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Citation: Huang R, Guo L, Chen C, Xiang Y, Li G, Zheng J, et al. (2023) System analysis identifies UBE2C as a novel oncogene target for adrenocortical carcinoma. PLoS ONE 18(8): e0289418. https://doi.org/10.1371/journal. pone.0289418
Editor: Romi Gupta, The University of Alabama at Birmingham (UAB), UNITED STATES Received: April 15, 2023 Accepted: July 18, 2023 Published: August 3, 2023
Copyright: @ 2023 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability Statement: The data that support the findings of this study are available from https:// doi.org/10.6084/m9.figshare.23566341.v1.
Funding: This study was supported by the National Natural Science Foundation of China [82274512]; and the 2022 Joint funding of Guangzhou Science and Technology Bureau and Guangdong Provincial hospital [202201020327].the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
RESEARCH ARTICLE
System analysis identifies UBE2C as a novel oncogene target for adrenocortical carcinoma
Renlun Huang1,2º, Lang Guo1,3º, Chiwei Chen1,2, Yuyang Xiang1,4, Guohao Li3, Jieyan Zheng1,2, Yanping Wu5, Xiu Yuan6, Jianfu Zhou1,2, Wenxi Gao3*, Songtao Xiang 1,2*
1 The Research Center of Integrative Cancer Medicine, Discipline of Integrated Chinese and Western Medicine, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China, 2 Department of Urology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China, 3 Department of Urology, Hubei Provincial Hospital of Traditional Chinese Medicine, Hubei University of Chinese Medicine Affiliated Hubei Hospital of Chinese Medicine, Wuhan, Hubei, China, 4 The Second Clinical College of Southern Medical University, Guangzhou, Guangdong, China, 5 First Clinical College and Affiliated Hospital, Hubei University of Traditional Chinese Medicine, Wuhan, Hubei, China, 6 Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
@ These authors contributed equally to this work.
* gwx203@163.com (WG); tonyxst@gzucm.edu.cn (SX)
Abstract
Ubiquitin Conjugating Enzyme 2C (UBE2C) is an emerging target gene for tumor progres- sion. However, the tumorigenic effect and mechanism of UBE2C in adrenocortical carci- noma (ACC) remains unclear. Systematic investigation of the tumorigenic effect of UBE2C may help in understanding its prognostic value in adrenocortical carcinoma. First, we exploited the intersection on DFS-related genes, OS-related genes, highly expressed genes in adrenocortical carcinoma as well as differentially expressed genes (DEGs) between tumor and normal, and then obtained 20 candidate genes. UBE2C was identified to be the most significant DEG between tumor and normal. It is confirmed that high expression of UBE2C was strongly associated with poor prognosis in patients with ACC by analyzing RNA-seq data of ACC obtained from the Cancer Genome Atlas (TCGA) database imple- mented by ACLBI Web-based Tools. UBE2C expression could also promote m6A modifica- tion and stemness in ACC. We found that UBE2C expression is positively associated with the expression of CDC20, CDK1, and CCNA2 using ACLBI Web-based Tools, indicated the hyperactive cell cycle progression present in ACC with high UBE2C expression. In addition, UBE2C knockdown could significantly inhibit the proliferation, migration, invasion, EMT of adrenocortical carcinoma cells as well as the cell cycle progression in vitro. Notably, pan- cancer analysis also identified UBE2C as an oncogene in various tumors. Taken together, UBE2C was strongly associated with poor prognosis of patients with ACC by promoting cell cycle progression and EMT. This study provides a new theoretical basis for the development of UBE2C as a molecular target for the treatment of ACC.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: UBE2C, Ubiquitin Conjugating Enzyme 2C; DEGs, differentially expressed genes; EMT, epithelial-mesenchymal transition; mRNAsi, mRNA expression-based stemness index; ACC, Adrenocortical carcinoma; BLCA, Bladder Urothelial Carcinoma; BRCA, Breast invasive carcinoma; CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, Cholangio carcinoma; COAD, Colon adenocarcinoma; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA, Esophageal carcinoma; GBM, Glioblastoma multiforme; HNSC, Head and Neck squamous cell carcinoma; KICH, Kidney Chromophobe; KIRC, Kidney renal clear cell carcinoma; KIRP, Kidney renal papillary cell carcinoma; LGG, Brain Lower Grade Glioma; LIHC, Liver hepatocellular carcinoma; LUAD, Lung adenocarcinoma; LUSC, Lung squamous cell carcinoma; OV, Ovarian serous cystadenocarcinoma; PAAD, Pancreatic adenocarcinoma; PCPG, Pheochromocytoma and Paraganglioma; PRAD, Prostate adenocarcinoma; READ, Rectum adenocarcinoma; SARC, Sarcoma; SKCM, Skin Cutaneous Melanoma; STAD, Stomach adenocarcinoma; TGCT, Testicular Germ Cell Tumors; THCA, Thyroid carcinoma; UCEC, Uterine Corpus Endometrial Carcinoma; UCS, Uterine Carcinosarcoma.
Introduction
Adrenocortical carcinoma (ACC) is an aggressive tumor originating in the adrenocortical cor- tex with a very low incidence about 0.5-1 per 1 million people [1] and poor prognosis with 5-year overall survival ranging from 16% to 47% [1, 2]. The 5-year survival rate of ACC at stage I is about 81%, but when the disease progresses to stage IV, the 5-year survival rate can be reduced to about 13% [3]. Disease progression is the main reason affecting the prognosis of ACC. Radical surgical excision is the only cure option for primary ACC [4]. However, the recurrence rate of ACC after surgical excision is highly [5]. Although it is prone to metabolic and endocrine toxicity, mitotane remain as the only drug approved for the treatment of advanced adrenocortical carcinoma and postoperative [6]. It follows that there is a lack of other effective drug for the treatment of ACC, so it is urgent to find new therapeutic targets. Hence, deciphering the complex molecular composition of ACC progression and developing targeted therapeutic agents, bioinformatics analysis as a convenient and effective strategy is a good choice to screen the available therapeutic targets.
Ubiquitination of modified proteins is an important cellular mechanism that targets the degradation of abnormal or short-lived proteins and plays an important role in tumor progres- sion [7]. UBE2C is a key member of the E2 ubiquitin-binding enzyme family, encoding pro- teins necessary for the destruction of target proteins [8]. Accumulating evidence suggested that overexpression of UBE2C can promote the proliferation of ACC by promoting the G2/M phase transition [9] and the activation of the Spindle and Kinechore-Associated (SKA) Com- plex [10]. Notably, the KRASG12D mutation can promote the expression of UBE2C to promote cell cycle progression and autophagy in lung cancer, which indicated that UBE2C is an attrac- tive carcinogenic genetic target for lung cancer with KRAS mutations [11]. A comprehensive bioinformatics analysis indicated the effect of UBE2C in promoting tumor progression [12], but the carcinogenic effect of UBE2C in ACC still needs to be further investigated.
