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Spindle and Kinetochore-Associated Complex Is Associated With Poor Prognosis in Adrenocortical Carcinoma
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Shoukai Yu, PhD,* and Jun Ma, MD
Hongqiao International Institute of Medicine, Shanghai Tongren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
ARTICLE INFO
Article history: Received 17 July 2021 Received in revised form 15 February 2022 Accepted 19 March 2022 Available online 21 April 2022
Keywords:
Adrenocortical carcinoma Cell cycle Prognosis Rare malignant tumor SKA complex
ABSTRACT
Introduction: The spindle and kinetochore-associated (SKA) complex, composed of three subunits (SKA1, SKA2, and SKA3), stabilizes spindle microtubule attachment to the kinetochore (KT) in the middle stage of mitosis. High expression of this complex is asso- ciated with poor prognosis for several tumors. However, the potential role of SKA complex overexpression in rare malignant diseases, such as adrenocortical carcinoma (ACC), has not been well investigated.
Materials and methods: In this study, we used several databases to explore the relationship between SKA subunit expression and prognosis in ACC patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) databases were used to analyze enriched pathways in ACC.
Results: The results suggest that each of the three SKA subunits are overexpressed in ACC and that high expression is correlated with poor patient prognosis. Overexpression of the SKA complex is associated with the expression of organelle fission, nuclear division, and chromosome segregation pathways. Furthermore, differential expression of hub genes for proteins that interact physically or functionally with the SKA complex (CCNB2, UBE2C, BUB1B, TPX2, CCNA2, CDCA8, CCNB1, MELK, TOP2A, and KIF2C) revealed additional po- tential biomarkers for ACC.
Conclusions: Our findings provide additional understanding of the mechanisms of ACC and suggest an approach for biomarker discovery using publicly available resources.
@ 2022 Elsevier Inc. All rights reserved.
Introduction
Adrenocortical carcinoma (ACC) is a rare malignant disease with poor prognosis.1-4 It is an aggressive cancer that occurs in both children and adults.5 When identified in the early stages, ACC may be eligible for surgical removal; however, metastasis often occurs before diagnosis.6 Thus, biomarkers are needed for the early diagnosis, prognostic prediction, and develop- ment of new treatment approaches for ACC.7,8
The spindle and kinetochore-associated (SKA) complex, which is composed of three subunits (SKA1, SKA2, and SKA3), stabilizes spindle microtubule attachment to the kinetochore (KT) in the middle stage of mitosis.9,10 Dysre- gulation of the SKA complex is closely associated with the prognosis of malignant tumors such as breast cancer, cer- vical cancer, liver cancer, and lung cancer. In hepatocellu- lar carcinoma (HCC), there is a significant association between SKA1 overexpression and poor prognosis,
* Corresponding author. School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China. Tel .: /fax: 021-63846590. E-mail address: shoukaiyu@sjtu.edu.cn (S. Yu).
0022-4804/$ - see front matter @ 2022 Elsevier Inc. All rights reserved.
including correlations with tumor size and staging.11 In addition, upregulated SKA2 expression is associated with poor prognosis in breast cancer; SKA2 can affect the pro- liferation, migration, and invasion of breast cancer cells.12 In cervical cancer, the overexpression of SKA3 is also associated with poor prognosis and can affect proliferation, migration, and invasion.13 Despite this evidence for the role of the SKA complex in cancer, the potential clinical value of SKA proteins as biomarkers for ACC has not been investigated.
With the rapid development of microarray and ribo- nucleic acid (RNA) sequencing technology, research based on RNA expression plays an important role in biomedical investigation. Therefore, in this study, we unified a wide range of publicly available databases to investigate the expression of the SKA complex, explored correlations with prognosis, and evaluated potential mechanisms of regu- lation in ACC patients. We used the TCGA, ONCOMINE, cBioPortal, UALCAN, GEPIA, and STRING databases to obtain a comprehensive understanding of the structure and function of the SKA complex in ACC. In addition, we performed Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interac- tion (PPI) analyses to provide a mechanistic insight into the function of the SKA complex in ACC and investigated the potential clinical value of hub genes as biomarkers. Additional understanding of the role of the SKA complex in ACC provides a mechanistic insight and may further provide the development of new treatments for rare ma- lignant tumors.
