125 |
| SCARB1 | 2 (50.00%) | 2 (2.82%) | 0.0125 |
| CCN2 | 2 (50.00%) | 4 (5.63%) | 0.0301 |
| ENTREP3 | 2 (50.00%) | 4 (5.63%) | 0.0301 |
| FAM241A | 2 (50.00%) | 4 (5.63%) | 0.0301 |
| MEX3C | 2 (50.00%) | 4 (5.63%) | 0.0301 |
| NOM1 | 2 (50.00%) | 4 (5.63%) | 0.0301 |
| TUT7 | 2 (50.00%) | 4 (5.63%) | 0.0301 |
| ZFPM1 | 2 (50.00%) | 4 (5.63%) | 0.0301 |
| GRIN3B | 2 (50.00%) | 5 (7.04%) | 0.0413 |
| MYO1G | 2 (50.00%) | 5 (7.04%) | 0.0413 |
| UTRN | 2 (50.00%) | 5 (7.04%) | 0.0413 |
| ACBD5 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| ADAM23 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| AOAH | 1 (25.00%) | 0 (0.00%) | 0.05 |
| APBB1IP | 1 (25.00%) | 0 (0.00%) | 0.05 |
| ARL5B | 1 (25.00%) | 0 (0.00%) | 0.05 |
| ASB1 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| ATP8B1 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| BAMBI | 1 (25.00%) | 0 (0.00%) | 0.05 |
| BMPER | 1 (25.00%) | 0 (0.00%) | 0.05 |
| BMT2 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| BTD | 1 (25.00%) | 0 (0.00%) | 0.05 |
| C11ORF68 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| C7ORF33 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| CCN6 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| CCR4 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| CDC40 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| CEP70 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| CHD4 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| CHML | 1 (25.00%) | 0 (0.00%) | 0.05 |
| CMC2 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| CNBP | 1 (25.00%) | 0 (0.00%) | 0.05 |
| DCUN1D4 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| DGKI | 1 (25.00%) | 0 (0.00%) | 0.05 |
| DHCR7 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| DPY19L1 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| DPY19L2P1 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| DRAP1 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| EEPD1 | 1 (25.00%) | 0 (0.00%) | 0.05 |
| ELMO1 | 1 (25.00%) | 0 (0.00%) | 0.05 |
DYRK1B, LYN, and NLK were the kinase targets of CDH2 in patients with ACC (p <. 001) (Table 4). The kinase targets of CDH13 were TTK, CDK2, and CHEK1 in patients with ACC (p <. 001) (Table 4).
Correlation of differentially expressed genes and CDH2 and CDH13 expression in patients with ACC
A total of 4,824 and 2,748 genes were found to be closely related to CDH2 and CDH13, respectively, in patients with ACC (Figure 3a-d). Among them, 2,096 and 1,898 genes showed positive correlation and 2,728 and 850 genes showed negative correlation with CDH2 and CDH13 expression, respectively (Figure 3a-d). Fifty genes showed significant positive and nega- tive correlation with CDH2 and CDH13 expression in patients with ACC (Figure 3b,c,e,f). The expression of CDH2 was strongly and positively associated with VSNL1 (Pearson correlation
coefficient [PCC] = 0.6735, p= 1.043e-11; Figure 3g), TCF7 (PCC=0.6491, p = 9.77e-11; Figure 3h), and RASL10B (PCC= 0.6475, p = 1.128e-10; Figure 3i). The expression of CDH13 was positively correlated with COL4A1 (PCC = 0.7066, p = 3.436e-13; Figure 3j), ANGPT2 (PCC= 0.693, p = 1.478e-12; Figure 3k), and ESAM (PCC = 0.6546, p = 5.997e-11; Figure 3l) expression.