In this study, we confirmed that UBE2C is a major response gene that affects the prognosis of ACC. In addition, we also explored the relationship between UBE2C expression and m6A methylation, proliferation-related and metastasis-related pathways in ACC. Moreover, in vitro experiments showed that UBE2C knockdown result in poor prognosis of ACC by inhibiting the proliferation, migration, invasion, epithelial-mesenchymal transition (EMT) of adrenocor- tical carcinoma cells as well as the cell cycle progression. And the pan-cancer analysis sug- gested that UBE2C is an oncogenic gene in various tumors. In conclusion, this study identified UBE2C as a novel oncogenic gene in promoting adrenocortical carcinoma progression and contributing to its poor prognosis.
Materials and methods
Research framework
Fig 1 showed the framework of this study.
Data retrieval, preprocessing, and analysis
Obtained from TCGA database (https://tcga-data.nci.nih.gov/tcga/), GEO database (http:// www.ncbi.nlm.nih.gov/geo), GTEx database (https://www.gtexportal.org/), these significant data were used to detect target genes related to poor prognosis in patients with ACC. These data were analyzed using R packages implemented by R version 4.0.3 (R foundation for statisti- cal computing, 2020). Conveniently, all above data retrieval, preprocessing, and analysis were performed in STRING (https://cn.string-db.org/), ACLBI Web-based Tools (https://www.aclbi. com/) or Xiantao Academic web-based Tools (https://www.xiantao.love/products).
A
B
Data Sources
The main responded gene
NIH
NATIONAL CANCER INSTITUTE GDC Data Portal
GEO Gene Expression Omnibus
Intersection genes related to poor prognosis in ACC
Groep ₿ Normal
Normal vs Tumor
UBE2C as the most DEG
12
Tumer
The diferent espredation of target genes
Öğrelated geres
9
STRING
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GTEx Portal
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The expression of UBE2C related to poor prognosis
10.0
RiskType Hich Stoupa Low groups
I
7.5
Metastasis
5.0
High cxp
25
1.00
Overall survival probability
Log-rank P = 4.930-06 HR(High groups)=9.992
Alive
95%CI(3.722, 26.827)
1.00
II
+
0,75
Recurrenc
- High groups
10
· Dead
- Low groups
0,75
0.50
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0.50
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0.25
995000 753-1.0100
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High groups Low groups
30 40
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05%C43 747-0.062)
e
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0.25
0.50
0.75
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0
2.5
5 7.5 Time (years)
10
12.5
False positive fraction
£2C
IV
pTNM_stage
newTumor
UBE2C
Status
1012
D
UBE2C promoted cell cycle progression and EMT in ACC
ceczr
CDICH
-
URA
si-UBE2C-2
-
-
si-UBE2C-1
0%.
ODC23
si Con
-
A
-
CONAZ
E-cad
A
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6
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A
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COCHE
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-
Vimentin
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snail
-
-
MMP2
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346
4
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GAPDH
A
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SW-13
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Pan-cancer analysis identified UBE2C as an oncogene in various tumors
UBERC log2;TPM-1)
12
**
-
Tumor
Overall survival defined by UBE2C
-
Progression-free survival defined by UBE2C
.5
Normal
=
-
UBE2C 1og2[TPM+1)
Overall Survival defined by UBE2C -
A
:
A
1
Da
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ACC/T=79,N=250;
BUCA(1:408:N-40)
BRCA(T= 1037:10-572)
CESCIT-308,N-223
CHOL(T=36JPG)
COAD(T:45S,Nv820)
DLBC(T:4810-029)
ESCA(T=162.N= 1456)
GBM(T=153/4:2647)
HNSC(T-802:30-44)
KICH(T:63:N=113)
KIRC(T=530,N=161)
KIRP(T=286,N=121)
LAML(T=154:300)
LGG(1-510:30:2642)
LHC(T=371200276)
Porom survival
2
:
TOGA+GTER
ICGA-GTEx
Số 100
12.5
UBE2C log2;TFN+1)
12.5
Disease-free survival defined by UBE2C
UBERC log0( TPM+1)
100
10.0
6
=
-
0 0
LUACHT:5133-37)
LUSC(T=501,A=627)
MESO(TAB6,80)
OV(T-374,8x1801
PAAD(T=178.)=332)
PCPG(T=100N)=))
FRAD(=496:36297)
READ(T=165,N=789)
SARCIT-200,3829
SKCM(T=470)=1810)
STAD(T:3752:391)
TGCT(T=134N-361)
THCA(T=510,Nv/11)
THYM(T=119,N=2)
UCECIT-503,177
UCS(T-98,10-142)
Percent survival
WVM(T=802-0)
=
:
TOGA-GTER
TOGA+GTEx
0
$
Months
=
150
-
| logFC | P.Value | |
|---|---|---|
| UBE2C | 5.236594192 | 1.92E-25 |
| CDC20 | 4.609994351 | 8.72E-21 |
| CDK1 | 4.437658015 | 8.77E-22 |
| CCNA2 | 3.820155771 | 3.44E-18 |
Fig 1. Overview of the research design. (A) Data sources used in this research; (B) The main response genes related to poor prognosis in ACC were analyzed. (C) The expression of UBE2C is positively correlated with advanced clinicopathological characteristics and poor prognosis of ACC. (D) UBE2C expression can promote cell cycle progression and EMT. (E) Pan-cancer analysis identified UBE2C as an oncogene in various tumors.
Venn diagrams
In order to screen out the likely response gene, Venn diagrams were created to analyze the dif- ference between several dataset using a web-based tool (http://www.bioinformatics.com.cn/ static/others/jvenn/example.html).
mRNA expression-based stemness index (mRNAsi)
mRNA expression-based stemness index (mRNAsi) was identified as a measure of the tumor development according to the previously publication-n [13]. Based on the OCLR algorithm [13], the mRNAsi of ACC was calculated using ACC RNA-seq data obtained from TCGA data- base implemented by ACLBI Web-based Tools.
Cell culture
The ACC cell line SW-13 was purchased from the American Type Culture Collection (ATCC, Maryland, USA) and cultured in Leibovitz’s L-15 Medium (L-15, 11415064, Gibco, Grand Island, NY, USA) supplemented with 1% penicillin and streptomycin and 10% fetal bovine serum (Gibco). The culture of SW-13 cells needed to maintain at 37℃.
RNA interference
RNA interference (RNAi) was used to interfere with the expression of UBE2C in SW-13 cells. The siRNAs targeting UBE2C (TranSheepBio, Shanghai, China) and the scramble siRNAs were purchased. And then siRNAs targeting UBE2C were transfected into SW-13 cells using Lipofectamine™ 3000 (L3000015, ThermoFisher SCIENTIFIC) according to the manufactur- er’s instructions.
