ABBS
Original Article
Exploring DNA topoisomerase II alpha in adrenocortical carcinoma through multi-omics analysis: a potential biomarker and therapeutic target
Jianming Lu1,3,1, Pei Deng2,1, Zhenjie Wu3,1, Yuxiang Liang3, Yangjia Zhuo3, Yongding Wu3, Yingke Liang3, Jianheng Ye3, Wenjie Xie3, Zhouda Cai1, Chao Cai2, Jiahong Chen4,
Le Zhang5, Junhong Deng1, Weide Zhong3,6,*, Jiaojiao Tang7,*, and Zhaodong Han3,*
1Department of Andrology, Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou 510180, China, 2Department of Urology, Minimally Invasive Surgery Center, the First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou 510230, China, 3Department of Urology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China, 4Department of Urology, Huizhou Municipal Central Hospital, Huizhou 516008, China, 5Institute for Integrative Genome Biology, University of California, Riverside, California 92507, USA, 6State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China, and 7 Department of Cardiology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, China “These authors contributed equally to this work.
*Correspondence address. Tel: 020-81048312; E-mail: eyhzd@scut.edu.cn (Z.H.) / gladys_tang@foxmail.com (J.T.) / eyweidezhong@scut.edu.cn (W.Z.)
Received 19 December 2024 Accepted 24 June 2025 Published 27 August 2025
Abstract
Adrenocortical carcinoma (ACC) is a rare but aggressive cancer. Recent studies identified DNA Topoisomerase II Alpha (TOP2A) as a potential biomarker for ACC, which can provide new avenues for targeted therapy and improve clinical outcomes. This study aims to elucidate the role of TOP2A in ACC by exploring its prognostic value and identifying inhibitors for ACC therapy. Utilizing RNA sequencing data, mutation data, and clinical information from The Cancer Genome Atlas (TCGA-ACC) and additional datasets from the Gene Expression Omnibus (GEO), dif- ferential expression and prognostic analyses are conducted to assess the significance of TOP2A in ACC. Im- munohistochemistry and cell assays, including cell viability, colony formation, and transwell assays, are conducted to validate the oncogenic effects of TOP2A. The “IOBR” R package is used to examine the relationship between TOP2A expression and CD8+ T-cell infiltration. The CMap platform is used to identify potential TOP2A inhibitors. In vivo assays verify the therapeutic effect of TOP2A inhibitors on ACC. Our findings indicate that TOP2A is significantly overexpressed in ACC and is associated with poor prognosis. Immunohistochemistry and cell assays confirm the oncogenic role of TOP2A. Furthermore, distinct gene expression patterns related to dif- ferent TOP2A expression levels are identified, influencing the response to immunotherapy. Potential inhibitors targeting TOP2A are discovered, and the therapeutic effects of resminostat and etoposide are confirmed via in vivo assays, suggesting new therapeutic strategies for ACC treatment. In conclusion, TOP2A serves as a crucial biomarker in ACC and is associated with adverse clinical outcomes and a diminished immune response. The identification of potential inhibitors against TOP2A opens new avenues for the development of targeted therapies for ACC patients.
Key words adrenocortical carcinoma, TOP2A, prognosis, immunotherapy response, therapeutic inhibitors
Introduction
Adrenocortical carcinoma (ACC) is a rare endocrine malignancy
with an annual incidence rate of 0.5-2 cases per million in adults and 0.2-0.3 cases per million in children [1,2]. Despite its rarity,
@ The Author(s) 2025. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https:// creativecommons.org/licenses/by-nc-nd/4.0/).
ACC has a poor prognosis, with a 5-year survival rate of only 35% [3], which decreases to less than 10% in advanced stages [4]. Post- surgical resection often leads to local recurrence and distant metastases in nearly two-thirds of patients [3,5]. Mitotane is currently the sole chemotherapeutic agent for ACC, characterized by its inconsistent efficacy and severe side effects when used as a monotherapy [6,7]. Other treatments, such as the combination of etoposide, doxorubicin, and cisplatin with mitotane (EDP-M) and immunotherapy, have shown uncertain efficacy and severe adverse effects [8]. Similarly, the application of immunotherapy, which has garnered significant attention in recent years, continues to lead to unstable therapeutic outcomes in ACC, leading to numerous challenges [9]. Therefore, new biological markers and therapeutic targets for ACC are urgently needed.
The development of new biomarkers and therapeutic targets for ACC is challenging because of the pronounced heterogeneity of tumors [10-14]. Advances in high-throughput sequencing technol- ogies have enabled multiomics approaches that integrate genomics, transcriptomics, proteomics, and more to explore the molecular heterogeneity of cancers [10]. This approach aids in identifying potential biomarkers and therapeutic targets and enhances our understanding of the complex biology of ACC.
DNA topoisomerase II alpha (TOP2A) encodes an essential enzyme that regulates chromosome condensation and sister chromatid separation by altering DNA topology during replication and transcription [15]. TOP2A is also involved in cell cycle regulation and DNA repair [16]. Studies have shown that TOP2A is overexpressed in various tumors, including hepatocellular carcinoma, non-small cell lung cancer, and breast cancer, where it is linked to disease progression and poor prognosis [17-19]. While TOP2A overexpression has been identified in ACC and is associated with cell proliferation and invasion, comprehensive studies on its specific roles are lacking [20], which underscores the need for further research to understand the contribution of TOP2A to ACC pathogenesis and progression, potentially offering new strategies for targeted therapies.
Materials and Methods Study flow
As illustrated in Figure 1, this study compiled gene expression and clinical follow-up data from multi-center adult and pediatric ACC cohorts worldwide. Through differential and prognostic gene analyses, TOP2A was identified as a potential biomarker for ACC. The ability of TOP2A as a biomarker for ACC was subsequently validated using multi-center data, and its oncogenic effect was elucidated through immunohistochemistry and cell assays. Ulti- mately, through multi-omics analyses, immune infiltration assess- ments, and inhibitor studies, the potential of TOP2A as a therapeutic target was determined in detail.
Data collection and processing
This research was initiated by downloading RNA sequencing data, corresponding mutation information, and clinical details for the ACC cohort from The Cancer Genome Atlas available on the UCSC Xena platform (https://xenabrowser.net/datapages/). For data ana- lysis, we employed the R packages “clusterProfiler” (version 4.8.1) and “org.Hs.eg.db” (version 3.17.0) to convert RNA-seq Ensembl IDs into SYMBOL IDs. Additionally, we gathered data sets for four ACC samples, including both adults and children, from the Gene
Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih. gov/geo/), specifically adult samples (GSE19750, GSE10927) and pediatric samples (GSE76019, GSE76021). The adult meta-cohort included the GSE19750 and GSE10927 datasets, whereas the pediatric meta-cohort included the GSE76019 and GSE76021 datasets. Batch effects were corrected via the “sva” R package (version 3.48.0). The datasets and clinicopathological information included in this study are listed in Supplementary Table S1.
