Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma
FANGSHI XU1,2, PENG ZHANG1, MIAO YUAN2 XIAOJIE YANG1 and TIE CHONG1
1Department of Urology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi 710000;
2Department of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi 710061, P.R. China
Received August 8, 2019; Accepted April 17, 2020
DOI: 10.3892/etm.2020.8987
Abstract. The molecular mechanisms of adrenocortical carcinoma (ACC) carcinogenesis and progression remain unclear. In the present study, three microarray datasets from the Gene Expression Omnibus database were screened, which identified a total of 96 differentially expressed genes (DEGs). A protein-protein interaction network (PPI) was established for these DEGs and module analysis was performed using STRING and Cytoscape. A total of eight hub genes were identified from the most significant module; namely, calponin 1 (CNN1), myosin light chain kinase (MYLK), cysteine and glycine rich protein 1 (CSRP1), myosin heavy chain 11 (MYH11), fibulin extracellular matrix protein 2 (EFEMP2), fibulin 1 (FBLN1), microfibril associated protein 4 (MFAP4) and fibulin 5 (FBLN5). The biological functions of these hub genes were analyzed using the DAVID online tool. Changes in the expression of hub genes did not affect overall survival; however, downregu- lated EFEMP2 decreased disease-free survival. CSRP1 and MFAP4 expression levels were associated with adverse clinicopathological features. In conclusion, although all eight hub genes were downregulated in ACC, they appeared to have important functions in ACC carcinogenesis and progression. Identification of these genes complements the genetic expression profile of ACC and provides insight for the diagnosis, treatment and prognosis of ACC.
Correspondence to: Professor Tie Chong, Department of Urology, The Second Affiliated Hospital of Xi’an Jiaotong University, 157 West Five Road, Xi’an, Shaanxi 710000, P.R. China E-mail: chongtie@126.com
Abbreviations: ACC, adrenocortical carcinoma; DEG, differentially expressed gene; PPI, protein-protein interaction; GO, Gene Ontology; MF, molecular function; BP biological process; CC, cellular component
Key words: ACC, DEG, downregulated gene, enrichment analysis, PPI network, hub gene, survival analysis
Introduction
Adrenocortical carcinoma (ACC) is a rare urological tumor with an annual incidence of 0.7-2/million (1). ACC is highly invasive and metastatic. Meanwhile, the prognosis of ACC is poor and most patients survive only 4-30 months. The 5-year overall survival rate is 16-47% and only 5-10% for advanced patients (2). In addition, diagnosis of ACC is difficult. Indeed, more than one-half of the patients display metastatic symptoms as the first clinical manifestation and many cases remain difficult to diagnose even after pathological diagnosis. Therefore, there is great interest in determining the molecular mechanisms of ACC onset and progression and in developing diagnostic and therapeutic strategies.
Microarray technologies and bioinformatics analysis have made high-throughput genome-wide sequencing and measure- ment of gene expression possible. Thus, key signaling pathways can be elucidated comprehensively and systematically, thereby revealing the molecular mechanisms of disease development and progression. In the present study, three mRNA microarray datasets from the Gene Expression Omnibus (GEO) database were screened for obtaining differentially expressed genes (DEGs) and ACC hub genes were chosen from the most significant module. Subsequently, a protein-protein interaction (PPI) network was established and gene enrichment, survival, co-expression and cluster analysis were performed for the hub genes. These analyses may help clarify the mechanisms of carcinogenesis and progression of ACC and identify new targets for treatment.
Materials and methods
Research process. In the present study, three microarray datasets from the GEO database (www.ncbi.nlm.nih.gov/geo/) were screened according to specific inclusion and exclusion criteria. A total of 96 DEGs were chosen to analyze. The PPI network of DEGs was constructed and corresponding enrich- ment analysis was performed. From these analyses, hub genes were identified from the most significant module (degree cutoff=2; node score cutoff=0.2; K-core=2) in the PPI network. Subsequently, enrichment, survival and cluster analysis were performed on these hub genes. Finally, the Oncomine online database (www.oncomine.org/) was used to further verify the differential expression of hub genes between ACC and normal
tissue, and to analyze the relationships between clinical pheno- types and gene expression. Fig. 1 summarizes this research process.