Correlation of immune cell infiltration and CDH13 expression and anti-PD1/CTLA-4/PD-L1 immunotherapy in ACC
The expression levels of CDH13 in patients with ACC were positively associated with immune cell infiltration (B cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells) (p <. 05; Figure 4a-f). The cumulative survival of patients with ACC was longer than that of patients with low CD8+ T-cell expression levels (p = . 05; Figure 4b). However, the cumulative survival of patients with ACC was longer in those with low CDH13 expression levels (p =. 04; Figure 4b). Moreover, CDH13 expression in patients with ACC treated with anti- PD1/CTLA-4, anti-PD1PD-L1, and anti-PD-L1 was signifi- cantly downregulated (p = . 05) (Figure 4g-i).
Therapeutic drugs of CDH2 and CDH13 in ACC
Using the BEST database, we predicted foretinib and elesclo- mol as the top drug candidates for targeting CDH2 and CDH13, respectively (Figure 5a-f). Next, the genomics of drug sensitivity in the cancer database was used to evaluate the inhibitory effects of foretinib and elesclomol on an ACC cell line (SW13). Foretinib inhibited 953 cell lines with area under the curve (AUC) values greater than 0.980 (Figure 5c) and had a good inhibitory effect on these cell lines (0.00285 ≤ IC50 [uM] ≤ 3120) (Figure 5b). Furthermore, foretinib had a strong inhibitory effect on SW13 (an ACC cell line) (AUC = 0.783, IC50 [uM] = 3.25) (Figure 5d,e). However, elesclomol inhibited 921 cell lines, with AUC values greater than 0.0209 (Figure 5h) and had a good inhibitory effect on these cell lines (0.000231 ≤ IC50 [uM] ≤ 10.3) (Figure 5g). Elesclomol had a strong inhibitory effect on SW13 cells (AUC= 0.422, IC50 [uM] = 0.00763) (Figure 5i,j).
Discussion
Abnormal expression of CDH13 has been reported in various tumors. However, its expression in patients with ACC remains unknown. CDH13 expression is often downregulated in cancer cells. Low CDH13 expression is associated with poor prognosis in various cancers, such as lung, ovarian, cervical, and prostate cancer.13 Notably, we found that the expression of CDH13 was strongly upregulated in patients with ACC, and low expression was related to a good prognosis in ACC patients. However, CDH2 expression was strongly downregulated in patients with ACC. Downregulation of N-cadherin has been reported in ACC.14 CDH2 expression levels were lower in male patients than in female patients with ACC; patients exhibiting low CDH2 expression had longer survival times than those with high expression. The number of female patients with ACC generally exceeds that of male patients (1.5:1).15,16 Whether
a
C8B
CHRD
b
2
HOOK1
E
HSD17B4
ATP1345
ADAMTSL4
ACTN1
CLCN2
O
BOH1
FGGY
%
ANXA13
TRIO
ATP2B3
COPB2
8
MAGEA2
ECHS1
4p
DLG1
CHPT1
MAGEA12
MAGEAZB
3
*
5
CDH2
ATP5F1
DROSHA
ARTN
CHO5
ENAI
T
DNMT3L
AMTN
FAT3
1
4
“X
ELAVLZ
PHF20L1
IL18R1
IGF2BP2
-
2
BSN
A
Networks
FAM83A
FAM91A1
IFI44L
Co-expression
ZSWIM6
IQCG
CCDC39
Shared protein domainis
OPLAH
Co-localization
C8orf76
FBXO32
2
Physical Interactions
a
$
CDH2
Genetic Interactions
CDH2
DRAP1
CTBP2
CHD2
C
1
2
C110068
CDH13
d
ADAM23
DPY19L1
CHD4
EEPD1
E
ZFPMI
NOM1
DGKI
AMy
5
CDC40
7
BMPER
ELMO1
TRIB3
0
U
C7orf60
UTRN
DHCR7
8
BAMBI
=
SCARB1
CNBP
ACBD5
ČTGF
DCUN1D4
a
AOAH
PTPDC1
PP
ARL58
ZCCHC6
Networks
CHML
Predicted
MYO1G
GRIN3B
Co-expression
ATP9B1
el
Physical Interactions
IA
MEX3C
APBB1IP
CCR4
Pathway
4
BTD
Co-localization
Shared protein domains
FCRL2
CD1C
CEP70
A
CDH13
Genetic Interactions
CDH13
e
CDH2
Biological processes
f
CDH2
Cellular components
GO:0000902: cell morphogenesis