Cells counting
Cells proliferation was assessed using Neubauer counting chamber. First, RNAi technology was used to interfere with the UBE2C expression in SW-13 cells. Second, the treated SW-13 cells and non-treated SW-13 cells were seeded into the 6-well plates. Then, the SW-13 cells were digested with trypsin and resuspended in PBS. Finally, the proliferation of SW-13 cells was assessed by cell counting using Neubauer counting chamber.
Colony formation assay
The effect of UBE2C on the colony formation capabilities of SW-13 cells was detected as previ- ously described [14]. Briefly, the expression of UBE2C in SW-13 cells were interfered using RNAi technology. The non-treated SW-13 cells and treated SW-13 cells (500 cells/well) were planted in a 6-well plate. The resultant colonies were fixed with 4% paraformaldehyde, and then coomassie blue solution were used to fix and stain the resultant colonies.
Wound-healing assay and transwell assay
Wound-healing assay and transwell assay were performed as previously described [14] to detect the change of migration and invasion capabilities of SW-13 cells, respectively. In wound-healing assay, SW-13 cells in a logarithmic growth phase were collected to seeded in a 6-well plate. A wound was created mechanically using a 1 ml pipette tip. In transwell assay, the basement membrane was simulated using matrigel (354248, Corning, NY, USA).
Western blotting
Western blotting was carried out as previously reported [15]. The antibodies used for western blotting included C-myc (18583S, CST), Cyclin D1 (55506S, CST), UBE2C (14234S, CST), cleased-PARP-1 (5625S, CST), cleased-Caspase-7 (9491T, CST), E-cad (20874-1-AP, protein- tech), N-cad (22018-1-AP, proteintech), Vimentin (10366-1-AP, proteintech), snail (ab216347, abcam), MMP2 (ab92536, abcam), GAPDH (ab8245, abcam).
Statistical analyses
Statistical analyses in this study were carried out using the SPSS 24.0 software (Abbott Labora- tories, Chicago, USA). Data were shown as mean + SD. P-values less than 0.05 was identified to be statistically significant.
Results
UBE2C expression strongly associated with advanced histopathological characteristics and poor prognosis of adrenocortical carcinoma (ACC)
In order to detect the main response gene strongly associated with poor prognosis in patients with ACC, we analyzed the intersection of four gene clusters including DFS-relates genes, OS- relates genes, differentially expressed genes (DEGs), and highly expressed genes. The volcano plot showed the DEGs of the patients with ACC (Fig 2A and S1 Table). After exploiting the intersection, we obtained 20 candidate genes in ACC including CDC20, NUF2, BUB1, CCNA2, LMNB1, PTTG1, FSCN1, MELK, KIF11, CEP55, CDCA3, TROAP, CDKN3, CCNB2, CDT1, AURKB, CDT1, AURKB, BIRC5, UBE2C, CENPM, KIF4A (Fig 2B and S2 Table). To identify the major response gene affecting prognosis in patients with ACC, GO and KEGG pathways enrichment analysis were used to analyze the activated signaling pathways accompanied by the highly expression of these candidate genes (Fig 2C). Notably, UBE2C was the most significant DEG between ACC and normal (Fig 2D and $3 Table). We also found that UBE2C was highly expressed in ACC especially in stage III and stage IV using Xiantao Academic Web-based Tools (Fig 3A). To further study the correlation between UBE2C expression and histopathological characteristics of ACC, we tested UBE2C within the ACC dataset to better describe its func- tionality (Fig 3B and S4 Table). These analyses confirmed that ACC patients with high UBE2C expression tended to have a poor prognosis compared to those with low UBE2C expression (Fig 3C-3E). Taken together, UBE2C was highly expressed and strongly correlated with poor prognosis in patients with ACC.
UBE2C expression promoted m’A methylation and stemness in ACC
In order to understand the mode of UBE2C regulating downstream pathway, we explored the correlation between UBE2C expression and m6A methylation in ACC. Based on the identifica- tion and analysis of the m6A methylation regulators by Juan Xu’s research [16], a circle was used to describe the interaction of expression on 20 m6A regulators in ACC using ACLBI Web-based Tools (Fig 4A). And then the expression levels of UBE2C were categorized into two groups including G1 group (above 75th percentile of UBE2C expression) and G2 group (below 25th percentile of UBE2C expression). The result showed that G1 group contained 16 significantly overexpressed m6A regulators compared with G2 group (Fig 4B and S5 Table). We found that UBE2C expression was positively associated with the mRNAsi in ACC using ACLBI Web-based Tools, which suggested that UBE2C may lead to poor prognosis in patients with ACC via promoting self-renewal of ACC stem cells (Fig 4C and 4D).
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CDT1
PTTG1
CDKN3
KIF11
KIF4A
CEP55
MELK
BUB1
LMNB1
BIRC5
NUF2
CENPM
CCNB2
CDC20
TROAP
CCNA2
AURKB
CDCA3
FSCN1
0
UBE2C
FSCN1
PTTG1
CDC20
CCNA2
BIRC5
CDT1
LMNB1
AURKB
CCNB2
TROAP
MELK KIF11
CEP55
CDKN3
CENPM
NUF2
CDCA3
KIF4A
BUB1
https://doi.org/10.1371/journal.pone.0289418.g002
A
B
8
**
12
The expression of UBE2C Log2 (FPKM+1)
The expression of UBE2C Log2 (FPKM+1)
I
10
Metastasis
6
High exp
8
**
Alive
II
**
Recurrence
4
6
ns
4
III
Low exp
2
Dead
2
IV
0
0
Tumor free
With tumor
Stage I
Stage II Stage III Stage IV Pathologic stage
pTNM_stage
newTumor
UBE2C
Status
Tumor status
C
D
1.00
Log-rank P = 4.93e-06
10.0
RiskType
. High groups
Log2(TPM+1)
Overall survival probability
HR(High groups)=9.992 95%CI(3.722, 26.827)
. Low groups
0.75
!!