Dataset screening. Relevant datasets were obtained from the GEO database using the key words ‘Adrenal cortical carcinoma’ OR ‘Adrenocortical carcinoma’ OR ‘Adrenal carcinoma’. The research type was set to ‘Expression profiling by array’ and the organism was selected as ‘Homo sapiens’. In total, 28 relevant datasets were initially identified. Datasets GSE19750 (3), GSE12368 (4) and GSE14922 (5) were ulti- mately selected according to the following inclusion criteria: i) Achievable comparison of ACC with normal adrenal tissue; and ii) original data can be downloaded in CEL format. In addition, the following exclusion criteria were applied: i) Childhood ACC; and ii) use of molecular targeted drugs for ACC before surgical treatment.
DEG identification. Using GEO 2R online analysis software (www.ncbi.nlm.nih.gov/geo/geo2r/), each dataset was divided into ACC group and normal tissue group. The TOP250 option was then used to obtain a genomic profile of DEGs between the tumor and normal groups in each dataset. A P-value <0.01 and LogFC absolute value ≥0.5 were used as initial screening conditions, where FC indicates fold change. DEGs which are shared between datasets are presented in Venn diagrams.
KEGG and GO enrichment analyses. DEGs were subjected to gene enrichment analysis to obtain the main biological functions and signaling pathways in which they were involved. The Gene Ontology (GO) Consortium (geneontology.org/) is a database of new semantics vocabulary standards that are appli- cable to various species that can define and describe gene and protein functions (6). GO genetic annotations fall into three broad categories: i) Molecular function (MF); ii) biological process (BP); and iii) cellular component (CC). Gene function was defined and described according to these categories.
Kyoto Encyclopedia of Genes and Genomes (www.kegg. jp; version 94.0; KEGG) is a comprehensive database that integrates information on genomic, chemical and system functions (7). Using the KEGG database, information on the signaling pathways of genes can be obtained to deeply exca- vate the molecular mechanisms of the genes.
Database for Annotation, Visualization and Integrated Discovery (DAVID) is an online bioinformatics analysis and integration tool (david.ncifcrf.gov) for Functional Annotation, Gene Functional Classification, Gene ID Conversion and other analyses (8). DAVID (version 6.8) was used to complete the GO and KEGG enrichment analyses of DEGs and hub genes to obtain information on their molecular functions, biological processes, cytogenetics and signaling pathways.
PPI network construction and module analysis. Functional links among proteins often reflect the genetic association among their genes. A PPI network can be used to describe the interactions among proteins and identify hub regulatory genes of disease. The STRING database (version 11.0; string-db.org) can search for interactions between known and predicted proteins, which can be used to analyze and establish the PPI network of DEGs (9). Cytoscape (version 3.4.0; Cytoscape
User Support, Education and New Initiatives are supported by the National Resource for Network Biology; award no. P41 GM103504) is an open source bioinformatics software platform for visualizing molecular interaction networks (10). The Cytoscape plugin MCODE is an application for cluster analysis (11). With Cytoscape, a visualization of the molecular functions of DEGs can be obtained. Using the clustering analytic function of MCODE, the most significant module in a PPI network of DEGs was obtained; the hub genes were derived from this module.
Hub gene selection and analysis. After obtaining the most significant module in the PPI network of DEGs, genes with a score ≥3 were selected as hub genes. PubMed Gene was employed to perform functional description of the hub genes (www.ncbi.nlm.nih.gov/gene/). The cBioPortal (www. cbioportal.org) platform was used to establish a network rela- tionship between the hub genes and their co-expressed genes. The Cytoscape plugin BINGO was used to visualize the BP of hub genes (12). The University of California Santa Cruz (UCSC) Cancer Genomics Browser (https://genome-cancer. ucsc.edu/) is a genomic database containing >22,700 shares of sample information (13). Users can explore the relationships between genomic changes and clinical phenotypes using visu- alized clinical data and phenotypic characteristics, such as age, tissue grade and pathology subtypes. Hierarchical clustering analysis of hub genes by the USCS Cancer Genomics Browser can identify the differential expression of hub genes between tumors and normal samples. The analysis can evaluate whether hub genes could be used as diagnostic markers.