GO:0002064: epithelial cell development
GO:0099173: postsynapse organization
GO:0098978: glutamatergic synapse
GO:0000226: microtubule cytoskeleton organization
GO:0048786: presynaptic active zone
GO:0007286: spermatid development
GO:0005788: endoplasmic reticulum lumen
GO:0006874: cellular calcium ion homeostasis
GO:0007420: brain development
GO:0030425: dendrite
GO:0043010: camera-type eye development
GO:0005759: mitochondrial matrix
GO:0009150: purine ribonucleotide metabolic process
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
-log10(P)
-log10(P)
h
g
CDH13
CDH2
Biological processes
Molecular functions
GO:0002286: T cell activation involved in immune response
GO:0032787: monocarboxylic acid metabolic process
GO:0098918: structural constituent of synapse
GO:0045598: regulation of fat cell differentiation GO:0006909: phagocytosis
GO:0042826: histone deacetylase binding
GO:0050673: epithelial cell proliferation
GO:0015075: monoatomic ion transmembrane transporter activity
GO:0051099: positive regulation of binding
GO:0016887: ATP hydrolysis activity
GO:0046890: regulation of lipid biosynthetic process
GO:0019901: protein kinase binding
GO:0007264: small GTPase mediated signal transduction
GO:0005509: calcium ion binding
GO:0007517: muscle organ development
GO:0042803: protein homodimerization activity
GO:0032940: secretion by cell
GO:0120031: plasma membrane bounded cell projection assembly
0
1
2
3
4
5
GO:0006259: DNA metabolic process
-log10[P)
GO:0099536: synaptic signaling
GO:0045785: positive regulation of cell adhesion
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
-log10(P)
1 CDH13
Cellular components
j
CDH13
Molecular functions
GO:0017053: transcription repressor complex
GO:0097060: synaptic membrane
GO:0005178: integrin binding
GO:0098858: actin-based cell projection
GO:0003714: transcription corepressor activity
GO:0031012: extracellular matrix
GO:0140657: ATP-dependent activity
GO:0098797: plasma membrane protein complex
GO:0033218: amide binding
GO:1990351: transporter complex
GO:0005543: phospholipid binding
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
-log10(P)
-log10(P)
| Gene | Gene set | Leading Edge Number | P-value |
|---|---|---|---|
| CDH2 | CCAGGGG,miR-331 | 27 | <2.2e-16 |
| GTACAGG,miR-486 | 13 | <2.2e-16 | |
| CTGAGCC,miR-24 | 45 | <2.2e-16 | |
| CDH13 | GTACTGT,miR-101 | 65 | <2.2e-16 |
| ACACTAC,miR-142-3P | 50 | <2.2e-16 | |
| CTTTGCA,miR-527 | 57 | <2.2e-16 |
a sex-related difference in CDH2 expression is an important factor affecting the prognosis of patients with ACC warrants further investigation. The transcript levels of CDH2 and CDH13 in patients with ACC aged >65 years were significantly lower than those in patients aged <65 years. Does this indicate an age advantage in the survival rate of patients with ACC? In this context, it is pertinent to mention that the survival rate of children with ACC who underwent surgery was lower than that of adults with ACC.17 Next, we attempted to explain the abnormalities in CDH2 and CDH13 expression through genetic alterations in patients. The expression of CDH2 and CDH13 was altered in 9% and 15% of patients with ACC, respectively. Abnormal expression of CDH2 and CDH13 caused by genetic changes may also be an important factor. DNA methylation affects the abnormal expression of CDH2 and CDH13 in cancer patients.18,19 However, this hypothesis warrants further investigation. These results suggest that CDH2 and CDH13 may serve as potential therapeutic and prognostic markers in patients with ACC.