Methods
Analysis of SKA subunit expression in ACC
RNA levels of the SKA complex in a variety of cancers were analyzed using ONCOMINE (www.oncomine.org), a cancer microarray database and a web-based translational data- mining platform for genome-wide expression analyses. The threshold was P < 0.0001 and multiple change = 1.5, with the analysis type set as tumor versus normal groups and the data type set as messenger RNA (mRNA). Additional expression data for ACC were evaluated using UALCAN, a web-portal for in-depth analyses of gene expression data from The Cancer Genome Atlas (TCGA; http://ualcan.path. uab.edu) and Gene Expression Profiling Interactive Anal- ysis (GEPIA), an open and straightforward database for investigating publicly available cancer transcriptome data. The expression of SKA1, SKA2, and SKA3 in paired ACC and normal tissue samples from TCGA database and the Genotype-Tissue Expression (GTEx) project was evaluated using UALCAN and GEPIA.14,15 Samples from UCSC XENA (https://xenabrowser.net/datapages/) were analyzed through Toil.16 Data in the form of TPM (transcripts per million) were converted to log2 (TPM + 1) for the analyses. The differential expression of SKA mRNA was evaluated via box plots and t-tests. Heatmaps were generated with R statistical software, version 3.6.3, using the ‘ggplot2’ package.
Analysis of cBioPortal
cBioPortal (www.cbioportal.org) for Cancer Genomics is a publicly available, open-source platform to explore cancer genome data; it provides visualization, analysis, and available downloads of large-scale multi omics cancer data. In this study, the cBioPortal database was used to analyze mRNA expression z-scores (RNA Seq V2 RSEM) of SKA genes.
Evaluation of the prognostic value of SKA gene expression in ACC
The potential clinical value of SKA upregulation in ACC pa- tients was evaluated by the Receiver Operator Characteristic (ROC) curve analysis. The expression data have been firstly ranked and split into two groups (high and low) for SKA members separately. Kaplan-Meier curves were generated to estimate the correlation between SKA complex expression and the overall survival (OS) of ACC patients. The associations of SKA gene expression with clinical characteristics of ACC were evaluated by the Pearson correlation. Further examina- tion using the clinical data from TCGA led to the identification of diagnostic and prognostic biomarkers for ACC.
Analysis of SKA-associated genes and pathways dysregulated in ACC
The gene expression data of 79 ACC patients were down- loaded from TCGA. Pearson correlation coefficients (|r| > 0.4 and P < 0.001) were applied to measure and identify genes coexpressed with SKA genes. Venn diagrams were imple- mented using Webtools (http://bioinformatics.p-sb.ugent.be/ webtools/Venn/) to identify genes correlated with all three SKA subunits. Potential biological functions and signaling pathways related to SKA genes were explored using the “clusterProfiler” package in R software.17 GO and KEGG ana- lyses were conducted for differentially expressed genes. For the GO analysis, biological process (BP), cellular composition (CC), and molecular function (MF) were applied, with P < 0.05 considered statistically significant.
Identification of an SKA-associated protein-protein interaction) network
The STRING database (http://string-db.org) was used to assess and integrate a protein-protein interaction (PPI) network for SKA coexpressed genes. The network included direct (phys- ical) and indirect (functional) associations with scores more than 0.7 considered significant.18 Cytoscape 3.8 (http://www. cytoscape.org) and CytoHubba plug-ins were implemented to identify the top 10 upregulated and downregulated hub genes.19-21 The ROC curves were plotted to analyze the prog- nostic ability of hub proteins in combinations, as a combina- tion of genes might improve the area under curve (AUC) values. Additional statistical analyses (t-test and rank sum test) and visualization were conducted using the R package and GraphPad Prism 7,22,23 with P values less than 0.05 considered significant.