To assess the potential function of hub genes in clinical progression of ACC, the prognostic analysis and clinical correlation analysis were performed. Overall survival rate and disease-free survival rate in ACC were analyzed using cBioPortal. Oncomine (www.oncomine.org) was used to further verify whether the expression of hub genes between ACC and normal tissues was significant different (P<0.05) and to evaluate the relationships between expression of hub genes and clinical phenotypes, including capsular inva- sion, grade and vascular invasion. The clinical correlation analysis is based on the Kolmogorov-Smirnov test. During the verification of Oncomine database, we set the following parameters: i) Analysis type, cancer vs. normal analysis; ii) cancer type, adrenal cortex carcinoma; and iii) data type, mRNA.
Results
Identification of DEGs in ACC. In the present study, ‘Adrenocortical carcinoma’, ‘Adrenal cortical carcinoma’ and ‘Adrenal carcinoma’ were used as the search terms for the GEO database. Initially, 815 studies were obtained. Subsequently, 29 studies were obtained through study type filter (set as expression profiling by array), of which only nine were of human tissue origin and the rest were animal or cytological experiments. In the residual nine studies, GSE90713 involved metastatic ACC samples and GSE73417 involved a neoplastic transplant model. GSE19776, GSE19775, GSE28476 and GSE15918 did not include compared normal tissues. Thus, only three datasets were ultimately selected.
Select 3 GEO datasets according to certain search settings, inclusion and exclusion criteria
96 DEGs were selected among three mRNA expression profiling sets
1. Construct the PPI network of DEGs using Cytoscape
2. GO and KEGG pathway enrichment analysis of DEGs using DAVID
The most significant module was obtained from PPI network. The hub genes were selected with degree ≥3
1. Co-expression analysis using cBioportal
2. GO and KEGG pathway enrichment analysis of Hub genes
3. Hierarchical clustering analysis of hub genes using UCSC Cancer Genomics Browser
CSRP1 / MFAP4 were selected for the highest degree
4. Survival analysis of hub genes using cBioportal
Verify the differential expression of CSRP1 and MFAP4 in different studies using Oncomine
To analyze the association between the clinical phenotype (capsular invasion, grade, vascular invasion) of ACC and the expression levels of CSRP1 and MFAP4.
Finally, three datasets from the GEO database were selected according to the aforementioned criteria: i) GSE19750 (44 ACC; 4 normal); ii) GSE12368 (28 ACC; 6 normal); and iii) GSE14922 (4 ACC; 4 normal; 4 non-functioning adenomas; 4 secretory type). DEGs were identified in each dataset. In total, 1,464, 764 and 1,088 DEGs were identified in GSE19750, GSE12368 and GSE14922, respectively. As a result, 96 DEGs were shared across all three datasets (Fig. 2A).
GO and KEGG enrichment analysis of DEGs. DAVID ver. 6.8 was used to perform GO and KEGG enrichment analyses for all identified DEGs. The pathways with P<0.05 and the highest enrichment, based on the number of enriched genes, are presented in Table I. The CCs associated with the DEGs in the present study were mainly extracellular structures, including ‘extracellular exosome’, ‘extracellular region’ and ‘extracellular space’. The MFs of these DEGs were
predominantly associated with functional binding, including ‘actin binding’ and ‘integrin binding’. Moreover, DEGs were found to be related with some tumor biological process, such as ‘cell adhesion’, ‘muscle contraction’ and ‘negative regulation of inflammatory response’. According to the KEGG signaling pathway analysis, DEGs were significantly enriched in ‘drug metabolism-cytochrome P450’ and the ‘pertussis’ pathways.