CDH2, CDH13, and their ANGs are linked to a complex interaction network through coexpression and physical and genetic interactions. The molecular functions of CDH2 and its ANGs mainly include histone deacetylase binding, monoa- tomic ion transmembrane transporter activity, ATP hydroly- sis, protein kinase binding, and calcium ion binding. This shows that CDH2 and its ANGs may affect gene expression, cell cycle, and energy metabolism by regulating histone acet- ylation, protease activity, and ion channels, ultimately affecting tumor proliferation, differentiation, and metastasis. The mole- cular functions of CDH13 and its ANGs in patients with ACC include integrin binding, transcription corepressor activity, ATP-dependent activity, amide binding, and phospholipid binding. Thus, CDH13 and its ANGs may regulate the prolif- eration, invasion, migration, and angiogenesis of cancer cells by affecting integrin function, gene transcription, ability meta- bolism, and amide and phospholipid metabolism. Taken together, the functions involving CDH2, CDH13, and their ANGs may be involved in the occurrence and progression of ACC. Therefore, the regulation of these genes may be a potential treatment strategy for ACC.
Mining miRNA and kinase targets of key genes is an impor- tant breakthrough in ACC treatment. We found that miR-331, miR-486, miR-24, miR-101, miR-142-3P, and miR-527 are targets of CDH2 and CDH13 in patients with ACC. MiR-331, miR-24, miR-101, and miR-527 are associated with tumor cell proliferation, migration, invasion, and drug resistance, and may, therefore, be promising targets for cancer therapy.2 However, their relationship with ACC has not yet been reported. Furthermore, miR-486-3p may inhibit ACC cell pro- liferation by reducing the production of fatty acid synthases and fatty acids.23 Our results also indicate that miR-101, miR- 142-3P, and miR-527 are targets of CDH13 in patients with ACC. In a previous study, we showed that miR-142-3P might be an important regulatory target in ACC.24 We investigated the kinase targets of CDH2 and CDH13 in patients with ACC. We found that DYRK1B, LYN, NLK, TTK, CDK2, and CHEK1 were the kinase targets of CDH2 and CDH13. DYRK1B is a serine/threonine kinase involved in tumor progression and cell proliferation. Silencing or inactivation of DYRK1B may be a potential therapeutic strategy in cancer.25 Overexpression of LYN promotes the proliferation, migration, and invasion of cervical cancer cells by activating the IL-6/STAT3 pathway. Thus, it could be used as a novel target for the treatment of cervical cancer.26 NLK is a key regulator in many cancers. Lentivirus-mediated NLK knockout inhibited the growth and metastasis of small cell lung cancer; therefore it can be used as a potential target for the treatment of small cell lung cancer.27 However, its role in ACC has not yet been clarified. Furthermore, high expression of TTK, CDK2, and CHEK1 has been reported in ACC, which may play an impor- tant role in ACC progression and serve as potential biomarkers for future diagnosis and treatment.28-30 In summary, these miRNAs and kinases may serve as potential therapeutic targets for ACC.
