Identification of Key Genes and Related Drugs of Adrenocortical Carcinoma by Integrated Bioinformatics Analysis
Authors
Jian-bin Wei1 İD , Xiao-chun Zeng1, Kui-rong Ji2, Ling-yi Zhang2, Xiao-min Chen2
Affiliations
1 The Third Clinical Medical College, Fujian Medical University, Fuzhou, China
2 Department of Endocrinology, Zhongshan Hospital Xiamen University, Xiamen, China
Keywords
protein-protein interaction network, differentially expressed genes, targeted drug
received 25.09.2023
accepted after revision 01.11.2023 published online 18.12.2023
Bibliography
Horm Metab Res 2024; 56: 593-603 DOI 10.1055/a-2209-0771 ISSN 0018-5043
@ 2023. Thieme. All rights reserved. Georg Thieme Verlag KG, Rüdigerstraße 14, 70469 Stuttgart, Germany
Correspondence
Prof. Xiao-min Chen Zhongshan Hospital Xiamen University Department of Endocrinology 361000 Xiamen China Tel .: 13860487599 chenxiaomin0517@sina.com
Supplementary Material is available at https://doi.org/10.1055/a-2209-0771
ABSTRACT
Adrenocortical carcinoma (ACC) is a malignant carcinoma with an extremely poor prognosis, and its pathogenesis remains to be understood to date, necessitating further investigation. This study aims to discover biomarkers and potential therapeutic agents for ACC through bioinformatics, enhancing clinical di- agnosis and treatment strategies. Differentially expressed genes (DEGs) between ACC and normal adrenal cortex were screened out from the GSE19750 and GSE90713 datasets avail- able in the GEO database. An online Venn diagram tool was utilized to identify the common DEGs between the two data- sets. The identified DEGs were subjected to functional assess- ment, pathway enrichment, and identification of hub genes by performing the protein-protein interaction (PPI), Gene Ontol- ogy (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The differences in the expressions of hub genes between ACC and normal adrenal cortex were validated at the GEPIA2 website, and the association of these genes with the overall patient survival was also assessed. Finally, on the QuartataWeb website, drugs related to the identified hub genes were determined. A total of 114 DEGs, 10 hub genes, and 69 known drugs that could interact with these genes were identified. The GO and KEGG analyses revealed a close associ- ation of the identified DEGs with cellular signal transduction. The 10 hub genes identified were overexpressed in ACC, in addition to being significantly associated with adverse prog- nosis in ACC. Three genes and the associated known drugs were identified as potential targets for ACC treatment.
Introduction
Adrenocortical carcinoma (ACC) is a malignant form of cancer that originates in the adrenal cortex. ACC presents various histological subtypes, including the conventional, oncocytic, myxoid, and sar- comatoid types. ACC has a low incidence, with just 0.7-2 cases per million people reported each year [1]. However, ACC also has an extremely poor prognosis, with a 5-year overall survival rate of 60-80% for stage I tumors, which reaches as low as 13 % for stage IV cases [2]. The lack of approaches to establish an early diagnosis remains the primary contributing factor to the persistently high mortality rate of ACC. In contrast to other malignancies that have
benefited from the advances in genomics, molecular diagnostics, and liquid biopsy in terms of early detection, the diagnosis of ACC has seen limited progress over the past decade due to the rarity of cases. The existing diagnostic methods for ACC rely predominant- ly on imaging techniques and the detection of hormones and as- sociated metabolites. However, the specificity and sensitivity of these methods remain below the required clinical standards, par- ticularly for the differentiation of ACC from adrenal adenomas, which leads to a high rate of misdiagnosis. Tissue pathology biop- sy is considered the gold standard for ACC diagnosis, although this method has the disadvantages of sampling difficulty, potential for
tumor cell dissemination during sampling, and certain other diag- nostic challenges [3]. Consequently, a significant proportion (40- 70%) of patients miss the optimal treatment window as an early diagnosis is not established [4]. Early-stage ACC is treated primar- ily through radical surgical resection. However, even after radical surgery, nearly one-third of the patients experience local recur- rence or distant metastasis [5]. The other treatment modalities, such as mitotane, platinum-based chemotherapy, targeted thera- py, and radiation, either yield suboptimal therapeutic outcomes or result in high recurrence rates and adverse reactions [6]. These is- sues collectively contribute to rendering the treatment of late- stage ACC challenging, with limited treatment options available. Therefore, exploring strategies to establish early diagnosis and pro- vide efficient treatment is the current focus of the research on ACC. Drugs, such as insulin-like growth factor-1 (IGF-1), sirolimus (mTOR), and various kinase inhibitors, are currently in clinical trials for the evaluation of their efficacy against ACC, although satisfac- tory results have not been achieved so far. Therefore, it is impor- tant to identify further precise and effective drug targets to guide the trials and improve ACC prognosis and treatment.