Prognostic gene screening
Within the GSE19750-10927 cohort, differential analysis was conducted to differentiate between tumor and benign tissue samples via the ‘limma’ package (version 3.6.3), identifying 233 differentially expressed genes (DEGs) under the criteria of |logFC| > 2 and adjusted P < 0.05. The R package ‘timeROC’ (version 0.4) was used to calculate the time-dependent area under the receiver operating characteristic curve (AUC) for evaluating the prognostic predictive performance of TOP2A. Univariate Cox regression and Kaplan-Meier (KM) survival analyses were performed via the R package ‘survival’, and the optimal cutoff value for TOP2A expression was automatically determined via the ‘surv_cutpoint’ function from the R package ‘survminer’ (version 0.4.9) with the parameter ‘minprop’ = 0.3. Patients were stratified into high- and low-expression groups on the basis of this cutoff for Kaplan-Meier (KM) analysis. Genes with a median survival time AUC > 0.7 and P value < 0.05 in the Cox regression analysis across all cohorts were defined as having significant prognostic value. Additionally, variables with P values < 0.2 in the univariate analysis of clinical features were included in a multivariate Cox regression model to assess the statistical significance of TOP2A as an independent prognostic factor.
Functional enrichment
We employed Spearman correlation to assess the association between TOP2A expression and the expressions of all other genes. The ‘clusterProfiler’ (version 4.8.1) R package was utilized for functional enrichment analysis to identify significantly enriched pathways associated with the Kyoto Encyclopedia of Genes and Genomes (KEGG).
Immunohistochemistry (IHC)
Samples of ACC and adrenal adenoma tissues were obtained from Huizhou Central Hospital with ethical approval. The samples were fixed in 4% paraformaldehyde, embedded in paraffin, and sectioned at 4 um thickness. Sections were treated with 1% H2O2, blocked with goat serum, and incubated overnight with primary antibodies at 4℃, followed by a 30-min incubation with biotiny- lated secondary antibodies at room temperature. IHC staining protocol: The final score was a combination of the percentage of positively stained cells and the staining intensity. The immunor- eactive score (IRS) method was used to evaluate the staining results [21]. The IRS was calculated by multiplying the staining intensity score by the percentage of positive cells. The staining intensity was scored as 0 (negative), 1 (weak), 2 (positive), or 3 (strong). The percentage of positive cells was scored as follows: 1 (<10%), 2 (11%-50%), 3 (51%-80%), or 4 (>80%). The final scores ranged from 0 (negative) to 12 (strong staining in >80% of the cells). The antibody used was anti-TOP2A antibody (YT4701; Immunoway, Suzhou, China).
Down
20
GEO
GSE10927
(BA a foe) oBop
RSPOZ
GSE19750 Gene Expression Omnibus Meta Cohorts
0 AADAGPDENT
POGERN
5
ADGRVI
9
Adrenocortical Carcinoma
Jogz (told change)
Differential Expression Genes
TCGA
& GEO Gene Expression Omnibus
1.00
High Low
1ROC Analysis 2COX Analysis
0.75
Validation
K
Kyoto Encyclopediarat Genes and Genome
0.50
0.25
Log-rank p < 0.0001
0.00
0
50
100
150
Prognostic Value
Functional Enrichment
Topoisomerase II Alpha (TOP2A)
1 IHC
2 Colony formation
1 Somatic mutation 2 Copy number variation Mutation analysis
3CCK-8 4Transwell
Experimental validation
1
0.088
0.155
High TOP2A_p
0.8
0.020
0.001
Low TOP2A_p
CD8+ T Cell infiltration level
R =- 0.22, p = 0.053
0.6
High TOP2A_b
0.4
0.160
0.008
Low TOP2A_b
pvalue
PD1-noR
PD1-R
0.2
PD1-R
PD1-noR
ConnectivityMap
-
1
Expression of TOP2A
Immunotherapy Response
Potential drug Predict
Immune landscape
Cell transient transfection
This study utilized two ACC cell lines, SW13 and NCI-H295R, obtained from Procell Biotechnology (Wuhan, China). These ACC cell lines were cultured in DMEM/F12 medium (Bio-Channel,
Nanjing, China) supplemented with 10% fetal bovine serum (Thermo Fisher, Waltham, USA) and maintained in a humidified incubator at 37℃ with 5% CO2. In accordance with the manufacturer’s instructions, negative control (NC) and TOP2A
siRNAs (Tsingke Biotech, Guangzhou, China) were transfected into ACC cells via the GP-transfect-Mate system (GenePharma, Suzhou, China). The cells were then incubated for 48 h in the incubator before total protein extraction for western blot analysis. The sequences of the siRNAs are listed in Supplementary Table S2.
Western blot analysis
Detailed experimental procedures were described in our previously published papers [22]. Briefly, cells were collected and lysed in RIPA buffer containing protease inhibitors. The resulting protein samples were separated via SDS-PAGE and electrotransferred onto PVDF membranes, which were then blocked with 5% non-fat milk. The membranes were subsequently incubated with the aforemen- tioned anti-TOP2A primary antibody (YT4701; Immunoway) at a dilution of 1:2000 and with the anti-ß-actin primary antibody (sc- 47778; Santa Cruz Biotech, Santa Cruz, USA) at a dilution of 1:5000. After incubation, the membranes were washed three times with PBST, each for 10 min, before exposure. ß-Actin served as a loading control. Protein band intensities were quantified via ImageJ.
Cell assays
For the Cell Counting Kit-8 (CCK-8) assay, we used a 96-well plate with five replicates per condition, each containing 4 x 103 tran- siently transfected cells in 100 uL of medium. After 2, 24, 48, and 72 h of incubation, 100 uL of a 1:9 ratio of CCK-8 solution (MeilunBio, Dalian, China) to medium mixture was added to each well. Two hours postincubation, the optical density (OD) at 450 nm was measured via a microplate reader. This experiment was performed in triplicate to ensure reliability.
For the colony formation assay, each cell line was seeded at 1000 cells/well in a 6-well plate and cultured at 37°℃ in a 5% CO2 incubator. After two weeks, the cells were washed twice with cold phosphate-buffered saline (PBS), fixed with 4% paraformaldehyde for 15 min, and stained with 1% crystal violet solution for 20 min at room temperature. The number of visible colonies was counted. This experiment was performed in triplicate.
For the transwell assay, 4 x104 transiently transfected cells were seeded in serum-free medium in the upper chamber, while complete medium was added to the lower chamber. The cells were maintained at 37℃ in 5% CO2 for 48 h, washed with saline and fixed with paraformaldehyde. The cells were stained with 0.1% crystal violet solution and photographed under a microscope, and the stained cells were counted. This experiment was performed in triplicate to ensure reproducibility.