7.5
- High groups
+
- Low groups
0.50
5.0
0.25
2.5
0.00
Median time:2.6
Status
Groups
High groups
39
17
4
2
0
0
Alive
· Dead
Low groups
40
33
8
4
10
20
2
0
2.5
5
7.5
10
12.5
Time
E
Time (years)
5
1.00
0
True positive fraction
0.75
0.50
Type
UBE2C
1-Years,AUC=0.885
95%CI(0.753-1.018)
0.25
3-Years,AUC=0.936
95%CI(0.888-0.985)
5-Years,AUC=0.855
0.00
95%CI(0.747-0.962)
z-score of expression
-1012
0.00
0.25
0.50
0.75
1.00
False positive fraction
https://doi.org/10.1371/journal.pone.0289418.g003
UBE2C expression activated the molecular pathways of cell cycle, proliferation, and metastasis
At first, a previously published geneset [17] was analyzed for understanding the correlation between UBE2C expression and the activation of several signaling pathway in ACC implemented by ACLBI
A
B
Group
Group G1 G2
METTL3 ***
7.47e-04
3
ZC3H13*
3.26e-02
2
GF2BP GF2BP
8.34e-08
1
F2BP3
RBM15 ***
WTAP ***
8.36e-04
0
RBMX
HORNPO
RBM15B ***
7.94e-05
-1
YTHDO
HINRN
VIRMA ***
5.31e-04
-2
B1
-3
YTHDF3 **
2.64e-03
YTHDO
FTO
RBMX ***
1.13e-06
HNRNPC ***
2.40e-06
YTHDF
ALKBH5
Writers
HNRNPA2B1 ***
3.29e-07
Readers
YTHDC1 **
2.91e-03
YTHDF2
31113
Erasers
YTHDF2 **
1.16e-03
Positive correlation with P < 0.001
YTHDF1 **
4.27e-03
ALKBH5 **
2.39e-03
YTHDF3
WIAP
Negative correlation with P < 0.001
METTL14
2.01e-01
METTL14
IRMA
logrank test, P
YTHDC2
1.65e-01
METTL3
3.41e-01
RBMIS
BM15B
FTO
☐
0.05
☐ 0.001 ☐ le-05
IGF2BP2 **
6.14e-03
IGF2BP1 **
5.99e-03
IGF2BP3 **
9.77e-03
C
D
log.(S)=10.69, p=1.64e-05, @spearman= 0.46, Cl950% [0.26, 0.63], “pairs = 79
wilcox.tests p=2.1e-05
1.00
1.00 -
0,75
0.75 -
..
mRNAsi score
Group
mRNAsi
..
0.50
G1
G2
0.50 -
..
..
0.25 -
0.25
0.00 -
0.00
2.5
5.0
7.5
10.0
Log2 (UBE2C expression)
G1
G2
https://doi.org/10.1371/journal.pone.0289418.g004
Web-based Tools, and the results showed that UBE2C expression was positively correlated with the activation of cell cycle, proliferation, and metastasis (Fig 5). To further explore the downstream sig- naling pathways activated by UBE2C, we analyzed DEGs between G1 group and G2 group as described above (Fig 6A-6C and S6 Table). Based on STRING protein-protein interaction network [18], we found that there are 10 candidate proteins closely related to UBE2C protein expression (Fig 6D). And based on LogFC and P-value (LogFC ≥ 2 or LogFC ≤-2, P-value < 0.05), we identified 349 significantly DEGs (Fig 6E). And then, we screened out a cluster containing 4 genes related to cell cycle by intersecting the two data sets (Fig 6E and S7 Table).
log.(S)=11.48.p = 0.091. [[pouren”=0.19, Class[-0.40,0.04], figar=79
log. (S) = 11.13, p= 0.134, Bspearman=0.17, Class[-0.06,0.38], figars=79
log.(S) = 7.84,p = 1.77e-48, Buceomen” 0.9Z Clayu[0.95,0.98]. rgan=79
log.(S) - 11.06, p=0.045, Pracaman”0.23, Clos.[0.00,0.43], Fpsis ” 79
0.5.
0.8.
1.00.
Tumor_Inflammation_Signature
Cellular_response_to_hypoxia
0.80+
Tumor_proliferation_signature
0.4.
0.6.
0.75.
EMT_markers
..
..
0.78+
0.3.
0.50.
0.4 .
0.76+
..
0.2.
0.25.
..
0.2.
2.5
Log2 (UBE2C TPM + 1)
5.0
7.5
10.0
0.74+
2.5
Log2 (UBE2C TPM + 1)
5.0
75
10.0
2.5
5.0
7.5
10.0
2.5
7.5
10.0
Log2 (UBE2C TPM + 1)
Log2 (UBE2C TPM + 1)
5.0
log.(S) = 11.11,p = 0.099, Bspearman=0.19, Class[-0.04,0.40], ripan= 79
log.(S) = 11.12.p = 0.124, Bspcama= 0.17, Class[-0.05,0.39], ngain=79
log.(S) = 11.47, p = 0.136, 1 spearman= = 0.17, Class[-0.38, 0.06], ngan= 79
log.(s) =9.79, p=1.74c-17, 8 spearman= 0.78, Class/0.67, 0.86], [gain=79
0.75-
0.75.
0.3.
0.70-
0.78.
ECM-related_genes
0.70+
0.2.
Angiogenesis
0.65-
8
8
Apoptosis
DNA_repair
0.76-
0.60-
0.1.
0.65+
%
&
.
0.55-
0.74.
. .
..
9
0.0.
0.50-
..
0.60-
%
0.72.
2.5
5.0
75
10.0
0.45.
Log2 (UBE2C TPM + 1)
2.5
5.0
7.5
10.0
2.5
5.0
7.5
10.0
2.5
5.0
7.5
Log2 (UBE2C TPM + 1)
Log2 (UBE2C TPM + 1)
10.0
Log2 (UBE2C TPM + 1)
log.(S) = 8.64,p= 1.87e-35, @speaman=0.93, Class.(0.89,0.96], ngan= 79
log.(S) = 11.48, p = 0.129, 6spramun= = 0.17, Class[-0.38, 0.06], ngain= 79
log.(s) = 11.06, p =0.047, [spearman=0.22, CI gos [0.00,0.43], ngain= 79
log.(S) = 11.50,p = 0.082, spearman == 0,20, Class[-0.41,0,03], (gars= 79
0.8.
0.6.
0.68+
0.5.
0.7.
~0.66+
0.70.
G2M_checkpoint
Inflammatory_response
PI3K_AKT_mTOR_pathway
P53_pathway
0.4.
0.64.
60.6.
0.65.
..
0.3.
0.62+
0.5.
0.2.
.
2.5
5.0
7.5
10.0
2.5
5.0
7.5
10.0
2.5
5.0
7.5
10.0
2.5
5.0
Log2 (UBE2C TPM + 1)
7.5
10.0
Log2 (UBE2C TPM + 1)
Log2 (UBE2C TPM + 1)
Log2 (UBE2C TPM + 1)
log.(s) = 9.50, p= 1.860-15, Papaman=0.75, Cios[0.63,0.83]. mais = 79
log.(s) = 11.37, p= 0.621, pipewww/F-0.06, Class.[-0.28,0.17], [pains=79
log, (s) = 11.55,p=0.020, Tapewwan == 0.26, Class[-0.46,-0.04], [gains=79
log.(S) = 11.65, p=2.74e-04, Psptamar?” -0.40, Clos[-0.57,-0.19], rgan=79
IL-10_Anti-inflammatory_Signaling_Pathway
Genes_up-regulated_by_reactive_oxigen_species_(ROS)
0.76.
0.900·
0.8.
0.81.
0.875·
0.72.
..
0.7.
MYC_targets
%
-
TGFB
9
:
0.78.
0.850.
0.68+
0.6.
0.75.
..
0.825·
0.5.
0.64-
..