Microarray data are being increasingly applied to the field of cancer biology in recent times as relevant data from clinical stud- ies and sequencing experiments is readily available in public data- bases. These data may then be analyzed using the bioinformatics approach, and the resulting novel data generated would assist cli- nicians in deepening their understanding of the concerned disease. With the launch of the Human Genome Project, the role of genes in the development and progression of diseases is being explored extensively, and using genetic data to gain insights into the mech- anism of diseases and developing strategies for disease treatment has become a research hotspot. The present study aimed to iden- tify the potential causative genes of ACC and the associated target drugs using bioinformatics methods and genetic databases. First, the adrenocortical carcinoma (ACC)-related datasets were down- loaded from the Gene Expression Omnibus (GEO) database. Next, the online Venn diagram tool was used for identifying the differen- tially expressed genes (DEGs) between ACC and normal adrenal tis- sue samples, followed by the selection of the common differential genes for the two datasets. Subsequently, the identified DEGs were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to reveal the enrichment pathways related to these DEGs. Furthermore, a protein-protein interaction network (PPI) of these DEGs was constructed, which revealed 114 DEGs, 10 hub genes, and 69 corresponding target drugs.
Materials and Methods
Acquisition of the microarray data
The GSE19750 and GSE90713 datasets were downloaded from the GEO database (www.ncbi.nlm.nih.gov) using the GPL570 platform and the GPL15207 platform. The GSE19750 dataset contains 44 ACC samples and four standard adrenal samples. The GSE90713 dataset included 58 ACC samples and five standard adrenal samples.
Screening of the differentially expressed genes
The genes in the GSE19750 and GSE19750 datasets were subject- ed to the statistical thresholds of p < 0.05 and | logFC | ≥2 to ob- tain the DEGs, and the screened-out genes were introduced into the online Venn diagram tool to identify the intersecting DEGs. Log FC≤-2 indicated downregulated genes and log FC ≥ 2 indicated upregulated genes.
GO and KEGG analyses
GO analysis is based on the multi-faceted annotation of genes from the perspectives of cell biology and molecular biology. KEGG is a comprehensive public database that integrates data on genomes, signaling pathways, enzymology, etc. In the present study, the re- sults of the GO and KEGG analyses in relation to adrenocortical car- cinoma were extracted using the DAVID online tool, and the data of the top three biological process (BP), cellular constituent (CC), and molecular function (MF) and top eight KEGG pathways were ob- tained after removing the analysis data with p≥0.05, respectively.
PPI network construction and screening of key genes
Protein interactions are crucial for the development of a disease. In this regard, the construction of PPI networks assists in under- standing the interactions between various biomolecules at the source level, revealing the key regulatory genes for deciphering the molecular association between tumorigenesis and progression. In the present study, the identified DEGs were used for constructing a PPI network using STRING, which is the most important protein interaction database available to date. The constructed PPI network was then visualized and analyzed using the Cytoscape software (version 3.10.0). The hub genes were identified from the network using the cytoHubba plug-in in the same software.
Prognostic analysis of the identified hub genes in GEPIA2
GEPIA2 (http://gepia2.cancer-pku.cn) is an online tool based on the TCGA and GTEx databases, and in the present study, this tool was used for determining the association of overall survival (OS) with the identified hub genes based on the generated Kaplan- Meier curves (KM). The objective was to assist in understanding the effect of key gene expression levels on disease survival.
Screening of the drugs targeting the identified key genes
QuartataWeb (http://quartata.csb.pitt.edu/) is an open-to-public website, which enables uploading key genes to screen for the drugs targeting these genes among all the existing already reported drugs and compounds.
Results
Identification of DEGs in ACC
The screening of the microarray data based on the statistical thresholds followed by the Venn diagram intersection analysis to determine the DEGs common between the GSE90713 and GSE19750 datasets revealed a total of 114 DEGs, including 21 up- regulated and 93 downregulated genes ( Fig. 1, > Table 1).