Mutation landscape
The “maftools” R package (version 2.16) was utilized to assess the tumor mutational burden (TMB) for each patient. To investigate the genomic mutation differences between the high- and low-TOP2A expression groups, we visualized mutation waterfall plots of the top 20 genes with the highest mutation frequencies in the ACC via maftools and the ComplexHeatmap R package (version 2.16). Additionally, waterfall plots for the top 10 amplified (Amp) and deleted (Del) chromosomal segments in the ACC were analyzed for visualization. Differences in copy number variations (CNVs) between TOP2A expression subgroups were calculated and confirmed through chi-square tests. Differences in TOP2A expres- sion levels between mutation subgroups were determined and verified via the Wilcoxon test.
Immunotherapy response
We used the “deconvo_tme” function in the R package IOBR (version 0.99.9), which combines the MCPcounter, EPIC, xCell, and TIMER algorithms. CD8+ T-cell immunoinfiltration scores were evaluated for all samples in the TCGA-ACC and GSE76019 datasets. Spearman correlation analysis was then used to assess the relationship between TOP2A expression and CD8+ T-cell infiltration levels in both datasets. This approach systematically quantified the association between TOP2A expression and CD8+ T-cell infiltration level. The Subclass Mapping (Submap) algorithm was applied to predict responses to immune checkpoint blockade (ICB) therapy, which, on the basis of hierarchical clustering, identifies common subtypes between our TOP2A median expression-based subgroups (high vs. low) and a published transcriptional dataset with known immunotherapy responses [23,24]. The Submap algorithm com- pares gene expression profiles between these groups and the reference dataset, generating a set of P values corresponding to different immunotherapy response types. A lower P value indicates a greater likelihood of a positive response to ICB therapy. Furthermore, we compiled four immunotherapy datasets from the Tiger database, including PRJNA482620, Braun, GSE78220, and GSE91061, to identify samples resistant to immunotherapy. These genes were then categorized on the basis of TOP2A expression levels for Kaplan-Meier (KM) survival analysis.
Inhibitor prediction
We utilized the CMap platform to identify potential inhibitors targeting TOP2A. To delve deeper into the mechanisms of action of these inhibitors, specificity analysis was conducted using the mechanism of action (MoA).
In vivo assays
All animal experiments were approved by the Animal Care and Use Committee of Guangzhou First People’s Hospital. Four-week-old female nude mice were obtained from GemPharmatech (Nanjing, China) and subcutaneously injected with 3 x 106 SW-13 cells into the right flank. Once the subcutaneous tumor volume reached approximately 50-100 mm3, the mice were randomly divided into four groups: the first three groups, each containing 5 mice, and the final group, containing 6 mice. The groups were as follows: a negative control group (Control), which received an equal volume of PBS via intragastric gavage (i.g.) for 2 weeks; a resminostat group (20 mg/kg, i.g., administered continuously for 14 days); an etopo- side group (40 mg/kg, intraperitoneal injection, i.p., once per week for 2 weeks); and a combination treatment group (20 mg/kg of resminostat + 40 mg/kg of etoposide). The length and width of the tumors were measured every 3 days using calipers, and the tumor volume (V) was calculated via the following formula: V (mm3) = length (mm) x width2 (mm2)/2. Upon completion of the treatment, or if the tumor volume reached 2000 mm3 prior to that, the mice were euthanized, and the tumors were excised and weighed.
Statistical analysis
Statistical analyses and graphing were performed via R version 4.3.1, with some data visualizations facilitated by the Sanger Box bioinformatics tool. Immunohistochemistry and cellular experiment data were analyzed and graphed via GraphPad Prism version 8.0. Differences between two groups and multiple groups were assessed via the Wilcoxon rank-sum test and the Kruskal-Wallis test,
respectively. P values were two-sided, with statistical significance denoted as *P < 0.05, ** P < 0.01, and *** P < 0.001.
Results
Screening and identification of biomarkers for ACC To screen and identify biomarkers for ACC, we integrated two pediatric ACC datasets after batch correction into the GSE76019- 76021 cohort and two adult ACC datasets through batch correction to form the GSE10927-19750 cohort (Supplementary Figure SIA- D), which were merged. We subsequently performed differential gene expression analysis between ACC samples and benign samples within the GSE10927-19750 cohort, using a threshold of |log_FC| > 2 and adjusted P value < 0.05, resulting in 233 differential genes (Supplementary Figure SIE-F).
As depicted in the Venn diagram in Figure 2A, on the basis of these differential genes, we intersected prognostic genes across several adult ACC cohorts (TCGA, GSE10927, and GSE19750), ultimately identifying eight molecules: CDC20, TOP2A, ASPM, CENPF, KIF11, CEP55, KIF20A, and FOXM1. We then validated these eight molecules in pediatric ACC cohorts (GSE76019 and GSE76021) and discovered that only CDC20 and TOP2A demon- strated potential as biomarkers in pediatric ACC datasets (Figure 2B-C). In the TCGA-ACC cohort, TOP2A exhibited higher expres- sion levels than CDC20 did, suggesting that molecules with higher expression levels offer better detectability. Consequently, we identified TOP2A as our target molecule. Furthermore, in the GSE10927-19750, GSE10927, and GSE19750 cohorts, the expression levels of TOP2A were significantly greater in tumor tissues than in normal tissues (Figure 2D).
To further elucidate the correlation between TOP2A expression and the prognosis of ACC patients, we categorized TOP2A patients into high- and low-expression groups for KM survival analysis. The analysis across all cohorts revealed that a high expression level of TOP2A was significantly associated with an adverse prognosis (Figure 3A). Additionally, both univariate and multivariate Cox regression analyses revealed that TOP2A serves as an independent prognostic factor for ACC patients across various endpoints, including overall survival (OS), progression-free interval (PFI), and event-free survival (EFS) (Figure 3B-C). Collectively, these findings suggest that TOP2A may act as a biomarker for predicting the prognosis of both adult and pediatric patients with ACC.
Experimental validation of TOP2A in ACC
We then explored the role of TOP2A in clinical samples via in vitro experiments. Immunohistochemical staining revealed that TOP2A was located primarily in the cell membrane and cytoplasm, with higher expression in ACC tissues than in benign tissues (Figure 4A). We subsequently used loss-of-function assays to verify the function of TOP2A in ACC cell lines. Three siRNA sequences targeting TOP2A were designed, with si-1 and si-2 effectively reducing TOP2A expression in SW-13 and NCI-H295R cell lines (Figure 4B). CCK8 assays revealed that downregulating TOP2A inhibited ACC cell viability (Figure 4C). Colony formation assays revealed reduced cell colony formation, and transwell assays indicated decreased invasive ability with TOP2A knockdown (Figure 4D-E).
Functional enrichment analysis
We subsequently conducted functional enrichment analyses of TOP2A in adult (TCGA) and pediatric (GSE76019) ACC datasets.