2.5
5.0
7.5
10.0
2.5
5.0
7.5
10.0
2.5
5.0
7.5
10.0
2.5
5.0
7.5
Log2 (UBE2C TPM + 1)
10.0
Log2 (UBE2C TPM + 1)
Log2 (UBE2C TPM + 1)
Log2 (UBE2C TPM + 1)
log.(s) = 9.19.p = 9.310-27, Pipewwan=0.88, Cl spu.[0.82,0.92]. ngang=79
log.(S) = 11.01.p=0.018, Bupewne == 0.27, Chytr: [0.04,0,46], ngang= 79
log.(S) = 11.04.p=0.000, Pipeamas =0.24, Clos [0.02 0.45]. Physis = 79
0.75.
DNA_replication
0.70-
Collagen_formation
0.5.
Degradation_of_ECM
0.4.
..
..
0.65.
8 0.4.
0.3+
0.60.
..
A
0.55.
0.3.
25
5.0
7.5
Log2 (UBE2C TPM + 1)
10.0
2.5
7.5
Log2 (UBE2C TPM + 1)
5.0
10.0
2.5
7.5
Log2 (UBE2C TPM + 1)
5.0
10.0
https://doi.org/10.1371/journal.pone.0289418.g005
A
B
25
P
1
UBE2C
group
3
group
Down-regulation
0
2
GI
None
TROAP 0
G2
Up-regulation
1
CDKI
8
0
20
0
MK167
00
-1
€
O
PBK
-Log10 (P-value)
CEP55
90
MYBL.2
-2
O
-3
15
0
TKI
.
.
10
5
0
-1
0
1
5
Log2 (Fold change)
C
GO (Up)
GO (Down)
viral transcription
xenobiotic metabolic process
viral gene expression-
terpenoid metabolic process-
sister chromatid segregation-
steroid metabolic process-
ribosome biogenesis-
retinol metabolic process
ribonucleoprotein complex biogenesis-
-log10(p.adjust)
retinoid metabolic process-
Count
regulation of mitotic cell cycle phase transition-
retinoic acid metabolic process-
3
regulation of mRNA metabolic process
response to xenobiotic stimulus
6
regulation of cell cycle phase transition-
16
oxidative demethylation- ·
9
nuclear-transcribed mRNA catabolic process-
organic acid biosynthetic process-
12
nuclear division-
monocarboxylic acid biosynthetic process-
nuclear chromosome segregation-
Count
long-chain fatty acid metabolic process-
-log10(p.adjust)
ncRNA metabolic process-
lipid modification
mitotic sister chromatid segregation-
100
3.5
mitotic nuclear division-
125
fatty acid metabolic process-
fatty acid biosynthetic process-
3,0
mRNA catabolic process-
150
175
drug metabolic process
2.5
chromosome segregation
200
diterpenoid metabolic process
2.0
RNA splicing, via transesterification reactions-
cellular response to xenobiotic stimulus-
RNA splicing
cellular modified amino acid metabolic process
RNA catabolic process
cellular hormone metabolic process
DNA replication
carboxylic acid biosynthetic process-
0.02
0.03
0.04
0.05
0.06
0.025
0.050
0.075
0.100
Enrichment Ratio
Enrichment Ratio
0.125
D
E
CDC27
UBA1
CDK1
CDC23
CDC20
ANAPC10
UBE2C
A
345
4
7
ANAPC2
CCNA2
CDC16
ANAPC11
?
| logFC | P.Value | |
|---|---|---|
| UBE2C | 5.236594192 | 1.92E-25 |
| CDC20 | 4.609994351 | 8.72E-21 |
| CDK1 | 4.437658015 | 8.77E-22 |
| CCNA2 | 3.820155771 | 3.44E-18 |
Fig 6. UBE2C expression with the activation of the molecular pathways of cell cycle, proliferation, and metastasis. (A) Volcano plot: blue dots point different down-regulated genes and red dots point different up-regulated genes; (B) Heatmap: DEGs between G1 group and G2 group. G1 group was defined as above 75th percentile of UNE2C expression, and G2 group was defined as below 25th percentile of UNE2C expression as described above; (C) GO enrichment analysis of these genes; (D) 10 candidate proteins related to UBE2C protein were identified using STRING database; (E) Venn diagrams: the intersection between 11 candidate proteins obtained from STRING database and 349 candidate genes obtained from significant DEGs between G1 group and G2 group.
https://doi.org/10.1371/journal.pone.0289418.g006
Pan-cancer analysis identified UBE2C as an oncogene in various tumor
In this paper, pan-cancer analysis using TCGA dataset and GTEx dataset were all implemented by ACLBI web-based Tools. We found that UBE2C is almost overexpressed in all tumor includ- ing ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, UCEC, and UCS by means of the comparison on UBE2C expression between the tumor and normal obtained from TCGA dataset and GTEx dataset, respectively (Fig ZA). Moreover, UBE2C expression will leads to poor prognosis in various tumors (Fig 7B and 7C). More importantly, we found that UBE2C expression is positively associated with hyperactive MSI as well as TMB, especially in ACC (Fig 7D). This may imply that UBE2C can be used as a predictor of the efficacy of immunotherapy for patients with ACC. The relationship between UBE2C expression and immune infiltration in various tumors were also explored. And we found that UBE2C overexpression was positive correlation with the infiltration of Th1, Th2 as well as negative correlation with the infiltration of Treg, M2 macrophages (Fig 7E). But the relation between the Th1/Th2 immune balance and ACC progression need more studies. In conclusion, we found that UBE2C was highly expressed in various tumors and strongly associ- ated with poor prognosis, but also predicted the hyperactivity of MSI and TMB, especially in ACC.
UBE2C knockdown weakened proliferation, migration, and invasion via inhibiting ACC cycle progression and EMT in vitro
At first, we assessed the effects of UBE2C on adrenocortical carcinoma cell growth. Morpho- logical changes of SW-13 cells showed that transfected with control or UBE2C siRNAs strongly inhibited proliferation of adrenocortical carcinoma cell (Fig 8A). The clone formation assays further verified the promoting effect of UBE2C on the proliferation of SW-13 cells (Fig 8B). To test our hypothesis that UBE2C is involved in tumor invasion, siRNAs were used to transiently silence UBE2C expression. And then the effect of UBE2C on migration and invasion in adre- nocortical carcinoma cell were further investigated. Transwell assay results demonstrated that UBE2C siRNAs stimulation remarkably suppressed the invasion of adrenocortical carcinoma cell compared to treated with a scramble siRNA (Fig 8C). We assessed migration capacity through wound-healing assays in vitro with taking a picture and calculating the distance trav- elled at a 0-hour, 24-hour and 48-hour time point. Compared to SW-13 cells treated with a scramble siRNA, those receiving UBE2C siRNAs stimulation travelled a shorter distance (Fig 8D). For tumor invasion requiring both degradation of ECM components and cell migration, we performed western blotting and found that UBE2C knockdown indeed inhibited EMT of adrenocortical carcinoma cells, manifested as the decreased expression of N-cad, Vimentin, snail, MMP2 and increased expression of E-cadherin (Fig 8E). Moreover, western blotting indicated that UBE2C siRNAs can inhibit proliferation, inhibit damage repair of DNA, and induced apoptosis in ACC cells, characterized by the increased expression of cleased-Caspase- 7 as well as the decreased expression of cleased-PARP-1, C-myc, cyclin D1 (Fig 8F). In conclu- sion, these results demonstrated that UBE2C knockdown can weaken proliferation, migration, and invasion of adrenocortical carcinoma cell via inhibiting cycle progression and EMT.