PPI network and module analysis. Cytoscape (version 3.4.0) was used to construct a PPI network of DEGs (Fig. 2B). MCODE was used to extract the most significant module from the PPI network (Fig. 2C). The MCODE parameters were the following: i) Degree cut-off=2; ii) node score cut-off=0.2; iii) max depth=100; and iv) k-score=2 (11). The most promi- nent module had 8 nodes and 14 edges. DAVID was used to perform GO and KEGG enrichment analyses of the module (Table II). The genes in this most prominent module were not
A
GSE19750
GSE12368
B
NTRK3
ESM1
1464
224
764
CIR
NGFR
SERPING
PTPRB
96
TSPAN4
TEK
HGF
250
104
PCSK2
CXCL12
GPM6A
TUB
1088
STEAP4
GAS7
TLR4
PTGDS
DUOX1
PCDH9
GSE14922
SYT4
$1008
C
DPT
PRELP
PTH1R
CBLN4
MFAP4
PAPLN
DES
RXFP1
FBLN5
MFAP4
EFS
MYH11
FBLN1
TCF21
FBLN5
TMPO
EFEMP2
EFEMP2
PCDH10
FMO3
CSRP1
MYLK
SCUBE3
FBLN1
TPX2
NAV2
CNN1
MRGPRF
MYH11
CYP4B1
CNN1
MYLK
SORBS1
ANKSTA
KCNQ2
FANCI
CSRP1
SH3D19
significantly enriched in KEGG pathway analysis (P>0.05). In the GO analysis, the module was mainly enriched in some extracellular functions and structures, such as the ‘extracel- lular exosome’, ‘extracellular region’, ‘elastic fiber’ and ‘elastic fiber assembly’.
Hub gene selection and analysis. The DEGs were selected as hub genes if their cluster degrees were ≥3.0 in the MCODE analysis. A total of eight hub genes were identified, all of which were contained in the most significant module. PubMed Gene was used to obtain the corresponding gene names, abbreviations and functions (Table III). The cBio- portal online tool was used to construct a co-expressed gene network of the hub genes (Fig. 3) and the BP visualization network of the hub genes was completed via BiNGO (Fig. 4). Using UCSC for hierarchical clustering analysis, the hub genes displayed low expression in tumor tissues, compared with normal tissues (Fig. 5).
Changes in the expression of all hub genes did not affect overall survival rate (Fig. 6). However, alteration of EGF containing fibulin extracellular matrix protein 2 (EFEMP2) led to a decline in disease-free survival rate.
The hub genes, cysteine and glycine rich protein 1 (CSRP1) and microfibril associated protein 4 (MFAP4), showed the highest node degree of 5, suggesting that these genes may have important functions in ACC carcinogenesis and progression. Subsequently, further verification was carried out via the Oncomine database. CSRP1 and MFAP4 were significantly downregulated in different studies (14,15) (Fig. 7). Among these studies (14,15), only Giordano et al’s
study (14) was provided with sufficient information of clinicopathological features (including capsular invasion, histological grade and vascular invasion), hence, which was used to perform clinical correlation analysis. The results revealed that lower mRNA levels of CSRP1 and MFAP4 were associated with adverse capsular invasion, grade and vascular invasion (Fig. 8).
Discussion
The incidence of ACC is low; however, due to its high poten- tial for malignancy and metastasis, the 5-year survival rate of patients is only 16-47% (16). In addition, ACC is difficult to diagnose, even with imaging, hormone tests and postoperative diagnostic methods. Therefore, understanding the mecha- nisms of carcinogenesis and progression in ACC is of great importance to search for potential diagnostic markers and therapeutic targets.