a
b
GSE90713
GSE90713
29
21
803
59
93
593
GSE19750
GSE19750
| DEGs | Gene name |
|---|---|
| Upregulated | GGH, CCNB1, CALB1, PLA2G1B, ANLN, UBE2C, CCNE1, CCNB2, PRC1, FAM19A4, CDK1, TOP2A, CENPU, GJC1, TYMS, ZWINT, KIAA0101, CDKN3, NCAPG, TWIST1, SPINK13 |
| Downregulated | MUM1L1, TAGLN, IGF1, CD163, LAMA2, LUM, C1S, C1R, FNDC4, F13A1, OGN, GIPC2, HTR2B, PTGDS, SUGCT, TGM2, CLDN11, AADAC, CD55, PLN, PLAT, IGFBP6, LYVE1, MRAP, GSTA4, DDX3Y, SLC37A2, PLIN2, DNASE1L3, SERPING1, AKAP12, HSD3B2, CXCL12, ALDH1A1, CYP11B2, MC2R, PAPPA, MT1M, WISP1,COLEC11,IL1RL1,CYP11B1, MGST1, INMT, ABCB1, BRE, SORBS2, CDKN1C, SYTL5, MT1G, FMO2, STEAP4, EIF1AY, TMOD1, FBLN1, SLC16A9, PDGFD, ADH1B, SPON1, PLA2G4A, LMOD1, RARRES1, FABP4, PDGFRA, GPX3, HOXA5, NPY1R, C11orf96, VSIG4, RSPO3, PLCXD3, ECHDC3, DCN, PTX3, WFDC1, DNAJC12, ADGRV1, RPS4Y1, C7, MRC1, AOX1, ALAS1, IGFBP5, CYP1B1, PARM1, ABLIM1, KCNQ1, ANGPTL1, PTGIS, FBLN5, EFEMP1, S100A9, CD14 |
GO and KEGG analyses of the identified DEGs
The GO analysis revealed that the identified DEGs were mainly en- riched in response to drug, cell division, and innate immune re- sponse pathways among all BPs. In regard to CC, the enrichment of DEGs was noted in the extracellular region, extracellular space, and extracellular exosome, which together accounted for approx- imately 89.99% of the DEGs. In regard to MF, the DEGs were en- riched mainly in protein binding, identical protein binding, and cal- cium ion binding. The KEGG analysis revealed that the DEGs exhib- ited high enrichment in complement and coagulation cascades, arachidonic acid metabolism, drug metabolism-cytochrome P450, p53 signaling pathway, prostate cancer, cell cycle, oocyte meiosis, and Cushing syndrome (> Table 2).
PPI network construction and screening of hub genes
The identified 114 DEGs were subjected to screening using the Cy- toscape software, which revealed a total of 86 closely associated genes with 228 edges ( Fig. 2a). The 10 hub genes ( Fig. 2b) identified using the CytoHubba plug-in were CCNB1, TOP2A, UBE2C, ANLN, CDK1, CCNB2, TYMS, KIAA0101, PRC1, and CDKN3, which were all upregulated in ACC.
Expression and survival analyses of the hub genes in ACC
Expression analysis of the hub genes on the GEPIA2 website re- vealed that all ten hub genes were overexpressed in ACC ( Fig. 3a), suggesting a high probability of these genes being associated with the development of ACC. The survival analysis revealed that the overall survival (OS) of ACC patients was significantly correlated with the expression level of these hub genes (p<0.05). The surviv- al of the patients with a high expression of the hub genes was con- siderably decreased ( Fig. 3b).
Identification of the drugs targeting the hub genes
The 10 hub genes were imported into the QuartataWeb website, and the drugs targeting each of these genes were screened out among all the existing drugs and compounds available on the web- site. The following drugs were revealed for three hub genes: (1) A total of 38 drugs with known interactions were identified for the TOP2A gene, including 26 approved drugs. Among these 26 drugs, 11 are used for oncology treatment; (2) A total of 22 drugs with known interactions were identified for TYMS, including 11 ap- proved drugs. Ten of the 11 identified drugs are used in oncology treatment; and (3) Nine drugs with known interactions were iden- tified for CDK1, including one approved drug (> Table 3). The re- maining seven genes did not have any drug matches.
| Category | Term | Count | % | p-Value |
|---|---|---|---|---|
| BP | GO: 0042493 - response to drug | 9 | 8.181818182 | 2.00E-04 |
| GO: 0051301 - cell division | 8 | 7.272727273 | 0.004937458 | |
| GO: 0045087 - innate immune response | 8 | 7.272727273 | 0.044987861 | |
| CC | GO: 0005576 - extracellular region | 36 | 32.72727273 | 3.73E-10 |
| GO: 0005615 - extracellular space | 32 | 29.09090909 | 1.02E-08 | |
| GO: 0070062 - extracellular exosome | 31 | 28.18181818 | 7.63E-07 | |
| MF | GO: 0005515 - protein binding | 83 | 75.45454545 | 0.010045582 |
| GO: 0042802 - identical protein binding | 18 | 16.36363636 | 0.014738191 | |
| GO: 0005509 - calcium ion binding | 13 | 11.81818182 | 0.001036052 | |
| KEGG | hsa04610: Complement and coagulation cascades | 8 | 7.272727273 | 3.97E-06 |
| hsa00590: Arachidonic acid metabolism | 5 | 4.545454545 | 0.00136302 | |
| hsa00982: Drug metabolism - cytochrome P450 | 5 | 4.545454545 | 0.002371084 | |
| hsa04115: p53 signaling pathway | 5 | 4.545454545 | 0.002493941 | |
| hsa05215: Prostate cancer | 5 | 4.545454545 | 0.00690367 | |
| hsa04110: Cell cycle | 5 | 4.545454545 | 0.016858879 | |
| hsa04114: Oocyte meiosis | 5 | 4.545454545 | 0.019173366 | |
| hsa04934: Cushing syndrome | 5 | 4.545454545 | 0.032966716 |
BP: Biological process; CC: Cellular component; MF: Molecular function.