Discussion
Adrenocortical carcinoma is a rare tumor with a very low incidence. However, patients with adrenocortical carcinoma usually have poor prognosis. When patients with adrenocortical car- cinoma progressed from the stage I to the stage IV, the 5-year survival rate would be reduced
A
B
UBE2C log2(TPM+1)
12
**
UBE2C log2(TPM+1]
Overall survival defined by UBE2C
10.0
Tumor
8
7.5
Normal
9
5.0
PIHRAD
BRCA(T=1097;N=572)
Percent survival
nhigh()=2376
4
2.5
0
0.0
ACC(T=79;N=258)
BLCA(T=408;N=40)
CESC(T=306;N=22)
CHOL(T=36;N=9)
COAD(T=455;N=820)
DLBC(T=48;N=929)
ESCA(T=162;N=1456)
GBM(T=153;N=2647)
HNSC(T=502;N=44)
KICH(T=65;N=113)
KIRC(T=530;N=161)
KIRP(T=288;N=121)
LAML(T=151;N=0)
LGG(T=510;N=2642)
LIHC(T=371;N=276)
2
0
0
50
100
50 200
250
300
350
Months
TCGA+GTEx
TCGA+GTEx
Disease-free survival defined by UBE2C
12.5
UBE2C log2[TPM+1)
12.5
UBE2C log2(TPM+1)
9
10.0
10.0
Logrank pup
7.5
7.5
2
Percent survival
5.0
5.0
0
mow)-2375
2.5
2.5
2
0.0
0.0
LUAD(T=513;N=637)
LUSC(T=501;N=627)
MESO(T=86;N=0)
OV(T=374;N=180)
PAAD(T=178;N=332)
PCPG(T=180;N=3)
PRAD(T=496;N=297)
READ(T=165;N=789)
SARC(T=260;N=2)
SKCM(T=470;N=1810)
STAD(T=375;N=391)
TGCT(T=134;N=361)
THCA(T=510;N=711)
THYM(T=119;N=2)
UCEC(T=543;N=177)
UCS(T=56;N=142)
UVM(T=80;N=0)
영
8
p
50
100
150
200
250
Months
TCGA+GTEx
TCGA+GTEx
C
D
Correlation between UBE2C and MSI
Overall survival defined by UBE2C
Progression-free survival defined by UBE2C
ACC
MESO
-log10(p-value)
SE
SLAD
TNM
BLCA
2
Correlation
· 0.05
PAAD GBM
· 0.10
· 0.15
CHOL
sapr
· 0.20
LUAD
PRAD
ESCA
TGCT
KIRP
SKCM
OV
HINSC
LGG
BET
READ
DIBe
BLOC
CESC
COAD
-0.1
0.0
0.1
0.2
Correlation between UBE2C and TMB
PAAD
-log10(p-value)
LUAD
7.5
15
BRCA
PRAD
Y
2.5
SARC
KICH LGG
Correlation
STAD
· 0.2
CHOL
· 0.4
BLCA
· 0.6
LUSC
MESO
KIRC
UCS
SKCM
UVM
PAP
GBM
PCPG
READ
CESC
HN &
UCEC
TGCT
0.036033456789 11 13 15 17 19 Hazard Ratio
0.3125
LIHC
2 3 4
Hazard Ratio
ESCA
KIRP
COAD
THYM
E
-0.50
-0.25
0.00
0.25
0.50
stroma score
* p < 0.05
microenvironment score
immune score
** p < 0.01 p<0.001
T cell regulatory (Tregs)
T cell gamma delta
T cell NK
Correlation
T cell CD8+ naive
T cell CD8+ effector memory
0.8
T cell CD8+ central memory
0.4
T cell CD8+
0.0
T cell CD4+ naive
T cell CD4+ memory
-0.4
T cell CD4+ effector memory ***
T cell CD4+ central memory
T cell CD4+ Th2
T cell CD4+ Thi ***
T cell CD4+ (non-regulatory)
Plasmacytold dendritic cell
Neutrophil NK cell
Myeloid dendritic cell activated
Myeloid dendritic cell
Monocyte
Mast cell **
Macrophage M2
Macrophage M1
Macrophage
Hematopoietic stem cell
Granulocyte-monocyte progenitor
Eosinophil
Endothelial cell
Common myeloid progenitor
Common lymphoid progenitor
Class-switched memory B cell
B cell plasma
B cell naive
B cell memory
B cell
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
XCELL
| Cancer | Pvalue | Hazard Ratio(95% CI) | |
|---|---|---|---|
| ACC | <0.0001 | 7.53042(3.00242.18.88713) | |
| BLCA | 0.541 | 0.91236(0.6799,1.2243) | |
| BRCA | 0.0396 | 1.40483(1.08626,1.94197) | |
| CESC | 0.9301 | 1.02098(0 64194,1.62385) | |
| CHOL | 0.3708 | 1.54796(0.5946,4.02991) | |
| COAD | 0.6492 | 1.0948(0.74103,1.61745) | |
| DLBC | 0.6835 | 0.74063(0.17482,3.13756) | |
| ESCA | 0.1919 | 1.39263(0.84685.2.29029) | |
| GBM | 0.6589 | 0.92258(0.64507.1.31947) | |
| HNSC | 0.6072 | 1.07269[0.82089,1.40173) | |
| KICH | 0.1168 | 3.5179(0.73047,16.94203) | |
| KIRC | <0.0001 | 2.35792(1.70902,3.25318) | H |
| KIRP | 30-04 | 3.56766(1.80248,7,06149) | |
| LAML | 0.3637 | 0.82274(0.54005.1.25342) | |
| LGG | <0.0001 | 2.4858(1.69163,3.65282) | - |
| LIHC | 0.0325 | 1.461 16(1.03203,2.06873) | |
| LUAD | 0.0049 | 1.5263(1.13703,2.04883) | |
| LUSC | 0.5296 | 0.91685(0.69944.1.20184) | |
| MESO | <0.0001 | 3.32015(2.01772.5.4633) | |
| 0.8005 | 1.03416(0.79701,1.34186) | ||
| PAAD | 0,007 | 1.76976(1.16844.2.68054) | |
| PRAD | 0.129 | 2.9522(0.72977.11.94274) | |
| READ | 0.7085 | 1.16399(0.52513,2.5801) | |
| SARC | 0.1907 | 1.30256(0.87672,1.93524) | |
| SKCM | 0,017 | 1.38793(1.06035.1.81671) | |
| STAD | 0.7982 | 0.95826(0.69115.1.3286) | |
| TGCT | 0.7445 | 0.71632(0.09633.5.32657) | |
| THCA | 0.4223 | 1.51447(0.54951,4.17397) | |
| THYM | 0.0357 | 0.17924(0:03603,0,89173) | |
| UCEC | 0.0061 | 1.83146(1.1881,2.82321) | |
| UCS | 0.6695 | 0.86293(0.43849,1.69823) | |
| UVM | 0.0533 | 2.35483(0.988,5.61257) | TTTTTTTTTTTTTTm |
| Cancer | Pvalue | Hazard Ratio(95% CI) | ||
|---|---|---|---|---|
| ACC | 4c-04 | 3.25309(1.68492.6.28076) | ||
| BLCA | 0.6756 | 1.06588(0,7906,1,437) | ||
| BRCA | 0.0215 | 1.47137(1.05868.2.04494) | ||
| CESC | 0.2142 | 1.34126(0.84395.2.13161) | ||
| CHOL | 0.1997 | 1.80151(0.73269.4.42946) | ||
| COAD | 0.5169 | 0.88918(0.62334,1.2684) | ||
| DLBC | 0.9472 | 1.04149(0,31253.47105) | ||
| ESCA | 0.1571 | 1.38313(0.88258.2.16757) | ||
| GBM | 0.0876 | 0.73102(0.51027.1.04727) H | ||
| HNSC | 0.1837 | 1.2119(0.91288.1.60888) | ||
| KICH | 0.2943 | 1.93051(0.56472,6.59956) | ||
| KIRC | 40.0001 | 3.19513(2.24956.4.53815) | ||
| KIRP | <0.0001 | 3.51068(1.94117.6.34919) | ||
| LGG | 50-04 | 1.6695(1.24955,2 2306) | ||
| LIHC | 3c-04 | 1.72377(1.28171.2.31829) | - | |
| LUAD | 0.1182 | 1.24388(0,94596,1.63563) | ||