Microarray technology has enabled assessment of genetic expression changes in ACC, which has provided insight into the molecular mechanism of this disease and has already been used extensively in cancer research. In the present study, three ACC mRNA matrix datasets from the GEO database were screened, allowing the identification of 96 DEGs. Enrichment analysis indicated that these DEGs were associated with some tumor-related BPs, such as ‘cell adhesion’ and ‘nega- tive regulation of inflammatory response’ and may regulate ACC tumorigenesis and progression through the binding to other functional proteins (calcium-binding proteins, actin and integrin). In addition, the major functional region of these
| A, Cellular component | |||
|---|---|---|---|
| Term | Description | Count in gene set | P-value |
| GO:0070062 | Extracellular exosome | 26 | 0.0019 |
| GO:0005576 | Extracellular region | 20 | 0.0003 |
| GO:0005615 | Extracellular space | 17 | 0.0008 |
| B, Molecular function | |||
| Term | Description | Count in gene set | P-value |
| GO:0005509 | Calcium ion binding | 12 | 0.0007 |
| GO:0003779 | Actin binding | 6 | 0.010 |
| GO:0005178 | Integrin binding | 4 | 0.0150 |
| C, Biological process | |||
| Term | Description | Count in gene set | P-value |
| GO:0007155 | Cell adhesion | 8 | 0.0092 |
| GO:0006936 | Muscle contraction | 5 | 0.0022 |
| GO:0050728 | Negative regulation of inflammatory | 4 | 0.0078 |
| response | |||
| D, KEGG pathway | |||
| Term | Description | Count in gene set | P-value |
| Hsa00982 | Drug metabolism-cytochrome P450 | 3 | 0.0395 |
| Hsa05133 | Pertussis | 3 | 0.0471 |
| GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes. | |||
regulatory processes appeared to be extracellular locations. Cell adhesion is mediated by adhesion molecules, including members of the immunoglobulin superfamily and the integrin family. Integrins have crucial activities in regulating immune cell function, including transport of immune cells into tissues, activation of effector cells and formation of immune synapses between immune cells and tumor cells (14). Therefore, research on integrins is an active field in basic oncology. The present study identified that DEGs were significantly enriched in integrin regulation, suggesting that the selected DEGs play a crucial role in ACC carcinogenesis and progression.
The inflammatory response is also closely related to tumor progression. Although the immune system can recognize and kill tumor cells, the inflammatory response induced by immu- nization can also promote the proliferation of tumor cells and inhibit the anticancer response (17). Thus, inflammatory processes, as well as major metabolites involved in inflam- mation, including adiponectin and high-density lipoprotein, are strongly associated with the risk and invasiveness of solid tumors (18-20). The GO enrichment analysis also demonstrated
that DEGs are involved in ‘negative regulation of inflamma- tory response’ (Table I). However, the concrete inflammatory regulatory mechanisms of DEGs relied on further research. In summary, the enrichment analysis results of the current study are consistent with previous oncology research (12,21).
Using MCODE, the most significant module in the PPI network was obtained and eight hub genes with degree ≥3 were identified. The hub genes identified in the present study were all downregulated in ACC, compared with normal tissue. This result was not in agreement with a previous study by Xiao et al (22). Several reasons might account for this differ- ence. A possibility is the use of different study groups. From six datasets in the GEO database, Xiao et al (22) considered DNA topoisomerase II a (TOP2A), NDC80 kinetochore complex component, centrosomal protein 55, cyclin-dependent kinase inhibitor 3 and cyclin-dependent kinase 1, as five key genes that affect the progression and prognosis of ACC. In their analysis, the specimens of GSE33371 were from breast cancer and the specimens of GSE75415 were from adrenal cortical tumors of children. The dataset selection in the current study,
Table II. GO enrichment analysis of differentially expressed genes in the most significant module.
A, Cellular component
| Term | Description | Count in gene set | P-value |
|---|---|---|---|
| GO:0070062 | Extracellular exosome | 7 | 8.15x10-5 |
| GO:0005576 | Extracellular region | 4 | 0.0183 |
| GO:0071953 | Elastic fiber | 3 | 7.59x10-7 |
B, Molecular function
| Term | Description | Count in gene set | P-value |
|---|---|---|---|
| GO:0005516 | Calmodulin binding | 3 | 0.0025 |
| GO:0005509 | Calcium ion binding | 3 | 0.0328 |
| GO:0005201 | Extracellular matrix structural constituent | 3 | 0.0274 |
C, Biological process
| Term | Description | Count in gene set | P-value |
|---|---|---|---|
| GO:0048251 | Elastic fiber assembly | 3 | 2.23x10-6 |
| GO:0006939 | Smooth muscle contraction | 2 | 0.0064 |
| GO:0006936 | Muscle contraction | 2 | 0.0376 |
GO, Gene Ontology.
