Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. The presently available understanding of the pathogenesis of ACC is incomplete and the treatment options for patients with ACC are limited. Gene marker identification is required for accurate and timely diagnosis of the disease. In order to identify novel candidate genes associated with the occurrence and progression of ACC, the microarray datasets, GSE12368 and GSE19750, were obtained from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, and functional enrichment analysis was performed. A protein-protein interaction network (PPI) was constructed to identify significantly altered modules, and module analysis was performed using Search Tool for the Retrieval of Interacting Genes and Cytoscape. A total of 228 DEGs were screened, consisting of 29 up and 199 downregulated genes. The enriched functions and pathways of the DEGs primarily included ‘cell division’, ‘regulation of transcription involved in G1/S transition of mitotic cell cycle’, ‘G1/S transition of mitotic cell cycle’, ‘p53 signaling pathway’ and ‘oocyte meiosis’. A total of 14 hub genes were identified, and biological process analysis revealed that these genes were significantly enriched in cell division and mitotic cell cycle. Furthermore, survival analysis revealed that
Adrenocortical carcinoma (ACC) is a rare endocrine malignancy that arises from the adrenal cortex, with an occurrence rate of 0.7–2.0 cases per 1,000,000 each year (
At present, the diagnosis and classification of adrenocortical cancers relies on histological examination of tumor sections and immunohistochemical markers, such as Ki-67, IGF2 and SF-1, are used to support the diagnosis of ACC (
Following developments in microarray technology, several studies have demonstrated that abnormal expressed and mutated genes are involved in the tumorigenesis and progression of ACC (
The aim of the present study was to analyze the differentially expressed genes (DEGs) in ACC by combining two mRNA microarray datasets from the Gene Expression Omnibus (GEO) database. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network analyses were performed to provide detailed insights into the biological mechanisms in ACC. GEO and Oncomine databases were subsequently combined to validate the importance of the hub genes. In conclusion, using bioinformatics methods, the present study identified 14 hub genes which provided significant diagnostic and prognostic value and may serve as candidate biomarkers for ACC.
The gene expression data was retrieved from the GEO database (
The DEGs between ACC and normal adrenal gland samples were screened using GEO2R (
The Database for Annotation, Visualization and Integrated Discovery (DAVID;
The PPI network was predicted using the Search Tool for the Retrieval of Interacting Genes (STRING;
Hub genes with a degree of ≥10 were selected, and the network of the genes and their co-expression genes was analyzed using cBioPortal (
Following standardization of the microarray results by GEO2R, 970 and 998 DEGs were identified in the GSE12368 and GSE19750 gene expression profile datasets, respectively. The overlap between the 2 datasets contained 228 genes, as illustrated in the Venn diagram (
To investigate the functional annotation of the DEGs, GO terms and pathway enrichment analysis were performed using DAVID. The results indicated that the BPs of DEGs were significantly enriched in ‘Cell division’, ‘Regulation of transcription involved in G1/S transition of mitotic cell cycle’, ‘G1/S transition of mitotic cell cycle’, ‘aging’ and ‘signal transduction’ (
A total of 113 DEGs, consisting of 19 up and 94 downregulated genes, were filtered into the PPI network using the STRING database. The network contained 113 nodes and 272 edges, and were visualized using Cytoscape software (
A total of 14 genes were identified as hub genes (degrees ≥10). A network of the hub and their co-expression genes was analyzed using the cBioPortal online platform and is illustrated in
ACC is associated with a poor prognosis, limited treatment options and high tumor recurrence rates (
Microarray technology combined with bioinformatics analysis has enabled researchers to explore genetic alterations, and has been proven to be a useful approach in identifying novel biomarkers in several diseases, such as hepatocellular carcinoma and adrenocortical tumors (
Some of these hub genes have previously been identified as biomarkers for ACC (
Although six other hub genes (
Among these six hub genes, the elevated expression of
In conclusion, the present study identified and analyzed key biomarkers in ACC using bioinformatics analysis. Two databases were combined to screen 228 DEGs, and 14 hub genes, which may be regarded as powerful and promising biomarkers for predicting tumorigenesis and progression of ACC, were identified. These hub genes were associated with tumor cell proliferation and cell cycle regulation. Of note, candidate hub gene upregulation was associated with a worse survival rate and higher Weiss grade; and this may provide a basis for further clinical molecular target therapy experiments and diagnostic approaches for ACC, if these potential genes are developed as novel useful diagnostic as well as prognostic markers and the underlying pathological causative pathways or involved signaling targets are elucidated. However, further studies are required to confirm the biological functions and mechanisms of action of these genes in ACC.
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The present study was supported by grants from the National Science Foundation of China (grant nos. 81660138 and 81860146).
The datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request.