Discussion
Adrenal cortical carcinoma progresses rapidly, with the advanced tumors having a terrible prognosis. In the present study, 114 DEGs were identified in the data obtained from the GEO database using online tools, such as Venn, DAVID, and String, combined with bio- informatics methods. The GO analysis revealed that the identified DEGs were enriched mainly in pathways related to response to the drug, cell division, and innate immune response among BP. Among MF, protein binding, identical protein binding, and calcium ion binding occupied a higher proportion of these DEGs. Among CC, the DEGs were mainly enriched in the extracellular region, extra- cellular space, and extracellular exosome. According to the KEGG analysis, complement and coagulation cascades, arachidonic acid metabolism, drug metabolism-cytochrome P450, p53 signaling pathway, prostate cancer, cell cycle, oocyte meiosis, and Cushing syndrome occupied the top eight rankings. Next, the PPI network was constructed and then visualized using the CytoHubba plug-in, and from this network, 10 hub genes, namely CCNB1, TOP2A, UBE2C, ANLN, CDK1, CCNB2, TYMS, KIAA0101, PRC1, and CDKN3, were identified. All ten hub genes were among the upregulated genes. The graph plotted to illustrate the relationship between the expression levels of these hub genes and survival analysis outcomes revealed that the OS rate was significantly decreased in patients with high expression of the hub genes. Finally, the potential drugs targeting the hub genes were explored, which revealed 69 poten- tial drugs targeting TOP2A, CDK1, and TYMS genes. These findings
provided novel ideas to improve upon the current surgical thera- py-based treatment options for ACC.
As stated earlier, the main biological process (BP) associated with the DEGs identified in the present study were drug response, cell division, and immune response, indicating the close associa- tion of these genes with cellular adaptation to the external envi- ronment. Consistent with this finding, previous research has also highlighted the significant roles of cell division and immune re- sponse in ACC. For instance, according to the Weiss scoring system, which summarizes extensive literature evidence, tumor cell divi- sion activity is the most crucial factor influencing the survival rate of ACC patients. Earlier studies have indicated that over 90% of ACC cases exhibit marked nuclear atypia, and p27, a key inhibitory pro- tein regulating cell division, has been identified as the highest ex- pressed biomarker in ACC tissues [7]. These findings collectively highlight the importance of cell division in ACC. Immunological dysregulation contributes to the pathogenesis of several tumors, and ACC is no exception. Phagocytic macrophages, which form a vital component of innate immunity, have been demonstrated to limit tumor progression in ACC, which potentially explains the high- er incidence of ACC in females due to the androgen-dependent re- cruitment and differentiation of these macrophages [8]. In the pres- ent study, protein binding and calcium ion binding were identified as the major molecular function (MF) in which the identified DEGs were enriched. These functions are closely related to cellular signal transduction. The literature also reports that protein interactions, such as the binding of the Wnt protein to the Frizzled receptor, may lead to disrupted cell proliferation. In addition, binding of MEN1 to
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| SLC37A2 | NCAPG | CYP11B1 | CYP1B1 | MUM1L1 | PLA2G4A | PDGFD | PAPPA | FBEN5 |
|---|---|---|---|---|---|---|---|---|
| HOXA5 | EFEMP1 | GSTA4 | SERPING1 | PRC1 | CD163 | TMOD1 | ADH1B | SPON1 |
| SYTL5 | MC2R | TAGLN | ABCB1 | COLEC11 | PARM1 | C1R | MRAP | TGM2 |
| ABLIM1 | CD14 | C1S | MRC1 | PLA2G1B | GPX3 | CENPU | LAMA2 | BRE |
| TYMS | MT1G | VSIG4 | FBLN1 | KIAA0101 | LUM | CCNB1 | LYVE1 | OGN |
| KCNQ1 | MT1M | CLDN11 | PDGFRA | MGST1 | PTGDS | GGH | F13A1 | FABP4 |
| ANLN | PTX3 | PLAT | CXCL 12 | GIPC2 | CDK1 | SLC16A9 | ZWINT | PLIN2 |
| TOP2A | CYP11B2 | C7 | CD55 | LMOD1 | PTGIS | CCNE1 | CDKN3 | IGF1 |