| LUSC | 0.7139 | 1.06272(0.76763.1.47126) | ||
| MESO | 0.0018 | 2.40485(1.38489.4.17601) | ||
| OV | 0.3926 | 0.90116(0.70987,1.14399) | ||
| PAAD | 0.0057 | 1.72929(1.17267,2.55012) | ||
| PCPG | 0.0682 | 2.41501(0.93623.6.22949) | ||
| PRAD | <0.0001 | 2.70933(1.74072,4 2169) | ||
| READ | 0.6603 | 1.1564(0.6049.2.21973) | ||
| SARC | 0.0494 | 1.39778(1.00085.1.95214) | ||
| SKCM | 0.0434 | 1.20032(1.00683.1.57363) | ||
| STAD | 0.4596 | 0.87545(0.61535.1.2455) | ||
| TGCT | 0.2739 | 1.46299(0.74002.2.89226} | ||
| THCA | 0.0021 | 2.50265(1.39535.4.48865) | ||
| THYM | 0.5933 | 1.27924(0.51809,3.15867) | ||
| UCEC | 30-04 | 1.99268(1 376.2.88576) | ||
| UCS | 0.3388 | 1.37933(0.71363.2.66602) | ||
| UVM | 0.0046 | 3.15255(1.42421,6.97831) | ||
Fig 7. Pan-cancers analysis of UBE2C. (A) the different expression of UBE2C in 33 tumors. (B) the overall survival and disease-free survival defined by UBE2C in pan-cancer. (C) Forest plot: P-value, risk factor HR, and 95% confidence interval of the overall survival and disease-free survival defined by UBE2C in 33 tumors. (D) Spearman correlation analysis of UBE2C expression with MSI and TMB. The abscissa and the size of the dots in the figure represent the correlation coefficient, and the ordinate represent different tumors. The different colors represent the P-value; (E)
Heatmap plot: spearman correlation analysis between immune infiltration scores and UBE2C expression in 33 tumor tissues using XCELL method. * P < 0.05, ** P < 0.01, *** P < 0.001, the asterisk represents the degree of statistical significance (*P).
https://doi.org/10.1371/journal.pone.0289418.g007
by more than 60%. Unfortunately, only mitotane with limited therapeutical effects can be used for patients with advanced adrenocortical carcinoma or postoperative recurrence. Hence, it is a major challenge for the scientific community to develop more safe and more effective anti- ACC drugs. A new understanding of pathogenesis and an in-depth exploration of novel drug targets are urgently needed in ACC to find anti-cancer drugs.
It is helpful to excavate potential oncogene and to better integrate biological evidence for understanding the mechanism of tumor progression using web-based tools or database. We screened out 20 oncogenes including CDC20, NUF2, BUB1, CCNA2, LMNB1, PTTG1, FSCN1, MELK, KIF11, CEP55, CDCA3, TROAP, CDKN3, CCNB2, CDT1, AURKB, CDT1, AURKB, BIRC5, UBE2C, CENPM, KIF4A by analyzing TCGA data sets and GTEx data sets implemented by ACLBI Web-based Tools. We not only found these 20 oncogenes were highly expressed in tumor tissues, but also study the differential expression levels of these genes between tumor and normal. These results identified UBE2C as the significant DEG between tumor and normal.
Ubiquitin-proteasome system (UPS) is one of the major pathways for protein degradation through a series of steps such as substrate recognition, ubiquitin coupling and proteasome- ubiquitinated substrate degradation [19, 20]. Unlike autophagy, UPS primarily degrades single unfolded peptides with capacity of entering narrow channels of the proteasome [21]. Human UBE2C (also known as UBCH10) is located on chromosome 20q13.12 [22]. UBE2C can pro- mote anaphase-promoting complex (APC/C) specifically combine with protein degradation pathway, and plays a key role in regulating the cell cycle, apoptosis, and transcriptional pro- cesses by catalyzing degradation of proteins [8, 23, 24]. APC/C, a multi-subunit complexes, can accelerate the mitotic progression from metaphase to anaphase by catalyzing the multi- ubiquitination of key regulatory factors in the cell cycle [25]. Hence, the abnormal expression of UBE2C will cause the hyperactivity of the APC/C-dependent ubiquitination in tumor cells, which accelerates the cell cycle and eventually leads to malignant transformation. It is well known that the KRASG12D target have ushered in a new era in the treatment of KRAS-mutant oncology drugs [26]. Remarkably, a study discovered that KrasG12D mutation regulates the cell cycle by promoting the expression of UBE2C, which ultimately promotes tumor cell growth [11]. That validates UBE2C as a potential therapeutic target for lung cancer with KRASG12D mutations. Accumulating evidence also suggested that the high expression of UBE2C is strongly related to poor prognosis in patients with breast cancer [24, 27], lung adenocarcinoma [28] or gastric cancer [29]. On the one hand, based on the description of hallmarks of cancer proposed by Professor Douglas Hanahan [30], it has been confirmed that UBE2C can promote tumor growth [31], angiogenesis [32], tumor metastasis [33, 34], anchorage-independent growth [35], stemness [36], resist apoptosis [37, 38], induce immunosuppressive microenvi- ronment [39], and enhance glycolytic activity [40, 41]. On the other hand, high expression of UBE2C will lead to treatment failure attributed to overactive anchorage-independent growth and reduced oxidative stress-induced cell apoptosis resulting in chemotherapy resistance [35], and reduce radiosensitivity [38], respectively. In short, a series of studies shown that UBE2C is highly expressed in tumors tissues and able to promote tumor progression.