The top 10 activated and suppressed pathways were selected based on Normalized Enrichment Scores (NES) (Figure 5A,B and Supplementary Tables S3,S4). Intersecting these pathways across both datasets showed consistency in the majority of pathways (Figure 5C). Activated pathways were enriched in DNA replication, DNA damage repair, and cell cycle processes. Suppressed pathways were less commonly associated with tumors. These findings suggest that the biological functions of TOP2A in both adult and pediatric ACC patients are similar.
TOP2A and mutational genomics
We explored the role of TOP2A in the mutational genomics of ACC via multiomics data. In ACC, the genes with the highest mutation frequencies were TP53, MUC16, CTNNB1, TTN, and CNTNAP5 (Figure 6A). Stratifying the data according to TOP2A expression levels, we found that TP53 and CNTNAP5 mutations were significantly more common in the high TOP2A subgroup (Figure 6B). Similarly, in the context of copy number alterations (CNAs), the high TOP2A expression group presented a greater frequency of loss mutations. However, the expression of TOP2A was not associated with frequent CNA gains (Figure 6C). As depicted in Supplementary Figure S2, the high TOP2A expression group presented a greater TMB. Intriguingly, when categorized by different mutational statuses, such as within TP53, APOB, and groups with high-frequency loss of CNAs, TOP2A expression levels were higher. This finding suggests a bidirectional interaction between TOP2A and genomic mutations.
TOP2A and the immune microenvironment in the ACC Our multi-omics analysis revealed a positive correlation between TOP2A and tumor mutational burden, a biomarker for immu- notherapy response. However, the role of TOP2A in the ACC immune microenvironment is unclear. We analyzed the correlation between TOP2A expression and tumor immune cell infiltration via various algorithms in the adult (TCGA) and pediatric ACC (GSE76019) datasets (Figure 7A-B). The TIMER, xCell, EPIC, and MCPcounter algorithms revealed an inverse correlation between TOP2A expression and CD8+ T-cell infiltration in both the adult and pediatric datasets, although not all correlations were statistically significant.
Given the pivotal role of CD8+ T cells in antitumor immunity and their negative correlation with TOP2A, we predicted the response to PD-1 inhibitor therapy in ACC cohorts via the submap algorithm. The results revealed a greater PD-1 inhibitor response rate in the low TOP2A expression group (Figure 7C). KM survival analyses in immune therapy cohorts for glioblastoma, melanoma, and renal cancer patients also revealed a better prognosis in the low TOP2A expression group (Figure 7D-G). These findings suggest that TOP2A could be a biomarker for immunotherapy in ACC.
Seeking inhibitors targeting the TOP2A subgroup
Given the potential of TOP2A as an ACC biomarker, we investigated its viability as a therapeutic target. Using the CMap platform, we identified compounds that target pathways associated with TOP2A expression in the ACC (Supplementary Tables S5,S6). The top 20 compounds were identified in both the adult (TCGA) and pediatric (GSE76019) datasets (Figure 8A). Notably, three compounds- resminostat, palbociclib, and sunitinib-were common in both datasets (Figure 8B). Mode-of-action pathway analysis revealed that
A
GSE19750-10927-AUC
C
ICGA-OS&PFI-COX
Cohort
TCGA-OS&PFI-AUC
3447
40
37
DGEs
TOP2A
☐ TCGA-OS
161
32
4
2
☐ TCGA-PFI
4
0
0
0
☐ GSE19750
193
10
6
3
CDC20
☐
GSE10927
103
2
6
48
☐ GSE19750-10927
0
0
0
0
1
6
2
ASPM
1
☐ GSE76019
☐ GSE76021
489
169
359
55
CENPF
☐ GSE76019-76021
GSE19750
GSE19750-10927-COX
KIF11
Type
Intersection
CEP55
☐ Risky
☐ Not Significant
TOP2A
CENPF
CEP55
CDC20
KIF11
ASPM
B
TCGA-OS
TCGA-PFI
GSE19750
GSE10927
1.0
1.0
1.0
1.0
0.8
0.8
Sensitivity
0.8
LA
0.6
Sensitivity
0.6
0.4
0.4 0
Sensitivity
0.6
Sensitivity
TOP2A, AUC=0.75
0.4
0.4
CENPF, AUC=0.63
TOP2A, AUC=0.88
CENPF, AUC=0.88
TOP2A, AUC=0.91
CENPF, AUC=0.88
TOP2A, AUC=0.78
0.2
CEP55, AUC=0.68
CEP55, AUC=0.9
0.2
CEP55, AUC=0.91
CENPF, AUC=0.78
CDC20, AUC=0.73
KIF11, AUC=0.79
0.2
CDC20, AUC=0.86
CEP55, AUC=0.69
0.0
ASPM, AUC=0.76
KIF11, AUC=0.91
CDC20, AUC=0.88
0.2
0.0
ASPM, AUC=0.91
0.0
KIF11, AUC=0.88
CDC20, AUC=0.76
ASPM, AUC=0.91
0.0
KIF11, AUC=0.72
ASPM, AUC=0.78
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
1-Specificity
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity
1-Specificity
1-Specificity
GSE19750-10927
GSE76019
GSE76021-76019
1.0
GSE76021
1.0
1.0
1.0
0.8
0.8
Sensitivity
0.8
0.8
0.6
0.4 0
Sensitivity
0.6
Sensitivity
0.6
Sensitivity
0.4 0.6
TOP2A, AUC=0.78
0.4
TOP2A, AUC=0.74
3
CENPF, AUC=0.75
CEP55, AUC=0.75
CENPF. AUC=0.7
TOP2A, AUC=0.82
TOP2A, AUC=0.8
CDC20, AUC=0.73
0.2
CEP55, AUC=0.62
CDC20, AUC=0.71
0.2
CENPF, AUC=0.76
CEP55, AUC=0.75
CENPF, AUC=0.82
KIF11, AUC=0.73
CDC20, AUC=0.75
CEP55, AUC=0.83
0.0
0.2
ASPM, AUC=0.76
KIF11, AUC=0.7
CDC20, AUC=0.83
0.0
ASPM, AUC=0.77
KIF11, AUC=0.82
ASPM, AUC=0.79
KIF11, AUC=0.88
ASPM, AUC=0.88
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.0
1-Specificity
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity
1-Specificity
D
GSE19750
GSE10927
GSE19750-10927
15
2.5e-3
group
6.8e-6
group
2.5e-8
group
Expression
normal
Expression
15
10
normal
Expression
15
normal
cancer
cancer
5
10
10
cancer
5
5
0
0
TOP2A
TOP2A
TOP2A
the EGFR and HDAC inhibitor pathways were the top enriched pathways for both adult and pediatric ACC (Supplementary Figure S3A-B).