| Analysis Type by Cancer | Cancer V5. Normal SKA1 | Cancer V5. Normal SKA2 | Cancer V5. Normal SKA3 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Bladder Cancer | |||||||||
| Brain and CNS Cancer | 1 | 4 | 1 | ||||||
| Breast Cancer | 6 | 1 | 3 | 1 | 5 | ||||
| Cervical Cancer | 1 | ||||||||
| Colorectal Cancer | 8 | 1 | 13 | ||||||
| Esophageal Cancer | |||||||||
| Gastric Cancer | 2 | ||||||||
| Head and Neck Cancer | 1 | ||||||||
| Kidney Cancer | 3 | ||||||||
| Leukemia | 1 | 1 | |||||||
| Liver Cancer | 1 | ||||||||
| Lung Cancer | 5 | 1 | 1 | ||||||
| Lymphoma | 3 | ||||||||
| Melanoma | |||||||||
| Myeloma | |||||||||
| Other Cancer | 2 | 3 | |||||||
| Ovarian Cancer | 1 | ||||||||
| Pancreatic Cancer | |||||||||
| Prostate Cancer | 1 | ||||||||
| Sarcoma | 1 | ||||||||
| Significant Unique Analyses | 27 | 2 | 17 | 3 | 22 | ||||
| Total Unique Analyses | 368 | 271 | 281 | ||||||
1
5
10
10
5
1
☒ ☒ ☒
☐ %
☐ ☒ ☒
Fig. 1 - The mRNA expression of the spindle and kinetochore-associated (SKA) complex in various tumors from the ONCOMINE database. Red represents upregulation of the target gene, whereas blue represents downregulation. Threshold parameter settings were set at P value = 0.0001 and multiple change = 1.5.
A
B
7
C
6
3.5
6
The expression of SKA1 Log2 (TPM+1)
5
The expression of SKA2
3.0
5
4
Log2 (TPM+1)
The expression of SKA3
Log2 (TPM+1)
2.5
4
2.0
3
3
1.5
2
2
1.0
1
1
0.5
0
0
0.0
Normal
ACC
Normal ACC
Normal ACC
Results
The SKA complex is overexpressed in ACC
To determine whether the SKA complex may serve as a biomarker for ACC, we compared the mRNA levels for each of the SKA subunits for a variety of tumors and normal tissues from control patients using the ONCOMINE database. The mRNA levels of SKA1, SKA2, and SKA3 were elevated in several tumors, including breast, lung, and colorectal cancer, whereas other tumor types (bladder cancer, esophageal can- cer, melanoma, myeloma, and pancreatic cancer) showed no evidence of dysregulation (Fig. 1). These results are consistent with the results of previous studies9,24 and suggest that upregulation of SKA complex components is a common characteristic for specific cancer types.
To determine whether the trend of SKA upregulation is recapitulated in some cancers,11,13,24 we combined results from the UALCAN, GTEx, and TCGA databases. The over- expression of SKA1, SKA2, and SKA3 mRNAs was statisti- cally higher in the ACC patients versus normal groups (Fig. 2) using Wilcoxon rank sum tests. Further analysis demon- strated that the genetic alteration rates were 1.3% for SKA1, 5% for SKA2, and 8% for SKA3, with an increased expression for mRNA levels for all three SKA subunits in a subset of the ACC tumor samples (Fig. S1), compared to normal adrenal tissues.
To evaluate the predictive value of increased SKA expression in ACC, we performed the ROC analysis. The AUC was 0.818 for SKA1, 0.755 for SKA2, and 0.834 for SKA3, indicating poor overall survival for patients with elevated SKA complex expression (P < 0.05, Fig. 3). Furthermore, an analysis of the GEPIA database confirmed that ACC patients
A
1.0
B
1.0
C
1.0
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.4
0.4
0.4
0.2
SKA1
0.2
SKA2
0.2
SKA3
AUC: 0.818
AUC: 0.755
AUC: 0.834
0.0
CI: 0.755-0.881
0.0
CI: 0.674-0.837
0.0
Cl: 0.771-0.897
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
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0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
A
B
C
1.0
SKA1
1.0
SKA2
1.0
SKA3
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Low
Low
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High
High
Survival probability
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Survival probability
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Survival probability
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0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival
0.2
Overall Survival
0.2
Overall Survival
HR = 6.20 (2.48-15.47)
HR = 3.85 (1.67-8.84)
HR = 6.14 (2.46-15.30)
0.0
P < 0.001
0.0
P = 0.002
0.0
P < 0.001
0
1000
2000
3000
4000
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2000
3000
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1000
2000
3000
4000
Time (days)
Time (days)
Time (days)
with higher expression of the SKA complex subunits tended to have worse overall survival than those with low expres- sion (P < 0.01; Fig. 4 and Table S1). An analysis of the tumor characteristics of the patients suggested that SKA1 and SKA3 overexpression was correlated with the tumor (T) stage and that the expression of all three SKA genes was correlated with the metastasis (M) stage, although no correlation was observed with the nodes (N) stage or patient age (Table). Thus, these results support the potential of SKA complex expression as a biomarker for ACC.