| Gene | Full name | Function |
|---|---|---|
| CNN1 | Calponin 1 | Cell proliferation, anchorage-independent colony formation, cell motility and invasion. |
| CSRP1 | Cysteine and glycine rich protein 1 | A growth factor, cell proliferation, somatic differentiation. |
| MYLK | Myosin light chain kinase | Catalyze the phosphorylation of myosin light chains (MLC), cell inva- sion and metastasis. |
| MYH11 | Myosin heavy chain 11 | Hydrolysis of ATP, cell migration and adhesion, intracellular transport, signal transduction. |
| EFEMP2 | EGF containing fibulin extracellular matrix protein 2 | Blood coagulation, activation of complement, determination of cell fate during development. |
| FBLN1 | Fibulin 1 | Cell adhesion, migration, differentiation. |
| MFAP4 FBLN5 | Microfibril associated protein 4 Fibulin 5 | Cell adhesion, intercellular interactions. |
| Angiogenesis, epithelial cell motility, the activity of matrix metallopro- tease 9 (MMP-9). |
however, involved a different study group. Alternatively, differences in preliminary screening of the mRNA expres- sion datasets could also explain the discrepancies between the two studies. Unlike the previous study by Xiao et al (22), a preliminary screening of the mRNA datasets was conducted before obtaining the DEGs. The criteria were LogFC ≥0.5 and P<0.01, to ensure that the genes entering the analysis reached the pre-set threshold for statistical significance. Thus, differ-
ences in screening conditions and analysis likely explain the different results between previous research and the present study.
Compared with upregulated hub genes identified in previous studies, the present results suggested that down- regulated hub genes are also important in carcinogenesis. The following descriptions of the ACC-associated downregulated genes speculated how they may contribute to ACC onset.
FBLN5
FBLN2
MYLK3
RHOB
MFAPS
ITPR2
MFAR
MCOLN1
MYH11
MFAP4
HTR3C
SRI
ACTRIA
FB
N1
SFEMP2
RFC6
RYRA
TRPV5
TAPP
RPM3
CSNK2A1
TRPZ3
MSOLN2
MAPK3
MYLK
ATP2B
GSK3B
AURKB
ABL1
In ovarian cancer, calponin 1 (CNN1) is an important tumor suppressor gene (23). Low expression of CNN1 in peritumoral vessels is negatively related to the expression of vascular endothelial growth factor, which is involved in the generation of tumor blood vessels (24). In addition, CNN1 was associated with the progression and prognosis of bladder cancer in a previous study (25).
CSRP1 and MFAP4 are expressed at low levels in some tumors, yet this was shown to have different consequences in different tumor types or stages. CSRP1 was hypothesized to be a tumor suppressor gene in colorectal cancer (26). In addition, CSRP1 may be inactivated due to abnormal meth- ylation and may be an important diagnostic marker for liver cancer (27). However, celecoxib may exhibit anti-gastric cancer effects by suppressing expression of CSRP1 (28). In the present study, CSRP1 was downregulated in ACC, which indicated that CSRP1 may be a tumor suppressor gene. MFAP4 is a tumor suppressor gene in prostate cancer and it displays low expression in breast cancer (29,30). By contrast, downregulated MFAP4 may lead to adverse clinical incidents in ovarian cancer (31). This contradiction may be explained by the fact that, in early stage cancer, MFAP4 facilitates inflam- matory cell recruitment and assists immunological cancer surveillance to restrain cancer cell survival (32). However, in advance stage, alteration of the tumor microenvironment results in decreased immune function of lymphocytes and MFAP4 predominantly promoted cancer cell proliferation and migration (33). Similarly, it’s putative that low expression of MFAP4 ineffectively activate immune and inflammatory cells to suppress malignant progression of ACC.