| CCNB2 | S100A9 | TWIST1 | UBE2C | CDKN1C | IGFBP6 | WISP1 | ALDH1A1 | IGFBP5 |
| HSD3B2 | DCN | AKAP12 | AOX1 |
a
b
TYMS
TOP2A
PRC1
KIAA0101
ANLN
UBE2C
CCNB1
CCNB2
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transcription factors inhibits the transcriptional activity of MEN1, thereby contributing to tumor growth. Both of the above mecha- nisms have been confirmed as components of ACC pathogenesis [9]. Calcium ions play a crucial role in the synthesis and release of the adrenal cortex hormone, and alterations in the intracellular cal- cium ion concentration following cellular depolarization stimulate the production of hormones such as aldosterone [10]. In terms of cellular component (CC), the enrichment analysis results indicated a close connection to the tumor microenvironment, which has ex- tracellular region and extracellular vesicles as important compo- nents that play critical roles in immune evasion and cortisol secre- tion in ACC [11, 12]. Overall, the results of the Gene Ontology (GO) analysis suggested that cellular signal transduction could have a significant role in the pathogenesis of ACC. The KEGG analysis re- vealed the key pathways involved in the occurrence and progres- sion of ACC, including drug metabolism, cytochrome P450, p53 signaling pathway, cell cycle, and Cushing syndrome. Cytochrome P450 is significantly overexpressed in ACC and thereby contributes to enhanced chemoresistance [13]. P53 is a tumor suppressor pro-
tein, which is frequently reported to be mutated in ACC, leading to abnormal cell growth and division. Research suggests that target- ing p53 could be an effective therapeutic approach against ACC [14]. Furthermore, dysregulation of the cell cycle contributes to the accumulation of tumor mutation burden during the onset of ACC [15]. Cushing syndrome is associated with excessive cortisol secretion and serves as a crucial indicator of adverse prognosis in ACC [16]. In summary, the GO and KEGG analyses revealed the key players in the occurrence and progression of ACC.
CCNB1, which is also referred to as cyclin B1, belongs to the cy- clin family and plays a vital role in regulating cell mitosis [17]. CCNB1 binds to CDK1 during mitosis, forming a complex that re- mains in the nucleus under the action of p21 to promote the tran- sition of the cell growth cycle from G2 to M phase, thereby accel- erating cell mitosis and further promoting the proliferation and dif- ferentiation of tumor cells [18]. Previous research revealed that CCNB1 was highly expressed in a variety of cancers, such as colorec- tal, gastric, liver, and cervical cancers [19-22]. Recent years have witnessed an increase in the number of studies on ACC, and among
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these studies, one study involving survival analysis and multivari- ate COX regression analysis revealed that high CCNB1 expression is positively associated with high mortality in patients with ACC [hazard ratio (HR), 6.13; 95 % confidence interval (CI), 1.02 to 36.7] [23], which is consistent with the results of the present study.
CCNB2 belongs to the same family of cell cycle proteins as CCNB1 and is generally overexpressed in various malignancies. CCNB2 remains localized to the Golgi apparatus during mitosis, suggesting a role different from that of CCNB1 in mitosis [24]. Sev- eral studies have indicated that CCNB2 promotes the eukaryotic mitotic cycle transition from G2 to M phase through the activation
of CDC2 kinase [25]. The inhibition of CCNB2 in the cell cycle could disable the G2/M checkpoint, which then leads to gene mutations and tumorigenesis [26]. According to the data from related stud- ies, CCNB2 expression is 5.6-14-fold higher in ACC compared to that in adrenal adenomas. RT-qPCR results from a study also re- vealed a higher expression of CCNB2 mRNA in ACC, and this phe- nomenon is reportedly more common in cases with TP53 somatic cell variants. The above findings suggest that high CCNB2 expres- sion in ACC could be associated with TP53 somatic variants and atypical mitotic figures [27].