ZX and ZL conceived and designed the study, analyzed the data and drafted the manuscript. HY and ZH collected the data and performed the statistical analysis. XL was responsible for drawing the figures, and help designed the bioinformatics study. All authors read and commented on the manuscript.
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The authors declare that they have no competing interests.
Venn diagram, PPI network and the most significant module of DEGs. (A) DEGs were selected with a fold change >2 and an adjusted P<0.05 from GSE12368 and GSE1975 mRNA expression profiling sets. The 2 datasets revealed that there was an overlap of 228 genes. (B) The PPI network of DEGs was constructed using Cytoscape software and the 19 upregulated genes were marked in light red and the 94 downregulated genes are marked in light blue. (C) The most significant module was obtained from the PPI network which contained 14 nodes and 88 edges. PPI, protein-protein interaction network; DEGs, differentially expressed genes.
Interaction network and BPs of the hub genes. (A) The most significant BPs of the hub genes (P<0.05). (B) The hub genes and their co-expression genes were analyzed using cBioPortal. Nodes with a bold black outline represent hub genes, whereas nodes with a thin black outline represent the co-expression genes. (C) Hierarchical clustering of hub genes was constructed using UCSC. The upregulated genes are marked in red and downregulated genes in blue. The samples were all ACC primary samples. The tumor stages are presented as follows: Light purple bar, stage I; blue bar, stage II; red bar, stage III; and orange bar, stage IV. BP, biological process; ACC, adrenocortical carcinoma.
(A) Overall and (B) disease-free survival analysis of hub genes was performed using the cBioPortal online platform. P<0.05 was considered to indicate a statistically significant difference.
Association between the expression of each hub gene and tumor Weiss grade in the Giordano Adrenal 2 dataset. 0, no value (n=32); 1, low (n=13); and 2, high (n=20).
Oncomine analysis of the mRNA expression levels of (A)
GO and KEGG pathway enrichment analysis of DEGs in ACC samples.
A, Upregulated | |||
---|---|---|---|
ID | Description | Count | P-value |
GO:0051301 | Cell division | 8 | 5.60×10−7 |
GO:0000083 | Regulation of transcription involved in G1/S transition of mitotic cell cycle | 4 | 5.06×10−6 |
GO:0000082 | G1/S transition of mitotic cell cycle | 5 | 1.47×10−5 |
GO:0005634 | Nucleus | 23 | 1.26×10−7 |
GO:0005654 | Nucleoplasm | 17 | 4.07×10−7 |
GO:0072686 | Mitotic spindle | 4 | 3.33×10−5 |
GO:0019901 | Protein kinase binding | 5 | 2.83×10−3 |
GO:0035173 | Histone kinase activity | 2 | 6.38×10−3 |
GO:0005515 | Protein binding | 21 | 1.64×10−2 |
Hsa04115 | p53 signaling pathway | 3 | 3.19×10−3 |
Hsa04914 | Progesterone-mediated oocyte maturation | 3 | 3.19×10−3 |
Hsa04114 | Oocyte meiosis | 3 | 5.33×10−3 |
GO:0014068 | Positive regulation of phosphatidylinositol 3-kinase signaling | 6 | 4.30×10−4 |
GO:0006006 | Glucose metabolic process | 6 | 4.95×10−4 |
GO:0007165 | Signal transduction | 22 | 4.58×10−3 |
GO:0005615 | Extracellular space | 33 | 1.37×10−6 |
GO:0005578 | Proteinaceous extracellular matrix | 13 | 2.60×10−4 |
GO:0005576 | Extracellular region | 30 | 6.40×10−4 |
GO:0005509 | Calcium ion binding | 16 | 3.02×10−3 |
Hsa00350 | Tyrosine metabolism | 4 | 4.57×10−3 |
Hsa04510 | Focal adhesion | 7 | 1.44×10−2 |
Hsa04014 | Ras signaling pathway | 7 | 2.17×10−2 |
GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.
GO and KEGG pathway enrichment analysis of DEGs in the most significant module.
ID | Description | Count in gene set | P-value |
---|---|---|---|
GO:0005634 | Nucleus | 14 | 1.39×10−7 |
GO:0005829 | Cytosol | 12 | 3.86×10−7 |
GO:0005654 | Nucleoplasm | 11 | 1.25×10−6 |
GO:0019901 | Protein kinase binding | 5 | 1.48×10−4 |
GO:0035173 | Histone kinase activity | 2 | 3.08×10−3 |
GO:0005515 | Protein binding | 12 | 1.62×10−2 |
Hsa04115 | P53 signaling pathway | 3 | 1.36×10−3 |
Hsa04914 | Progesterone-mediated oocyte maturation | 3 | 2.28×10−3 |
Hsa04114 | Oocyte meiosis | 3 | 3.55×10−3 |
Hsa04110 | Cell cycle | 3 | 4.57×10−3 |
GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.