We explored the correlation between the differentially expressed genes and CDH2 and CDH13 expression in patients with ACC. The expression of 4,824 and 2,748 genes was cor- related with CDH2 and CDH13 expression, respectively. Among these, VSNL1, TCF7, RASL10B, COL4A1, ANGPT2, and ESAM were the top six genes whose expression was posi- tively correlated with the expression of CDH2 and CDH13. Therefore, targeting these genes may provide additional ther- apeutic options for ACC. Immune infiltration is closely asso- ciated with tumor progression and prognosis.31 Cancer immunotherapy has led to significant advances in the treat- ment of multiple cancers. As expected, the expression levels of CDH13 in patients with ACC were positively correlated with immune cell infiltration. Targeting CDH13 or its related reg- ulatory targets may be a feasible strategy for improving the immune microenvironment in patients with ACC. We also
| Gene | Kinase target | Description | Leading edge number | P-value |
|---|---|---|---|---|
| CDH2 | Kinase_DYRK1B | dual specificity tyrosine phosphorylation regulated kinase 1B | 2 | <2.2e-16 |
| Kinase_LYN | LYN proto-oncogene, Src family tyrosine kinase | 24 | <2.2e-16 | |
| Kinase_NLK | nemo like kinase | 5 | <2.2e-16 | |
| CDH13 | Kinase_TTK | TTK protein kinase | 9 | <2.2e-16 |
| Kinase_CDK2 | cyclin dependent kinase 2 | 102 | <2.2e-16 | |
| Kinase_CHEK1 | checkpoint kinase 1 | 42 | <2.2e-16 |
a
CDH2 Association Result
b
CDH2
c
CDH2
1
CDH2
BRASIL 900
00
-log10(pvalue)
CO
PHOOLS WNT4 GYLTLID
Z-Score Group
Z-Score Group
-3
2
-3
2
1
0
1
0
0
A
0
*
MERTR
-1
<3
1
2
-1
<3
1 2
CV
CAOTS
A
0
THPT
GALNT14 GALNT15
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
HACIÔNG
LOC872550
Pearson Correlation Coefficient (Pearson test)
d
CDH13 Association Result
e
CDH13
f
CDH13
₪
CDH13
GOL4AZ
10
SPOVE
EPLEBPS
-log10(pvalue)
CƠ
2
Z-Score Group >3
Z-Score Group
2
-3
₹
6
84
M
0
-1
2 4
1
0
<3
-1 <3
2 4
4
2
0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Pearson Correlation Coefficient (Pearson test)
Pearson-Correlation:0.6735 P-value:1.043e-11
h
Pearson-Correlation:0.6491 P-value:9.77e-11
. i
Pearson-Correlation:0.6475 P-value:1.128e-10 Sample Size:(N=79)
Sample Size:(N=79)
Sample Size:(N=79)
CDH2
1
CDH2
₪
12
CDH2
28
~
10
10
0
CƠ
0
00
20
VSNL1
TCF7
RASL10B
6
00
6
4
6
+
2
+
₪
0
2
0
2
4
6
8
10
12
2
4
6
8
10
12
2
4
6
8
10
12
CDH2
CDH2
CDH2
j
Pearson-Correlation:0.7066 P-value:3.436e-13 Sample Size:(N=79)
k
Pearson-Correlation:0.693 P-value:1.478e-12
1
Pearson-Correlation:0.6546 P-value:5.997e-11
00
Sample Size:(N=79)
Sample Size:(N=79)
CDH13
CDH13
CDH13
16
12
%
08
₪
0
COL4A1
14
ANGPT2
ESAM
0
12
co
8
0
CO
+
00
-
5
6
7
8
9
10
5
6
7
8
9
10
5
6
7
8
9
10
CDH13
CDH13
CDH13
a
CDH13
CDH13 Expression Level (log2 TPM)
Purity
B Cell
CD8+ T Cell
CD4+ T Cell
Macrophage
Neutrophil
Dendritic Cell
6
cor = - 0.009
p = 9.39e-01
partial.cor = 0.363
p = 1.58e-03
partial.cor = 0.099
p = 4.06e-01
partial.cor = 0.301
p = 9.76e-03
partial.cor = 0.236
p = 4.41e-02
partial.cor = 0.232
p = 4.85e-02
partial.cor = 0.408
p = 3.37e-04
4-
..