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| Gene | Drug ID | Drug name | Drug type | Drug group |
|---|---|---|---|---|
| TOP2A | DB00997 | Doxorubicin | Small Molecule Drug | Approved; Investigational |
| DB06263 | Amrubicin | Small Molecule Drug | Approved; Investigational | |
| DB00444 | Teniposide | Small Molecule Drug | Approved | |
| DB01204 | Mitoxantrone | Small Molecule Drug | Approved; Investigational | |
| DB00970 | Dactinomycin | Small Molecule Drug | Approved; Investigational | |
| DB01177 | Idarubicin | Small Molecule Drug | Approved | |
| DB00445 | Epirubicin | Small Molecule Drug | Approved | |
| DB00773 | Etoposide | Small Molecule Drug | Approved | |
| DB00276 | Amsacrine | Small Molecule Drug | Approved; Investigational | |
| DB00694 | Daunorubicin | Small Molecule Drug | Approved | |
| DB00385 | Valrubicin | Small Molecule Drug | Approved | |
| TYMS | DB06813 | Pralatrexate | Small Molecule Drug | Approved; Investigational |
| DB00441 | Gemcitabine | Small Molecule Drug | Approved | |
| DB01101 | Capecitabine | Small Molecule Drug | Approved; Investigational | |
| DB00322 | Floxuridine | Small Molecule Drug | Approved | |
| DB00642 | Pemetrexed | Small Molecule Drug | Approved; Investigational | |
| DB09327 | Tegafur-uracil | Small Molecule Drug | Approved; Investigational | |
| DB00544 | Fluorouracil | Small Molecule Drug | Approved | |
| DB09256 | Tegafur | Small Molecule Drug | Approved; Investigational | |
| DB00432 | Trifluridine | Small Molecule Drug | Approved; Investigational | |
| DB00293 | Raltitrexed | Small Molecule Drug | Approved; Investigational | |
| CDK1 | DB12010 | Fostamatinib | Small Molecule Drug | Approved; Investigational |
Anillin actin-binding protein (ANLN) is involved in the coding of the actin-binding protein, which comprises 1125 amino acids and is an essential regulator of the cell division process [28]. In late cy- tokinesis, ANLN binds to other furrow proteins in the cleavage fur- row to form a contractile ring, which is critical for proper cell divi- sion [29]. According to the cohort study data in the TCGA database, ANLN is overexpressed in a variety of malignant tumors, including ACC, and its overexpression is significantly related to low OS rates for tumors [30]. ANLN promotes tumorigenesis and progression by regulating cell growth, migration, and cytokinesis [31]. The pan-cancer analysis of ANLN revealed that it was a reliable refer- ence standard for tumor screening and personalized treatment [32].
Cyclin-dependent kinase 1 (CDK1) belongs to a family of cell cycle kinases that are involved in the transition of the cell cycle from the G2 to M phase of mitosis, and upon abnormal regulation, uncon- trolled cell proliferation would occur, which would eventually cause tumorigenesis [33]. In ACC, high expression of CDK1 accelerates the epithelial-mesenchymal transition (EMT) while promoting cell pro- liferation, in addition to altering the cell cycle by regulating the in- teractions of UBE2C and AURKA/B [34]. An in vitro experiment ex- ploring the interaction between CDK1 and centromeric protein (CENPF) revealed that the G2/M phase transition and cell prolifera- tion were activated during mitosis in the ACC cell line SW13 under the effects of CDK1 and CENPF, which could essentially explain the
poor prognosis of ACC [35]. Currently, several CDK1 inhibitors for tumor treatment have reached the clinical trial stage [36].
Cyclin-dependent kinase inhibitor 3 (CDKN3) is a vital regulator of the cell cycle and a member of the dual-specificity protein phos- phatase family. Interestingly, CDKN3 could bind to CDK1/CDK2 and facilitate the dephosphorylation of the CDK activation residues, ul- timately leading to reduced CDK activity [37]. Therefore, CDKN3 was considered an important tumor suppressor previously. How- ever, CDKN3 is overexpressed in a variety of cancers, including gas- tric cancer, pancreatic ductal adenocarcinoma (PDAC), and lung adenocarcinoma, and has a strong association with poor progno- sis [38-40]. It is reported that CDKN3 promotes tumorigenesis and tumor progression via two pathways [41]. The first pathway in- volves the inhibition of the phosphorylation of Rb protein through specific binding to CDK2. The second pathway involves the forma- tion of a complex with MDM2 and the p53 protein to block the in- duction of the p21 protein. CDKN3 was reported to exhibit signif- icantly higher transcript levels in ACC tissues compared to healthy adrenal tissues [42], which is consistent with the findings of the present study. Unfortunately, studies that have explored how CDKN3 accelerates ACC progression are scarce, and the specific pathogenic pathways remain unknown to date. Therefore, com- mencing the exploration with the two pathways discussed in the present study might be useful.