Genes coexpressed with the SKA complex suggest biological functions in ACC
To further evaluate the role of the SKA complex in ACC, we sought to identify coexpressed genes. Our analysis identified a total of 188 genes significantly correlated with SKA1, 1088 with SKA2, and 321 with SKA3 in the TCGA transcriptome database
(absolute correlation >0.7; P value < 0.001). The top 10 genes with positive coexpression and the top 10 genes with negative coex- pression of the SKA complex are shown in heat maps (Fig. 5A-C). In addition, Venn diagrams indicated that the coexpression of 163 genes was correlated with the coexpression of the three SKA subunits in combination (Fig. 5D and Table S2).
For an additional insight into the role of the elevated SKA expression in ACC, we performed GO and KEGG analyses on the 163 coexpressed genes. The results reveal that SKA com- plex coexpressed genes in ACC were enriched in organelle fission, nuclear division, and chromosome segregation path- ways (Fig. 6). We further identified hub genes by a PPI network analysis, which predicted coregulation of the SKA complex with CCNB2, UBE2C, BUB1B, TPX2, CCNA2, CDCA8, CCNB1, MELK, TOP2A, and KIF2C (Table S3 and Fig. S2). Each of these genes was verified to be overexpressed with the SKA subunit genes in ACC. To investigate the association between mRNA expression levels of SKAs and TP53 mutation, we evaluated
| Table - Clinicopathological characteristics of adrenocortical carcinoma (ACC) patients from the TCGA database. | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Characteristic | Low SKA1 | High SKA1 | P | Low SKA2 | High SKA2 | P | Low SKA3 | High SKA3 | P |
| N | 39 | 40 | 39 | 40 | 39 | 40 | |||
| T stage, n (%) | 0.031 | 0.114 | 0.008 | ||||||
| T1 | 6 (7.8%) | 3 (3.9%) | 6 (7.8%) | 3 (3.9%) | 6 (7.8%) | 3 (3.9%) | |||
| T2 | 25 (32.5%) | 17 (22.1%) | 24 (31.2%) | 18 (23.4%) | 26 (33.8%) | 16 (20.8%) | |||
| T3 | 3 (3.9%) | 5 (6.5%) | 3 (3.9%) | 5 (6.5%) | 4 (5.2%) | 4 (5.2%) | |||
| T4 | 4 (5.2%) | 14 (18.2%) | 5 (6.5%) | 13 (16.9%) | 3 (3.9%) | 15 (19.5%) | |||
| N stage, n (%) | 0.154 | 0.154 | 0.087 | ||||||
| N0 | 36 (46.8%) | 32 (41.6%) | 36 (46.8%) | 32 (41.6%) | 37 (48.1%) | 31 (40.3%) | |||
| N1 | 2 (2.6%) | 7 (9.1%) | 2 (2.6%) | 7 (9.1%) | 2 (2.6%) | 7 (9.1%) | |||
| M stage, n (%) | 0.025 | 0.025 | 0.018 | ||||||
| M0 | 35 (45.5%) | 27 (35.1%) | 35 (45.5%) | 27 (35.1%) | 36 (46.8%) | 26 (33.8%) | |||
| M1 | 3 (3.9%) | 12 (15.6%) | 3 (3.9%) | 12 (15.6%) | 3 (3.9%) | 12 (15.6%) | |||
| Age, mean ± SD | 43.87 ± 15.18 | 49.45 ± 16.04 | 0.117 | 46.64 ± 14.61 | 46.75 ± 17.02 | 0.976 | 46.13 ± 16.02 | 47.25 ± 15.71 | 0.754 |
A
8
SKA1
Log2 (TPM+1)
B
Log2 (TPM+1)
6
6
5
SKA2
4
Low
4
Low
High
3
High
2
2
1
0
0
SGO1
KPNA2
NUF2
FEN1
CDCA5
MCM6
CKAP2L
MCM3
EME1
NCAPH
SPAG5
TYMS
ASF1B
CDCA5
BIRC5
AURKA
KIF15
CNOT9
MYBL2
PRC1
MT-CO1
MT-ATP6
MTCO1P12
MT-ND4
MT-ND4
MT-CO1
MT-ND5
MTATP8P1
MT-ND4L
MT-ND4L