2
ACC
0-
2
0.2
0.4
0.6
0.8
1.0
0.11
0.12
0.13
0.20
0.25
0.30
0.35 0.07
0.09
0.11
0.13
0.15
Infiltration Level
0.08
0.12
0.16
0.12
0.14
0.16
0.18
0.49
0.50
0.51
0.52
0.53
b
CDH13
B Cell
CD8+ T Cell
CD4+ T Cell
Macrophage
Neutrophil
Dendritic Cell
CDH13
1.0
Log-rank P = 0.396
Log-rank P = 0.05
Log-rank P = 0.724
Log-rank P = 0.175
Log-rank P = 0.475
Log-rank P = 0.601
Log-rank P = 0.04
Cumulative Survival
0.8
OS
0.6-
Expression
Expression Low
Expression Low
Expression Low
Expression
Expression Low
Expression Low
0.4
Low
High
High
High
High
High
High
High
0
CDH13
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
Time to Follow-Up (months)
50
100
150
0
50
100
150
0
50
100
150
C
507
ImmunsScore
d
Endothotel cells
GSE143383
e
GSE76019
f
GSE90713
Neutrophils
Cytotoxic mrhocytes
Cor = 0.355; Pval = 7e-03
Cor = 0.361; Pval = 3.7e-02
Cor = 0.411; Pval = 1.5e-03
3
Macrophage
2
StromalScore_estimate
2
StromalScore_estimate
2
StromalScore_estimate
Macrophages_MO
Dendrise_cols delivales
Toets folicular neiper
T_calls_CD4 memory mating
1
1
1
-
T_cols_CD4_memory_activened T_00“‘S Game maquiatory (Treges
0
Mast_cols resting
0
0
-1
Monocytes
T_Ous GD4
-1
-1
Macrophages_M1
-2
Macrophages_MO
Dendrisa_colis activated T_cells_CO4_naive
-2
1
0
1
2
-2
T_cells_gamma deita
-1
CDH13 Expression (z-score)
0
1
CDH13 Expression (z-score)
2
-3
-2
-1
0
1
2
T_cul_CD4_THE Og resing
CDH13 Expression (z-score)
T_bills_CD4 memory Foama
& o
Gao cohort 2018 Anti-PD-1/CTLA-4
h
Macrophages Mi
Macrophages M2
Kim cohort 2019 Anti-PD-1/PD-L1
1 IMvigor210 cohort 2018 Anti-PD-L1
T_ouls_GDE
T-test, p = 0.00033
T-test, p = 0.015
Wilcoxon, p = 0.032
Skeletal muscle
Hobalocytes
2
2
Smy Apracylas
CDH13 Expression (z-score)
CDH13 Expression (z-score)
CDH13 Expression (z-score)
2
Epithelial cells
Preadipocytes
CO4 ._ memory Tons
1
1
Class-switched_memory_8-cell
Macrophages_M
0
0
Melanocytes
Monocytes
0
CDA+_Tem
Macrophages
CO4.
Sabocytes
-1
Memory_@-calix
-1
CD4 ._ naive_
Eosino SOC
Keratinocytes
-2
CD4_1001S
-2
QSE19775
GEE143383
TOGALAGG
08E10927
NR
R
NR
R
NR
R
Response
Response
Response
found that high levels of CD8+ T-cell infiltration can possibly prolong the survival of patients with ACC. However, our results show that CDH13 expression is not related to the infiltration of CD8+ T cells. CDH13 and its ANGs can also activate T cells. These findings provide new avenues for ACC immunotherapy using CD8+ T cells. Furthermore, we found that the expression of CDH13 in patients with ACC who were
administered anti-PD1/CTLA-4/PD-L1 was strongly downre- gulated. Thus, patients with ACC who are treated with anti- PD1/CTLA-4/PD-L1 antibodies may have a better prognosis. However, the role of immunotherapy in ACC is limited.32 Studies have shown that the RTK signaling pathway inhibitor, foretinib, and the HSP90 inhibitor, elesclomol, have good antitumor effects and are safe.33,34 However, many tyrosine
a
b
Cell line IC50 values (Foretinib)
C Cell line AUC values (Foretinib)
High expression indicates sensitivity
Cisplatin_1005
1.0
104
TW 37_1149
Number of cell lines screened: 953
Number of cell lines screened: 953
Maximum AUC: 0.980
Thapsigargin_180
Maximum IC50 (uM): 3.12e3
Minimum AUC: 0.0177
SB52334_304
10ª
Geometric mean (uM): 2.48
0.8
MetAP2 Inhibitor, A832234_410