KIAA0101 is a proliferating cell nuclear antigen (PCNA)-associ- ated factor that is involved in the regulation of DNA replication and cell proliferation through PCNA binding [43]. In recent years, KIAA0101 has been demonstrated to promote tumor progression by inducing the initiation of the Wnt/B-catenin signaling pathway and stimulating the epithelial-mesenchymal transition [44]. In one study, the levels of KIAA0101 mRNA in ACC, normal adrenal cor- tex, and benign adrenal cortical tumors were detected using re- al-time quantitative PCR, and the results revealed that the KIAA0101 mRNA levels in ACC were 12-fold and 9-fold higher than those in the normal adrenal cortex and benign adrenal cortical tu- mors, respectively (p<0.0001). In the same study, the expression of KIAA0101 in ACC was inhibited through gene knockdown, and it was revealed that the growth rate and cell invasiveness of adren- ocortical carcinoma cells were significantly decreased after gene knockdown, which suggested that KIAA0101 has a role in promot- ing ACC progression [45]. In the present study, KIAA0101 was iden- tified as one of the hub genes.
PRC1 (protein regulating cytokinesis-1) belongs to a family of microtubule-associated proteins involved in spindle formation and cytoplasmic division in mitosis. The expression of PRC1 was report- ed to be significantly increased in the S phase, and G2/M phase of the cell cycle, and a sharp decline was noted in its expression when the cells entered the G1 phase at the end of mitosis, suggesting that PRC1 is a critical cell cycle regulator [46]. In another study, the number of cancer cells blocked at the G2/M phase was higher after the knockdown of PRC1 in colon cancer, which reduced the inva- sive ability of colon cancer [47]. Previous studies on tumors report cytoplasmic division failure as a significant risk factor for tumor de- velopment and progression, and PRC1 overexpression may lead to impaired cytoplasmic division and chromosomal instability, which could be one of the oncogenic mechanisms of PRC1 [48]. In addi- tion, PRC1 overexpression reportedly activates the Wnt/B-catenin signaling pathway to promote the progression of hepatocellular carcinoma and lung adenocarcinoma [49, 50].
Topoisomerase II alpha (TOP2A) is a DNA topoisomerase, which exhibits a cell cycle-dependent expression that peaks in the G2/M phase [51]. TOP2A plays an indispensable role in DNA metabolism, including DNA replication and transcription. In particular, during transcription, TOP2A significantly affects chromosome metabo- lism by altering the topological state of DNA [52]. According to a study, TOP2A and Murine double minute 4 combined could pro- mote the expression of one another, leading to decreased activity of the tumor suppressor p53 and increased cancer cell invasiveness [53]. Previous studies revealed that TOP2A expression was signifi- cantly elevated in the ACC cell lines (NCI-H295R and SW13) and that silencing TOP2A expression decelerated the proliferation of ACC cells and significantly inhibited anchorage-independent growth and invasiveness [54]. In the present study, TOP2A was identified as a hub gene, and 11 tumor suppressor drugs were re- vealed to have this gene as their target.
TYMS encodes thymidylate synthase (TS), which facilitates the conversion of deoxyuridine to thymidylate, thereby playing an im- portant role in DNA synthesis and repair [55]. TYMS is reportedly overexpressed in a variety of tumors, and high TYMS expression is associated closely with cancer cell aggressiveness [56]. It is pro- posed that TYMS could promote tumor progression through EMT
[57]. Currently, TYMS inhibitors, such as 5-fluorouracil, are used widely in tumor treatment. The PPI network constructed in the present study confirmed that TYMS interacts with several protein molecules that promote ACC progression and that TYMS raises the tumor grade and reduces the overall survival in ACC [58].
UBE2C belongs to the E2 ubiquitin-conjugating enzyme family, which is involved in protein ubiquitination and promotes the deg- radation of the tumor suppressor p53. In addition, UBE2C inacti- vates CCNB during mitosis and regulates the cell cycle transition from M-phase to G1-phase [59]. UBE2C overexpression in endo- metrial carcinoma led to accelerated cell proliferation and EMT lev- els [60]. In gastric cancer, silencing UBE2C decelerated cell growth and inhibited cancer cell DNA replication [61]. The pan-cancer anal- ysis of UBE2C revealed that its expression levels were associated closely with the pathological stage, overall survival, and poor prog- nosis in ACC patients [62]. In the present study, UBE2C was identi- fied as one of the hub genes.
Despite the insightful findings of the present study, identifying hub genes using bioinformatics methods is just the first step and may not be sufficient to address all the challenges encountered in the treatment of ACC. The next would, therefore, be to explore and develop drugs targeting these hub genes. In the present study, QuartataWeb was employed to identify drugs that could target the hub genes, which revealed drugs targeting TOP2A, CDK1, and TYMS genes.