Following the identification of potential inhibitors targeting TOP2A in ACC, we selected resminostat, the highest-ranking candidate, and combined it with the known TOP2A-specific
A
TCGA-OS
TOP2A
TCGA-PFI
Progression Free Interval
TOP2A
GSE19750
TOP2A
GSE10927
1.00
High
1.00-
TOP2A
High
1.00-
High
1.00
Overall Survival
0.75
Low
0.75
Low
Overall Survival
0.75
Low
- High
Overall Survival
0.75
Low
0.50
0.50
0.50
0.50
0.25
Log-rank p < 0.0001
0.25
Log-rank
p < 0.0001
0.25
Logrrank
p
0.00057
0.25
Log-rank
p
0.00061
0.00
0.00
0.00
0
50
Time(month)
100
150
0
50
Time(month)
100
150
0.00
0
50
100
Time(month)
150
200
0
50
Number at risk
Number at risk
Time(month)
100
150
Number at risk
TOP2A
Number at risk
High
24
1
0
0
TOP2A
High
25
0
0
0
TOP2A
High
9
0
0
0
0
TOP2A
Low
55
27
8
2
Low
54
19
6
2
Low
13
9
6
4
1
High
8
0
0
0
0
50
Time(month)
100
150
Low
5
0
50
16
2
0
Time(month)
100
150
0
50
100
Time(month)
150
200
0
50
Time(month)
100
150
GSE19750_10927
TOP2A
GSE76019
TOP2A
GSE76021
TOP2A
GSE76019-76021
1.00-
TOP2A
High
1.00
L
High
1.00
High
1.00
Overall Survival
0.75
Low
Event-Free Survival
High
Low
Event-Free Survival
Low
Event-Free Survival
0.75
0.75
0.75
Low
0.50
0.50
0.50
0.50
0.25
Log-rank p < 0.0001
0.25
Log-rank
og-rank
Log-rank
p
0.00071
0.25
p
0.0028
0.25
P
0.00078
0.00
0
50
100
150
200
0.00
0.00
0.00
Time(month)
0
20
Time(month)
40
60
80
0
50
100
Time(month)
150
200
0
50
100
Time(month)
150
200
Number at risk
TOP2A
Number at risk
Number at risk
Number at risk
High
16
0
0
0
0
TOP2A
High
15
7
4
1
0
TOP2A
High
11
1
0
0
0
TOP2A
High
Low
30
32
14
8
8
1
0
0
0
4
Low
19
15
11
4
1
Low
8
5
4
1
0
Low
21
11
4
1
0
0
50
100
Time(month)
150
200
0
20
Time(month)
40
60
80
0
50
100
Time(month)
150
200
0
50
100
Time(month)
150
200
B
C
Univariate analysis
Multivariate Cox regression analysis
6
7
| Variable | HR (95%CI) | P | |
|---|---|---|---|
| TCGA-OS | : | ||
| age | 1 (0.99, 1) | 0.33 | |
| 0.99 (0.45, 2. | 1 | ||
| gender | 2) | 0.97 F | |
| stage | 2.9 (1.9, 4.6) | 3.00E-06 | 1- |
| T | 3.4 (2.1, 5.4) | 4.00E-07 | - --- |
| N | 2 (0.77, 5.4) | 0.15 1- | . 1 |
| M | 6.2 (2.7, 14) | 1.40E-05 | F 1 |
| TOP2A | 2.6 (1.9, 3.6) | 1.30E-08 | I- -1 |
| TCGA-PFI | |||
| age | 1 (0.98, 1) | 0.89 | |
| 0.67 (0.34, 1. | F | 4 | |
| gender | 3) | 0.23 | |
| stage | 2.2 (1.5, 3) | 8.30E-06 | F -1 |
| T | 1.9 (1.4, 2.6) | 8.10E-05 | 1- -1 |
| N | 2.9 (1.3, 6.3) | 0.0083 | F 1 |
| M | 3.5 (1.8, 7) | 0.00032 | F 1 |
| TOP2A | 1.9 (1.5, 2.4) | 8.50E-07 | F 1 |
| GSE19750 | |||
| age | 1 (0.99, 1.1) | 0.097 | |
| 1.3 (0.46, 3.4 | 0.65 F | 1 | |
| gender | ) | ||
| stage | 1 (0.74, 1.4) | 0.87 I- | -1 |
| TOP2A | 1.5 (1, 2.3) | 0.027 | |
| GSE10927 | |||
| age | 1 (0.98, 1) | 0.52 | |
| 1.5 (0.51, 4.2 | 0.48 F | 1 | |
| gender | ) | ||
| stage | 1.8 (1.1, 3) | 0.021 | F 1 |
| TOP2A | 1.7 (1.1, 2.5) | 0.013 | - -4 |
| GSE19750-10927 | |||
| age | 1 (0.99, 1) | 0.17 | |
| gender | 1.1 (0.55, 2.3 | 0.75 F | -1 |
| 1.1 (0.88, 1.4 | 0.37 F | ||
| stage | ) | 1 | |
| TOP2A | 1.5 (1.2, 2) | 0.0019 | I 4 |
| GSE76019 | |||
| age | 1.3 (1.1, 1.4) | 7.30E-05 | H |
| gender | 5.7 (1.7, 19) | 0.0048 | I · 1 |
| stage | 3.1 (1.5, 6.4) | 0.0029 | F · 4 |
| TOP2A | 1.9 (1.2,3) | 0.0084 | F |
| GSE76021 | |||
| age | 1(0.99, 1) | 0.49 | |
| gender | 1.3(0.38, 4.4) | 0.68 | 1 |
| stage | 1.4(0.74, 2.6) | 0.32 1- | 4 |
| TOP2A | 1.6(1,2.4) | 0.034 | |
| GSE76019-76021 | |||
| age | 1(1, 1) | 6.60E-05 | |
| gender | 2.9(1.3, 6.6) | 0.011 | F |
| stage | 2.1(1.3, 3.3) | 0.0017 | - |
| TOP2A | 1.6(1.2. 2.1) | 0.00068 | F 1 |
| -1 log2(Hazard Ratio(95%CI)) 0 1 2 3 4 5 | |||
| Variable | HR(95%CI) | P | |
|---|---|---|---|
| TCGA-OS | |||
| stage | 0.98 (0.23, 4.15) | 0.974 F | 1 |
| T | 2.37 (0.92, 6.12) | 0.075 +: | 1 |
| N | 2.07 (0.61, 7.05) | 0.246 F | : 1 |
| M | 0.65 (0.12, 3.65) | 0.628 + | 4 |
| TOP2A | 2.02 (1.29, 3.16) | 0.002 | .... |
| TCGA-PFI | |||
| stage | 2.67 (0.90, 7.89) | 0.077 | -1 |
| T | 0.74 (0.34, 1.59) | 0.436 1 | . 1 |
| N | 1.21 (0.42, 3.46) | 0.723 + | 1 : |
| M | 0.45 (0.13, 1.57) | 0.208 I | 1 |
| TOP2A | 1.68 (1.22, 2.32) | 0.001 | F |
| GSE10927 | |||
| stage | 2.75 (1.50, 5.03) | 0.001 | ...... 1 |
| TOP2A | 2.22 (1.37, 3.59) | 0.001 | |
| GSE19750 | |||
| age | 1.05 (1.01, 1.10) | 0.024 | |
| TOP2A | 1.79 (1.15, 2.79) | 0.01 | 4 |
| GSE19750-10927 | |||
| age | 1.02 (1.00, 1.05) | 0.084 | |
| TOP2A | 1.57 (1.19, 2.07) | 0.001 | 1 -4 |
| GSE76019 | |||
| age | 1.18 (0.98, 1.42) | 0.083 | 4 |
| gender | 5.23 (1.32, 20.71 ) | 0.018 | 1 4 |
| stage | 1.56 (0.50, 4.86) | 0.444 F | 4 |
| TOP2A | 1.67 (1.00, 2.79) | 0.05 | ..... |
| GSE76021 | |||
| stage | 1.58 (0.79, 3.16) | 0.193 F | 4 |
| TOP2A | 1.62 (1.07, 2.45) | 0.021 | F = |
| GSE76019-76021 | |||
| age | 1.01 (1.00, 1.02) | 0.08 | |
| gender | 3.67 (1.51, 8.94) | 0.004 | I 4 |
| stage | 1.69 (0.90, 3.16) | 0.101 F | ...... |
| TOP2A | 1.65 (1.23, 2.21) | 0.001 | - |
| -3 -2 log2(Hazard Ratio(95%CI)) -1 0 1 2 3 4 | |||
Figure 3. Prognostic analysis of TOP2A in ACC (A) Kaplan-Meier survival curves for TOP2A across various ACC datasets (Log-rank test). (B) Univariate analysis of TOP2A in the ACC. (C) Multivariate analysis of TOP2A in the ACC.