Minimum IC50 (uM): 0.00285
MCT1_6447_1436
102
AZD4547_1135
IC50 (micromolar)
Min screening concentration (uM): 0.0100
Max screening concentration (uM): 10 6
0.6
QS11_151
10
-…- max cone
AZD6738_1394
AUC
AZD7762_1402
Olaparib_1017
10
0.4
AZ20_1184
YM201636_310
10-
Methotrexate_1008
0.2
rTRAIL_1261
10-2
min conc
Foretinib_308
Correlation
TCGA_ACC GSE90713 GSE143383
GSE19775
1
0.5
10-3
0.0
0
ranked by sensitivity
ranked by sensitivity
-0.5
-1
d
e
Cell line (SW13) IC50 values (Foretinib)
Cell line (SW13) AUC values (Foretinib)
1.0
104
IC50 (uM): 3.25
AUC: 0.783
103
0.8
102
IC50 (micromolar)
0.6
10
max conc
AUC
100
0.4
10
0.2
10
min conc
103
0.0
ranked by sensitivity
ranked by sensitivity
09
h
f
Cell line IC50 values (Elesclomol)
Cell line AUC values (Elesclomol)
102
1.0
Number of cell lines screened: 921 Maximum IC50 (uM): 10.3
Number of cell lines screened: 921
Maximum AUC: 0.997
AZD6482_1066
101
Geometric mean (uM): 0.0486
Minimum IC50 (uM): 0.000231
Minimum AUC: 0.0209
0.8
AZD6482_156
Min screening concentration (uM): 0.000781
PLX-4720_1371
100
Max screening concentration (uM): 0 200
IAP_5620_1428
0.6
Sepantronium bromide_268
max conc
AZD1332_1463
10-
AUC
JAK3_7406_1434
0,4
High expression indicates sensitivity
IC50 (micromolar)
Cisplatin_1496
TW 37_1149
10ª
AZD4547_1135
FGFR_3831_1422
10º
0.2
IGFR_3801_1430
-min cono
BX795_1037
Talazoparib_1259
10
0.0
Cisplatin_1005
FEN1_3940_1419
ranked by sensitivity
CHIR-99021_154
1
j
Rucaparib_1175
Docetaxel_1007
Cell line (SW13) IC50 values (Elesclomol)
Cell line (SW13) AUC values (Elesclomol)
PARP_9495_1458
1.0
Piperlongumine_1243
102
AUC: 0.422
Alisertib_431
IC50 (uM): 0.00763
LIMK1 inhibitor BMS4_406
101
0.8
Doramapimod_1042
IOX2_1230
Olaparib_1495
IC50 (micromolar)
100
AZD4547_1497
0.6
RO-3306_1052
max conc
AUC
LDN-193189_478
10-
Elesclomol_1031
0.4
GSE19775
GSE33371
GSE10927
GSE12368
TCGA_ACC
GSE19750
GSE76021
GSE76019
GSE143383
GSE90713
Correlation
1
10ª
0.5
0
0.2
-0.5
104
-min conc
-1
104
0.0
ranked by sensitivity
ranked by sensitivity
kinase inhibitors against ACC (sunitinib, cabozantinib, and linsitinib) have been evaluated and have failed to obtain good results,35 but the effect of foretinib on ACC remains unclear. We evaluated the inhibitory effects of foretinib and elesclomol on SW13 (while SW13 may no longer be considered the ACC model, it was the only one analyzed owing to a lack of available information on H295R in the database). Foretinib and elesclo- mol exhibited broad-spectrum inhibitory effects on cancer cell lines. Foretinib and elesclomol may exert strong inhibitory effects on SW13 cells by inhibiting the expression of CDH2 and CDH13. Therefore, these drugs may be effective for the treatment of ACC. We identified the roles of CDH2 and CDH13 in ACC using bioinformatics methods. However, further validation through in vitro and ex vivo experiments is necessary to confirm their relationship.
In summary, our results provide insights into the expression, gene regulatory network, prognostic value, therapeutic targets, and drugs against CDH2 and CDH13 in patients with ACC. Our findings provide a better under- standing of the pathogenesis of ACC and could aid in devising effective treatment strategies. CDH2 and CDH13 may be potential prognostic and therapeutic targets of ACC.