MT-ATP8
MT-CO3
MT-ATP6
MT-ATP8
MTATP8P1
MTND4P35
MT-CYB
MT-CYB
MTCO2P12
MT-ND5
Z-score
-2
0
2
Z-score
C
-4
-4
-2
0
2
SKA3 Log2 (TPM+1)
4
3
Low
2
High
1
0
NUF2
CDC45
D
BRCA2
SKA1
CDCA8
CKAP2L
BUB1B
4
KIF4A
DEPDC1
1
20
BIRC5
RAD51
163
MT-CO1
MT-ND4
839
85
53
MTATP8P1
MTND4P35
MT-ATP6
SKA2
SKA3
MT-CYB
MTCO1P2
MT-CO2
MT-ND5
MTCO1P12
Z-score
-4
-2
0
2
A
B
Organelle Fission
Chromosome Segregation
Nuclear Division
BP
Nuclear Chromosome
Segregation
BP
Chromosome Segregation
Sister Chromatid
Segregation
Chromosomal Region
Chromosomal Region
Condensed Chromosome
CC
Spindle
CC
Chromosome, Centromeric
Chromosome, Centromeric
Region
Region
Catalytic Activity, Acting on DNA
Catalytic Activity, Acting on DNA
DNA Helicase Activity
MF
Single-Stranded DNA
Binding
MF
DNA-Dependent ATPase
DNA Replication Origin
Activity
Binding
Cell Cycle
KEGG
Cell Cycle
DNA Replication
Spliceosome
KEGG
Fanconi Anemia Pathway
DNA Replication
C
0.05
0.10
0.15
0.20
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
GeneRatio
D
GeneRatio
Organelle Fission
Organelle Fission
Nuclear Division
0
Nuclear Division
BP
Chromosome Segregation
Chromosome Segregation
Chromosomal Region
Chromosomal Region
Condensed Chromosome
CC
Condensed Chromosome
CC
Chromosome, Centromeric
Region
Chromosome, Centromeric
Region
Catalytic Activity, Acting on DNA
Catalytic Activity, Acting on DNA
DNA Helicase Activity
MF
DNA Helicase Activity
MF
DNA-Dependent ATPase
Activity
3’-5’ DNA Helicase
Activity
Cell Cycle
KEGG
Cell Cycle
DNA Replication
Fanconi Anemia Pathway
KEGG
Fanconi Anemia Pathway
DNA Replication
0.05
0.10
0.15
0.20
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
GeneRatio
GeneRatio
the correlation between them in ACC (Figs. S3 and S4). To evaluate the predictive value of changed hub-gene expression in ACC, we performed a ROC analysis (Fig. 7). Our findings provide additional understanding of the mechanisms of ACC and suggest an approach for biomarker discovery using pub- licly available resources.
Discussion
The SKA complex mediates binding to microtubules,25 with an essential role in the progression from metaphase to anaphase.25,26 Recent evidence indicates, however, that the SKA1, SKA2, and SKA3 genes are associated with apoptosis
and have roles in tumor development.12,24,27 Knockdown of SKA1 inhibits migration and invasion,11,28 and SKA2 and SKA3 have been shown to promote proliferation and invasion in various types of cancers.13,27,29 Furthermore, the over- expression of SKA genes has been associated with clinical stage and lymph node metastasis.27 As found in HCC,30 the SKA complex is significantly associated with TP53 mutation status. In our study, we observed an association between SKA complex and TP53 mutation status in ACC (Figs. S3 and S4). Although the potential clinical value of the SKA complex has been reported in other cancers,11,24,27 its role in rare malig- nancies, such as ACC, has not been elucidated.