A
20X
20X
20X
Case #1 (Non-cancer)
Case #2 (Non-cancer)
Case #3 (Non-cancer)
Immunoreactive score of TOP2A
20X
20X
20X
15-
*
10-
5-
0
Case #4 (ACC)
Case #5 (ACC)
Case #6 (ACC)
Non-cancer (n=5)
ACC (n=6)
B
C
H295R
SW-13
H295R
SW-13
0.4-
+- si-NC
3-
si-NC
NC si-1 si-2 si-3
NC si-1 si-2 si-3
kDa
Cell viability
0.3
si-TOP2A-1
회
si-TOP2A-1
si-TOP2A-2
Cell viability
2
si-TOP2A-2
TOP2A
174
0.2
1.
Actin
43
0.1-
0.0
0
24
48
72
0
0
24
48
72
D
E
Hours
Hours
si-NC
si-TOP2A-1
si-TOP2A-2
I
si-NC
si-TOP2A-1
si-TOP2A-2
H295R
H295R
SW-13
SW-13
I
H295R
SW-13
H295R
SW-13
800-
150-
Number of colony
500-
**
Number of invasive cell
300-
Number of invasive cell
Number of colony
**
400
100
200
600
300
400
50
200
100
100
200
0
0
0
0
si-NC
si-TOP2A-1
si-TOP2A-2
si-NC
si-TOP2A-1
si-TOP2A-2
si-NC
si-TOP2A-1
si-TOP2A-2
si-NC
si-TOP2A-1
si-TOP2A-2
A
TCGA-ACC (Adult)
Actived
Suppressed
DNA REPLICATION
ALLOGRAFT REJECTION
INES|
CELL CYCLE
GRAFT VERSUS HOST DISEASE
2.0
HOMOLOGOUS RECOMBINATION
ASTHMA
2.5
3.0
MISMATCH REPAIR
TYPE I DIABETES MELLITUS
3.5
BASE EXCISION REPAIR
AUTOIMMUNE THYROID DISEASE
NUCLEOTIDE EXCISION
INTESTINAL IMMUNE NETWORK FOR IGA PRODUCTION
p.adjust
0.000
REPAIR
SPLICEOSOME
RETINOL METABOLISM
0.005
RIBOSOME
PRIMARY BILE ACID BIOSYNTHESIS
0.010
OOCYTE MEIOSIS
ASCORBATE AND ALDARATE METABOLISM
RNA DEGRADATION
PRIMARY IMMUNODEFICIENCY
2.1
2.3
2.5
-3.5
-3.0
-2.5
-2.0
B
NES
NES
GSE76019(Paediatric)
Actived
Suppressed
CELL CYCLE
GRAFT VERSUS HOST DISEASE
INES|
DNA REPLICATION
ALLOGRAFT REJECTION
2.0
AUTOIMMUNE THYROID DISEASE
☒
SPLICEOSOME
2.5
☒
3.0
MISMATCH REPAIR
TYPE I DIABETES MELLITUS
3.5
OOCYTE MEIOSIS
NATURAL KILLER CELL MEDIATED CYTOTOXICITY
HOMOLOGOUS RECOMBINATION
ANTIGEN PROCESSING AND PRESENTATION
p.adjust
0.000
NUCLEOTIDE EXCISION
ARACHIDONIC ACID METABOLISM
0.001
REPAIR
BASE EXCISION REPAIR
HEMATOPOIETIC CELL LINEAGE
0.002
0.003
P53 SIGNALING PATHWAY
ASTHMA
PYRIMIDINE METABOLISM
COMPLEMENT AND COAGULATION CASCADES
2.0
2.4
2.8
-2.4
-2.3
-2.2
-2.1
NES
NES
C
Actived
Suppressed
DNA REPLICATION
CELL CYCLE
2
5
HOMOLOGOUS RECOMBINATION
GRAFT VERSUS HOST DISEASE
MISMATCH REPAIR
ALLOGRAFT REJECTION
8
BASE EXCISION REPAIR
5
AUTOIMMUNE THYROID DISEASE
NUCLEOTIDE EXCISION REPAIR
TYPE I DIABETES MELLITUS
SPLICEOSOME
2
5
ASTHMA
OOCYTE MEIOSIS
A
TOP2A
Stage
Age
TOP2A
pN
Low
pT
High
B
Gender
PCT
0
0.5
1
*** TP53
0.000
0.150
MUC16
0.060
0.090
17%
TP53
CTNNB1
0.050
0.100
16%
MUC16
16%
CTNNB1
TTN
0.040
0.060
11%
TTN
* CNTNAP5
0.000
0.080
8%
CNTNAP5
HMCN1
0.010
0.060
8%
HMCN1
PKHD1
0.030
0.050
8%
PKHD1
KMT2B
0.010
0.050
7%
KMT2B
NF1
0.010
0.050
7%
NF1
APOB
0.010
0.050
7%
APOB
PRKAR1A
0.040
0.030
7%
PRKAR1A
SVEP1
0.040
0.030
7%
SVEP1
TUT7
0.010
0.050
7%
TUT7
FRAS1
0.030
0.030
5%
FRAS1
LRP1
0.010
0.040
5%
LRP1
STAB1
0.010
0.040
5%
STAB1
ASXL3
0.010
0.040
5%
ASXL3
CMYA5
0.030
0.030
5%
CMYA5
CSMD1
0.010
0.040
5%
CSMD1
DAXX
0.030
0.030
5%
DAXX
C
76%
76%
12q14.3-Amp
76%
12q15-Amp
12q14.3-Amp
12q14.1-Amp
12q15-Amp
75%
5p15.33-Amp
12q14.1-Amp
75%
12q13.2-Amp
5p15.33-Amp
71%
5p13.1-Amp
12q13.2-Amp
69%
5q35.3-Amp
69%
5p13.1-Amp
5p13.2-Amp
68%
5q31.2-Amp
5q35.3-Amp
68%
5p14.1-Amp
5p13.2-Amp
56%
22q12.1-Del
5q31.2-Amp
47%
22q11.21-Del
5p14.1-Amp
43%
1p36.23-Del
22q12.1-Del
41%
17p13.1-Del
22q11.21-Del
40%
29%
13q14.2-Del
1p36.23-Del
4q34.3-Del
** 17p13.1-Del
29%
17q21.31-Del
* 13q14.2-Del
29%
4q35.1-Del
* 4q34.3-Del
29%
11p15.5-Del
** 17q21.31-Del
28%
9p21.3-Del
4q35.1-Del
Stage
Age
pN
pT
Gender
CNA (arm-level)
11p15.5-Del
* 9p21.3-Del
stage i
80
NO
T1
female
Gain
stage ii
60
N1
T2
male
Loss
Frequency
stage iii
40
Unknown
T3
stage iv
Unknown
20
T4
0
Unknown
0.00 0.25 0.50 0.75 1.00
| 0.730 | 0.790 |
| 0.730 | 0.790 |
| 0.730 | 0.790 |
| 0.810 | 0.680 |
| 0.700 | 0.790 |
| 0.780 | 0.630 |
| 0.780 | 0.610 |
| 0.780 | 0.610 |
| 0.760 | 0.610 |
| 0.760 | 0.610 |
| 0.510 | 0.610 |
| 0.430 | 0.500 |
| 0.350 | 0.500 |