Materials and methods
GEPIA
We used GEPIA (http://gepia.cancer-pku.cn/index.html) to analyze the relationships between gene expression, tumor pathological stages, and prognosis. The screening criteria were as follows: (1) genes: CDH2 and CDH13; (2) dataset: ACC; and (3) 77 patients; threshold-setting conditions: P-value cutoff = 0.05. The Student’s t-test was used to ana- lyze the expression of CDH2 and CDH13 in ACC. Kaplan - Meier curves were used to analyze the prognosis of patients with ACC.24
UALCAN
UALCAN (http://ualcan.path.uab.edu/analysis.html) is a comprehensive, user-friendly, and interactive web resource for mining and analysis of cancer data, mainly from The Cancer Genome Atlas (TCGA) database. We used UALCAN to analyze the expression of CDH2 and CDH13 in ACC. The “Expression Analysis” module of the UALCAN database was used to analyze TCGA gene expres- sion data; the screening criteria were set as follows: (1) genes: CDH2 and CDH13; (2) dataset: ACC; (3) 79 ACC patients (31 male and 48 female); threshold setting condi- tions: P-value cutoff = 0.05. The Student’s t-test was used for comparative analysis.24
BEST
BEST (https://rookieutopia.com/app_direct/BEST/) provides a curated database and innovative analytical pipelines to explore cancer biomarkers at a high resolution. Protein expres- sion, immune cell infiltration, candidate agents, and
immunotherapy targeting CDH2 and CDH13 in ACC were analyzed using BEST. The “Clinical association,” “Cell infiltra- tion,” “Immunotherapy,” and “Candidate agents” modules of the BEST database were used to analyze gene expression omnibus and TCGA gene expression data using the following screening criteria: (1) genes: CDH2 and CDH13; (2) dataset: ACC (10 datasets and 508 patients).24
cBioPortal
cBioPortal (http://cbioportal.org) is an online database used for tumor gene mutation analysis. We used cBioPortal to analyze alterations in CDH2, CDH13, and the top 50 ANGs. A total of 75 ACC samples were analyzed, and z-scores for mRNA expression relative to all samples (log RNA Seq V2 RSEM) were obtained using a z-score threshold of ± 2.0.24
STRING and GeneMANIA
STRING (https://string-db.org/cgi/input.pl.) and GeneMANIA (http://www.genemania.org.) are online data- bases used for analyzing gene - protein and PPI networks. STRING was used to build a low-confidence level (0.150) PPI network and screen criteria for species defined as humans. GeneMANIA was used to explore the functions of CDH2, CDH13, and their top 50 ANGs.24
Metascape
Metascape (https://metascape.org) is an online database used to analyze the functions and signaling pathways of genes and proteins. We used Metascape to analyze the functions and signaling pathways of CDH2, CDH13, and their top 50 ANGs.24
LinkedOmics
LinkedOmics (http://www.linkedomics.org/) is a public online platform for analyzing correlations between differentially expressed genes related to tumor target genes and for predict- ing miRNA and kinase targets. It was used to identify kinase targets, miRNA targets, and differentially expressed genes related to CDH2 and CDH13.24
TIMER
TIMER (https://cistome.shinyapps.io/timer/) is an online database used to analyze the relationship between tumor genes and infiltrating immune cells. We used it to analyze the correlation between CDH2 and CDH13 expression and immune cell infiltration.
Genomics of drug sensitivity in cancer analysis
Genomics of drug sensitivity in cancer (http://www. cancerRxgene.org) is a specialized public database for obtain- ing information on potential anticancer drugs. We used this database to identify drugs targeting CDH2 and CDH13 and to predict their anti-ACC activity.24
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This research was funded by postdoctoral Foundation of Guangdong Medical University [4SG22292G] and National Natural Science Foundation of China Youth Science Foundation Program [31101639].
ORCID
Yongli Situ [D http://orcid.org/0000-0003-2244-115X
Data availability statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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