In this study, we identified UBE2C as a potential oncogene after analyzing the TCGA data- set implemented by ACLBI Web-based Tools. Interestingly, we found that UBE2C only signifi- cantly expressed in patients with ACC stage III and stage IV using Xiantao Academic Web-
A
150
SW-13
siCon
si-UBE2C-1
si-UBE2C-2
Cell number (*104)
100
50
1
0
siControl
si-UBE2C-1
si-UBE2C-2
B
Clonies number (SW-13)
si-UBE2C-1
si-UBE2C-2
200-
siCon
150
100-
SO-
-
-
0
siCon
si-UBE2C-1
si-UBE2C-2
C
SW-13
D
SW-13
siCon
0h
24h
si-UBE2C-1
48h
si-UBE2C-2
siCon
si-UBE2C-1
si-UBE2C-2
E
SW-13
siCon
si-UBE2C-1
si-UBE2C-2
F
SW-13
SW-13
siCon
si-UBE2C-1
E-cad
si-UBE2C-1
si-UBE2C-2
si-UBE2C-2
1.00
1.20
1.87
N-cad
siCon
UBE2C
1.00 0.51 0.53
C-myc
1.00
0.58 0.54
Vimentin
cleased-PARP-1
1.00
0.53
0.39
1.00
0.67
0.65
1.00 0.71 0.75
snail
Cyclin D1
cleased-Caspase-7
1.00
0.55
0.48
1.00
0.36
0.38
1.00 1.78 1.69
MMP2
GAPDH
GAPDH
1.00
0.88
0.91
GAPDH
https://doi.org/10.1371/journal.pone.0289418.9008
based Tools. Hence, patients with Advanced ACC or postoperative recurrence ACC are lack of effective treatment strategies. UBE2C is mainly expressed in advanced ACC, which may be a theoretical basis for solving these contradictions in ACC. Moreover, we evaluated the correla- tion between the expression levels of UBE2C and the function of m6A in ACC. At first, we identified the m6A methylation modification patterns in ACC mediated by 20 regulators using ACLBI Web-based Tools (Fig 4A). Accumulating evidence showed that more than 60% RNA modifications in mammalian cells are methylation modification, and m6A methylation is the most common, abundant, and conservative internal RNA modification [42]. M6A methylation can speed up transcription and participate in proteins translation and mRNA degradation [43-45]. m6A methylation modification can directly or indirectly regulate target genes, which can affect biological behaviors such as the proliferation, metastasis, and immune escape of tumor cells [46]. Hence, explaining the relationship between UBE2C and m6A regulators in ACC can enhance our understanding of the mechanism of UBE2C promoting the tumor development. In vitro experiments data showed that suppressing UBE2C expression can inhibit metastasis and proliferation, weaken DNA damage repair, and induce the apoptosis. Our preliminary results suggested that UBE2C can activate the C-myc signaling pathway to promote cell cycle progression as well as activate the snail signaling pathway to induce EMT in ACC. A study uncovered that PARP-1 can activate the snail signaling pathway to induce EMT in melanoma cells [47]. These results cued that the mechanism of UBE2C promoting prolifera- tion and metastasis of ACC will be more complicated, and further investigations are still needed to clarify this important issue.
Conclusions
In summary, the present study demonstrates that UBE2C is strongly correlation with poor prognosis in patients with ACC via promoting cell cycle progression and EMT. This study not only uncovers the mechanism of UBE2C in promoting ACC proliferation and metastasis but also provides a novel rationale for developing UBE2C as a potential molecular target for the treatment of advanced ACC.
Supporting information
S1 Table. The DEGs of the patients with ACC. The differentially expressed genes between patients with ACC and normal were analyzed. RNA-sequencing expression profiles of 79 patients with ACC were downloaded from the TCGA database. And RNA-sequencing expres- sion profiles of 258 normal were downloaded from GTEx database. These data were analyzed using R packages implemented by R version 4.0.3 in ACLBI Web-based Tools (https://www. aclbi.com/). “LogFC ≥ 2 or LogFC ≤-2, P-value < 0.05” was defined as a screening criterion for identifying differentially expressed genes.
(XLSX)
S2 Table. 20 candidate genes in ACC were identified. The intersection of four gene clusters was analyzed including DFS-relates genes, OS-relates genes, differentially expressed genes (DEGs), and highly expressed genes. (XLSX)
S3 Table. UBE2C was the most significant DEG between ACC and normal. RNA-sequenc- ing expression profiles of 79 patients with ACC were downloaded from the TCGA database. And RNA-sequencing expression profiles of 258 normal were downloaded from GTEx data- base. These data were analyzed using R packages implemented by R version 4.0.3 in ACLBI
Web-based Tools (https://www.aclbi.com/). (XLSX)
S4 Table. UBE2C expression within the ACC dataset. UBE2C expression and corresponding clinical information of 79 patients with ACC were described in detail. (XLSX)
S5 Table. G1 group contained 16 significantly overexpressed m6A regulators compared with G2 group. The result showed that GI group (above 75th percentile of UBE2C expression) contained 16 significantly overexpressed m6A regulators compared with G2 group (below 25th percentile of UBE2C expression).
(XLSX)
S6 Table. DEGs between G1 group and G2 group. The result showed the differentially expressed genes between G1 group (above 75th percentile of UBE2C expression) and G2 group (below 25th percentile of UBE2C expression). (XLSX)
S7 Table. A cluster containing 4 genes related to cell cycle by intersecting the two data sets. The candidate proteins obtained from STRING database; and the significant DEGs between GI group (above 75th percentile of UBE2C expression) and G2 group (below 25th percentile of UBE2C expression) were analyzed from the TCGA dataset. “LogFC ≥ 2 or LogFC ≤ -2, P- value < 0.05” was defined as a screening criterion.
(XLSX)
S1 Raw images.
(PDF)
Acknowledgments
We thank the research team members for their hard work.
Author Contributions
Conceptualization: Songtao Xiang.
Data curation: Lang Guo.
Formal analysis: Jianfu Zhou, Wenxi Gao.
Funding acquisition: Songtao Xiang.
Investigation: Guohao Li, Xiu Yuan.
Methodology: Renlun Huang, Jieyan Zheng.
Project administration: Yanping Wu.
Validation: Chiwei Chen, Yuyang Xiang.
Writing - original draft: Renlun Huang.
Writing - review & editing: Songtao Xiang.
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