| 0.240 | 0.580 |
| 0.240 | 0.550 |
| 0.160 | 0.420 |
| 0.140 | 0.450 |
| 0.190 | 0.390 |
| 0.240 | 0.340 |
| 0.140 | 0.420 |
Figure 6. Mutation and copy number alteration (CNA) analysis in ACC with high and low TOP2A expression (A) Distribution plot showing gene mutations, CNA frequency, and other clinicopathological characteristics (gender, tumor stage, age, pathological T- and N-stage) between high and low TOP2A ACC patients. (B,C) Statistical analysis of gene mutations and CNAs between high- and low-TOP2A ACC patients. (Chi-square and Fisher’s exact tests, P> 0.05, *P < 0.05, ** P < 0.01, *** P < 0.001).
inhibitor etoposide for in vivo experiments. SW13 cells were subcutaneously injected into BALB/c nude mice to induce xenograft tumors. These mice were then randomly divided into four subgroups and orally administered with resminostat or an equal volume of PBS, intraperitoneally injected with etoposide, or treated with a combination of resminostat and etoposide for 14 days (Figure 8C). Our in vivo assays revealed a trend toward resminostat- mediated inhibition of subcutaneous tumor growth, although the difference was not statistically significant. As a selective TOP2A inhibitor, etoposide significantly suppressed tumor growth. When used in combination, the treatment had a pronounced inhibitory
effect on ACC tumors. (Figure 8D-F). These findings provide new evidence for the clinical translation of TOP2A-targeted therapies in ACC.
Discussion
ACC is prone to metastasis and often shows resistance to treatment, with many patients presenting at an advanced stage at diagnosis, leading to a poor prognosis [5]. Mitotane is currently the only chemotherapeutic agent approved for ACC, particularly for patients who are ineligible for surgical resection or those with recurrence or metastasis post-surgery [7]. However, its efficacy varies due to
A
TIMER
TCGA
xCell
TCGA
EPIC
TCGA
MCPcounter
TCGA
0.23
0.18,
p = 0.12
0.15
.
= 0.014
0.22, p
.
CD8+ T Cell infiltration level
R
=
CD8+ T Cell infiltration level
R
=
0.28, p
R =
=
0.056
R
=
0.22,
p
0.053 =
0.22
0,075
:
0.21
0.10
2
·
-…
CD8+ T Cell infiltration level
CD8+ T Cell infiltration level
0.050
0.20
…
0.05
1
0.19
0.025
0.18
0.00
0.000
0
..
H
2
4
6
8
2
4
6
8
2
4
6
8
2
4
6
Expression of TOP2A
Expression of TOP2A
Expression of TOP2A
Expression of TOP2A
8
B
TIMER
GSE76019
xCell
GSE76019
EPIC
GSE76019
MCPcounter GSE76019
0.085
.
.
R
.
0 ).44, p = 0.0
.
CD8+ T Cell infiltration level
=
CD8+ T Cell infiltration level
R
=
0.6
p
=
0.00015
CD8+ T Cell infiltration level
R
=
0.31, p = 0.078
CD8+ T Cell infiltration level
4.0
R
=
0 28
1
0.04
0.080
p
= C
0.24
0.075
3.6
0.02
0.070
0.23
0.065
3.2
0.00
0.060
0.22
IL
M
2.8
6
8
9
10
11
6
7
8
9
10
6
7
8
9
7
8
9
10
Expression of TOP2A
Expression of TOP2A
11
10
11
6
Expression of TOP2A
Expression of TOP2A
11
C
D
GBM-PRJNA482620
TCGA
GSE76019
1
pvalue
1.0
TOP2A
0.088
0.155
High TOP2A_p
Low
Nominal p value
High
0.8
Bonferroni corrected
Survival probability
0.8
0.020
0.001
Low TOP2A_p
0.6
0.5
High TOP2A_b
0.3
0.4
0.160
0.008
Low TOP2A_b
0.0
logrank test p=0.097
0.2
pvalue
PD1-noR
PD1-R
PD1-noR
PD1-R
Number at risk
Low
18
18
12
8
1
High
16
14
8
5
2
0
14
28
42
56
OS(Months)
E
Melanoma-GSE78220
F
Melanoma-GSE91061
G
RCC-Braun_2020
1.0
TOP2A
1.0
TOP2A
1.0
TOP2A
Low
high
Low
high
Low
high
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.5
0.5
0.5
0.3
0.3
0.3
0.0
logrank test p=0.13
0.0
logrank test p=7.9e-3
0.0
logrank test p=5.5e-4
Number at risk
Number at risk
Number at risk
Low
13
1,3
9
6
3
High
33
21
14
7
2
Low
120
7,6
54
31
2
High
13
9
3
1
1
Low
1.6
1,3
11
9
1
High
52
26
10
6
1
0
8
16
24
32
0
9
18
27
36
0
18
36
54
OS(Months)
72
OS(Months)
OS(Months)
individual differences, and it has significant side effects. Thus, identifying new biomarkers and therapeutic targets for ACC is critically important.