In the drug screening step of the present study, 26 approved drugs were identified for TOP2A, 11 of which are considered essen- tial for tumor treatment, such as Doxorubicin, which is an antitu- mor anthracycline antibiotic used most commonly for treating leu- kemia, lymphoma, bladder cancer, and breast cancer [63]. Amru- bicin is used mainly for the treatment of non-small cell lung carcinoma and small cell lung carcinoma, particularly for recurrent small cell lung carcinoma, with its efficacy proven clinically [64]. Teniposide is a derivative of podophyllotoxin and is used mainly for the treatment of lymphocytic leukemia and central nervous sys- tem lymphoma [65]. Mitoxantrone is an anthraquinone derivative that is used mainly for the treatment of metastatic breast cancer and acute leukemia in the clinic [66]. Dactinomycin was one of the first antibiotics to be reported to exhibit anticancer activity and is used mainly for treating cancers such as gestational trophoblastic carcinoma, testicular cancer, and ovarian cancer [67]. Idarubicin is one of the most commonly used anthracyclines in acute myeloid leukemia, and it offers the advantages of low drug resistance and low cardiotoxicity [68]. Epirubicin is the most critical drug for breast cancer chemotherapy, and it leads to fewer side effects compared to doxorubicin [69]. Etoposide is a derivative of the same podo- phyllotoxin as Teniposide, although the former has a broader range of applications. Currently, Etoposide is used mainly for treating can- cers such as Kaposi’s sarcoma, non-lymphocytic leukemia, lung cancer, and testicular cancer [70]. Amsacrine is a classic topoi- somerase inhibitor that was used previously for treating acute leu- kemia [71]. Daunorubicin is an antitumor drug with a long history of application in the treatment of various cancers, including acute granulocytic leukemia, breast cancer, and ovarian cancer [72]. Val- rubicin is an anthracycline antitumor agent used primarily for the in situ treatment of refractory bladder carcinoma [73].
A total of 11 approved drugs were identified for TYMS, 10 of which are used for cancer treatment. One among these drugs is Pralatrexate, which is a novel anti-folate drug that was approved by the FDA in 2009 for the treatment of peripheral T-cell lymphoma. This drug presents a significantly reduced probability of myelosup- pression compared to methotrexate [74]. Gemcitabine is one of the most widely used pyrimidine analogues, which has been ad- ministered as the first-line treatment for advanced pancreatic can- cer for over 20 years, although with progress in research, it has been discovered that Gemcitabine is also applicable to breast and non- small cell cancers [75]. Capecitabine is an anti-metabolic agent, which is currently used primarily for treating breast cancer and gas- trointestinal tract tumors [76]. Floxuridine is a nucleoside analog, and studies have demonstrated that Floxuridine-based chemother- apy regimens preserve fertility in patients with trophoblastic tum- ors during pregnancy [77]. Pemetrexed is a novel anti-folate drug that is used frequently in the clinic for the treatment of advanced non-small cell lung cancer [78]. Tegafur-uracil is a fluoropyrimidine antitumor agent, which significantly improves the overall survival rate of patients with advanced oral cancer [79]. Fluorouracil, also referred to as 5-Fluorouracil, is the active metabolite of capecit- abine, with antitumor effects against a variety of cancers, includ- ing colorectal, breast, and gastric cancers [80]. Tegafur is a fluoro- pyrimidine drug used for the oral treatment of gastric cancer, and this drug may be administered in combination with cisplatin [81]. Trifluridine induces cellular senescence by inhibiting endothelial cell autophagy and autophagy flux, and this drug is used in the clin- ic for treating advanced colorectal cancer [82]. Raltitrexed is a TS inhibitor that promotes apoptosis in gastric cancer cells through RSK4 upregulation [83].
CDK1 is currently under screening for just one targeted drug named Fostamatinib, which is a spleen tyrosine kinase inhibitor used for treating chronic immune thrombocytopenia in the clinic [84]. Its antitumor effects are also being explored in certain stud- ies, which have revealed, to date, that Fostamatinib reduces the levels of the tumor proliferation marker Ki-67 by inhibiting the sig- nal transduction of B-cell receptors and nuclear transcription fac- tors [85]. Fostamatinib is, therefore, a promising next-generation antitumor drug.
Conclusion
Ten genes, namely, CCNB1, CCNB2, ANLN, CDK1, CDKN3, KIAA0101, PRC1, TOP2A, TYMS, and UBE2C, were identified as potential mark- ers for the diagnosis, prognosis, and treatment of ACC using the bioinformatics approach. In addition, drugs targeting three of these 10 genes, namely, TOP2A, TYMS, and CDK1, could be identified for use in the ACC clinical studies to assist in deciphering the molecu- lar mechanisms and metabolic changes associated with ACC. Nev- ertheless, the present study relied primarily on the existing biolog- ical databases and information, due to which it lacks the direct functional validation of the identified potential targets. Conse- quently, the biological roles of the identified genes are less eluci- dated, thereby necessitating additional laboratory experiments to validate the findings of the bioinformatics analyses and obtain fur- ther specific functional information.
Conflict of Interest
The authors declare that they have no conflict of interest.
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