Notably, we demonstrated that all three SKA subunits are upregulated in ACC and that a high expression of the
1.0
0.8
0.6
Sensitivity (TPR)
0.4
0.2
CCNB2 (AUC = 0.821)
UBE2C (AUC = 0.902)
BUB1B (AUC = 0.778)
- TPX2 (AUC = 0.912)
CCNA2 (AUC = 0.761)
- CDCA8 (AUC = 0.789)
- CCNB1 (AUC = 0.893)
- MELK (AUC = 0.855)
- TOP2A (AUC = 0.858)
- KIF2C (AUC = 0.758)
0.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
SKA genes is correlated with overall survival of ACC pa- tients, suggesting predictive potential of the SKA complex as a novel biomarker for ACC. Furthermore, expression was correlated with tumor, nodes, and metastases staging to different extents for the three subunits. To our knowledge, this is the first demonstration of the potential value of the SKA complex as a poor prognostic biomarker and potential therapeutic target for ACC patients. GO and KEGG analysis results demonstrated that genes coexpressed with the SKA
complex were enriched in organelle fission, nuclear divi- sion, and chromosome segregation pathways. In general, errors in chromosome segregation and cell division can lead to aneuploidy, the production of nonviable cells, or the first step in cancer. In addition, a PPI network analysis identified 10 hub genes (CCNB2,31 UBE2C,32 BUB1B,7,33-35 TPX2,36 CCNA2,37 CDCA8,36 CCNB1,38 MELK,36,39 TOP2A,40,41 and KIF2C42), each of which was shown to be dysregulated in ACC. Among them, BUB1B, KIF2C, and TOP2A have been
identified to play important roles in chromosome segrega- tion pathways. 43-45 CCNA2, CCNB1, and MELK are highly associated with cell cycle process. 46-48 Especially, for BUB1B, it has already been identified as a biomarker of aggressive ACC.35 Consistent with the previous findings, our results highlighted the potential value of SKA members as new biomarkers in ACC. Moreover, these findings were consis- tent with the proposed multiple analytic strategy.
Although data for rare malignancies are relatively limited, the SKA complex was found upregulated in ACC patients and high mRNA levels of SKA complex were significantly associ- ated with OS for ACC patients. In the GEPIA database, each of these 10 hub genes was overexpressed and was negatively associated with the prognosis of ACC patients. Therefore, our results are suggestive of a pathway of dysregulation in ACC that involves the SKA complex. Another limitation for the current analyses was the lack of validation in primary speci- mens or cell lines, which could highlight a path to the future.
Interestingly, microRNA-520a-3p (miR-520a-3p) plays an important role as a tumor suppressor gene in the develop- ment and progression of different cancers, and SKA2 is tar- geted by miR-520a-3p in gastric cancer cell lines.49 Therefore, studies to further clarify the role of miR520a-3p and other microRNAs in regulating SKA function in ACC would be of interest. Furthermore, future ablation studies and further investigation to extend our findings to other rare cancers may provide an increased understanding of the role of the SKA complex in different pathways of carcinogenesis.
Previous studies have shown that different SKA members are significantly recognized in different cancers.9-11,13 SKA1 was related to alpha fetoprotein (AFP), tumor size, and tumor, nodes, and metastases staging of liver cancer patients and proliferation, clinical staging, and lymph node metastasis of non-small cell lung cancer patients. While in breast cancer patients, SKA2 was identified to be related to the proliferation, migration, and invasion of cancer cells. The member SKA3 is related to the proliferation and migration of cancer cells and tumor growth in patients with cervical cancer. ACC is a cancer that is highly mutated with major changes in gene copy numbers.5º Combined with our previous results (Fig. 5D), more genes are related to SKA2, whereas less genes are related to SKA1 and SKA3. This could indicate that SKA2 is a better prognostic biomarker for ACC patients.
In summary, our results demonstrate that the mRNA level of the SKA complex is significantly upregulated in ACC. Moreover, the overexpression of SKA1, SKA2, and SKA3 is significantly associated with poor prognosis of ACC patients. These results suggest that the SKA complex has the potential to serve as a poor prognostic biomarker and therapeutic target for ACC patients. Evaluation of SKA proteins in combination with other biomarkers may enhance our understanding of the molecular basis of ACC and help investigators develop new therapies for ACC.
Supplementary Materials
Supplementary data related to this article can be found at https://doi.org/10.1016/j.jss.2022.03.022.
Author Contributions
Shoukai Yu and Jun Ma conceived the study, carried out sta- tistical analysis, wrote, and approved the manuscript.
Acknowledgments
I thank numerous investigators who contributed datasets used here and members of the Lemos laboratory for discus- sions at Harvard University.
Disclosure
None declared.
Funding
This work was supported by the Shanghai Municipal Human Resource Bureau and Shanghai Science and Technology Com- mittee [Pujiang Talent Program grant numbers, 19PJC085].
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