TOP2A facilitates the unwinding of the DNA superhelix through transient cleavage and re-ligation of duplex DNA strands, modifying DNA topology [16]. This enzyme is a primary target for several
A
TCGA-ACC
GSE76019
B
TCGA(Adult)
ibrutinib
resminostat
resminostat
tipifarnib
dacinostat
valrubicin
cobimetinib
rociletinib
dacomitinib
cabozantinib
17
tas
afatinib
avasimibe
0.7
tenovins
brexpiprazole
0.6
0.5
devazepide
voapaxar
0.4
7b-cis
importazole
resminostat
0.3
teniposide
scriptaid
3
palbociclib
KIN001-127
-NCS
ponatinib
sunitinib
selumetinib
torin-1
1.85
dicycloverine
oligomycin-a
1.90
1.95
palbociclib
norethynodrel
2.00
saracatinib
crizotinib
lapatinib
taselisib
17
maprotiline
·
sunitinib
etoposide
·
ochratoxin-a
vandetanib
neratinib
sunitinib
·
palbociclib
2.0
1.9
1.8 1.8
-NCS
1.9
-NCS
2.0
GSE76019(Pediatric)
C
Subcutaneous injection
Tumor volum reached 50-100mm3
Ramdon assigned treatment
Sacrifice
Control (Oral gavage)
Resminostat (Oral gavage)
Treatment completion or tumor volume reached 2000 mm3
Etoposide (Intraperitoneal injection)
Etoposide+Resminostat
D
E
Control
F
Resminostat(20mg/kg)
Control
Etoposide(40mg/kg)
**
4000
0
1cm
3
4
7
*
8
Resminostat+Etoposide
ns
Resminostat (20mg/kg)
Tumor volume (mm3)
Tumor weight (mg)
2400
3000
ns
MILJ
0
1cm
3
4
5
6
7
2000
8
9
10
11
12
13
2000
**
Etoposide (40mg/kg)
1600
ns
**
1000
0
1cm
3
4
5
6
7
8
9
10
11
12
13
1200
Resminostat +Etoposide
Ţ
800
0
0
1cm 2
4
S
6
1
8
,
10
11
15
400
*
-1000
Control
Resminostat(20mg/kg)
Etoposide(40mg/kg) Resminostat+Etoposide
0
20
25
30
35
40
45
Time (days)
chemotherapeutic agents, including adriamycin and etoposide, which exploit the role of TOP2A in DNA processing [25]. Notably, TOP2A plays a crucial role in mitigating DNA damage caused by platinum-based drugs [26]. Despite preliminary studies on TOP2A, comprehensive reports integrating multi-center, multi-omics ana- lyses with experiments are lacking. This study aims to address this gap. Our findings indicate that TOP2A is a prognostic risk factor for ACC in both adults and children. Various in vitro assays and clinical samples revealed a significant increase in TOP2A expression in ACC tissues compared with that in their benign counterparts, demon-
strating its oncogenic effect. This evidence highlights the potential of TOP2A as a biomarker in ACC.
In this study, we identified TOP2A as a prognostic risk factor for ACC in adults and children, and high expression of TOP2A is associated with tumor aggressiveness and poor prognosis. The carcinogenic effect of TOP2A was confirmed in vitro, indicating that TOP2A has great potential as a prognostic biomarker of ACC.
According to the results of the multi-omics analysis, the TOP2A subgroup exhibited pronounced mutational heterogeneity, particu- larly mutations in TP53, which were found exclusively in the subset
with elevated TOP2A expression. TP53 is a crucial tumor suppressor gene and a prevalent driver gene in ACC pathogenesis. Moreover, TP53 mutations are adverse prognostic factors in ACC [27]. Within the TP53 mutation subgroup, the TOP2A expression level was greater than that in the TP53 wild-type subgroup. Functional enrichment analysis of ACC data derived from adult patients (TCGA) and children (GSE76019) revealed that activation pathways are predominantly involved in DNA replication, DNA damage repair, and cell cycle processes in both datasets. Research indicates that ACC patients harboring somatic TP53 variants exhibit atypical mitosis, potentially indicative of M phase disorders [28]. In addition, TOP2A can promote tumor progression by activating the AKT/mTOR pathway [29,30]. This interplay underscores the importance of TOP2A in the molecular landscape of ACC and its potential role in guiding personalized medical strategies.
Tumor mutational heterogeneity significantly influences cancer immunotherapy efficacy. Our immune infiltration analysis revealed a negative correlation between TOP2A expression and CD8+ T-cell infiltration in both the adult and pediatric ACC datasets. Subsequent submap analysis and results from multiple immunotherapy cohorts revealed that subgroups with lower TOP2A expression had better responses to immunotherapy. Therefore, TOP2A has potential as a predictive biomarker for immunotherapy effectiveness.
To identify the multifaceted biomarker capabilities of TOP2A in ACC, we sought inhibitors that target its expression subgroups. Among the compounds identified in both the adult and pediatric ACC cohorts are resminostat, palbociclib, and sunitinib. Resmino- stat, an HDAC inhibitor, has orphan drug designation for cutaneous T-cell lymphoma treatment [31]. Palbociclib, a selective inhibitor of CDK4/6, is used for HR-positive, HER2-negative breast cancer [32]. Sunitinib, a multi-targeted receptor tyrosine kinase inhibitor, is used to treat gastrointestinal stromal tumors and metastatic renal cell carcinoma [33,34]. Our in vivo assays demonstrated that while resminostat alone had no apparent effect on tumor growth, its combination with etoposide had a synergistic inhibitory effect. Future pre-clinical studies will explore the therapeutic efficacy and mechanisms of these inhibitors in ACC.
However, our study has several limitations. Although validated in public databases, the number of ACC samples collected is limited, necessitating more samples to corroborate our results. Additionally, the role of TOP2A in ACC has only been verified through in vitro and in vivo assays, and further research is needed to fully elucidate its mechanisms.
In summary, our research introduces a novel prognostic and immunotherapeutic biomarker for ACC. TOP2A has significant potential for enhancing prognostic assessment and therapeutic response evaluation in ACC and developing new therapeutic targets.
Supplementary Data
Supplementary data is available at Acta Biochimica et Biophysica Sinica online.
Data Availability
Clinicopathological information of each ACC patient in the Huizhou cohort that supports the findings of this study is not publicly available due to privacy and ethical restrictions, but the other public data acquired from open-sourced platforms can be requested directly from the TCGA and GEO websites
Funding
This work was supported by grants from the Guangdong Basic and Applied Basic Research Foundation (Nos. 2025A1515010395, 2022A1515010344, and 2023A1515140040), the Huizhou Science and Technology Plan Project in the Medical and Health Fields (No. 2022CZ010004), and the Science and Technology Development Fund, Macau SAR (File nos. 0090/2022/A, 0116/2023/RIA2).
Conflict of Interest
The authors declare that they have no conflict of interest.
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