Fibulin (FBLN) 1 and -5 belong to the FBLN protein family, which is involved in maintaining the stability of the basal membrane, elastic fibers and loose connective tissue. Schluterman et al (34) demonstrated that loss of fibulin 5
(FBLN5) expression promoted tumor progression by increasing the level of reactive oxygen species. In most human carcinomas, especially in kidney, breast, ovarian, colon and malignant metastatic carcinoma, FBLN5 was downregulated compared with normal tissues (35). In addition, FBLN5 is also a target for transforming growth factor-ß in endothelial cells, suggesting that FBLN5 may be a therapeutic target (36).
The myosin heavy chain 11 (MYH11) gene encodes the smooth muscle myosin heavy chain and mutations in MYH11 were mainly associated with aortic aneurysm and acute myeloid leukemia (37,38). Carcinoma metastasis and invasion are driven by cell movement, a process involving myosin/actin contraction and cell contact point degradation (39). Mutation and downregulation of MYH11 were associated with colon cancer and mucosal polyp syndrome (40). MYH11 was also downregulated in breast and bladder carcinoma (41).
EFEMP2 is an extracellular matrix protein necessary for elastic fiber formation and connective tissue development, processes that are highly associated with tumor invasion and metastasis (42). The expression of EFEMP2 in bladder cancer tissues was significantly lower than that in normal tissues in previous study (43). Zhou et al (43) confirmed that low expression of EFEMP2 could reduce the expression of epithelial marker E-cadherin, as well as increase the expres- sive levels of mesenchymal markers N-cadherin, vimentin, Snail and Slug and key factors of the Wnt/ß-catenin signaling pathway (B-catenin, c-Myc and cyclin D1). Their observations demonstrated that EFEMP2 inhibited tumor progression and metastasis in bladder cancer (43). However, to the best of our knowledge, studies on EFEMP2 in ACC have not yet been performed; thus, the findings of the present study may provide insight for adrenocortical tumorigenesis.
Myosin light chain kinase (MYLK) regulates myosin activity through phosphorylation and dephosphorylation of
striated muscle tissue development
muscle tissue development
muscle organ development
heart development
cardiac muscle tissue development
organ development
tissue development
system development
muscle structure development
cardiac muscle fiber development
anatomical structure development
cell development
multicellular organismal development
muscle cell differentiation
system process
anatomical structure) morphogenesis
muscle fiber development
muscle system process
developmental process
striated musclé cell differentiation
multicellular organismal process
cell differentiation
anatomical structure formation involved in morphogenesis
muscle cell development
muscle contraction
biological_procesalar component morphogenesis
striated muscle cell development
extracellular structure organization
cellular developmental process
cettular process
cellular component organization
cellular component assembly involved in morphogenesis
extracellular matrix organization
myonoril assembly
cellular component biogenesis
actin filament-based process
cellular component assembly
organelle organization
skeletal myofibni assembly
extracellular matrix assembly
cellular macromolecular complexactin cytoskeleton organization
macromolecular complex subunit organization
subunit organization
skeletal muscle myosin thick filament assembly
actomyosin structure organization
elastic fiber assembly
protein complex biogenesis
cytoskeleton organization
macromolecular complex assembly
myosin filament assembly or disassembly
cellular macromolecular complex assembly
striated muscle myosin thick filament assembly
protein complex assembly
myosin filament assembly
cellular protein complex assembly
the myosin light chain. Therefore, it is involved in many physiological processes, such as cell adhesion, cell prolif- eration, cell migration and infiltration (44). MYLK can increase the expression of epidermal growth factor receptor
and activate the ERK/JNK signal pathway, which can ablate the adhesion between cells and increase the aggressiveness of breast cancer cells (45). In addition, MYLK expression is low in prostate cancer, bladder cancer, non-small cell carci-
Normal Tissue
Primary Turmor
CNN1
MYH11
MFAP4
CSRP1
EFEMP2
FBLN1
MYLK
FBLN5
Sample type
A
Cases with alteration(s) in query gene(s)
Cases without alteration(s) in query gene(s)
100%
100%
-
90%
90%
CNN1
-
CSRP1
80%
EFEMP2
80%
FBLN1
Overall survival
70%
79%
60%
50%
50%
40%
47%
40%
Log rank Test P-value: 0.491
30%
30%
Log rank Test P-value: 0.461
Log rank Test P-value: 0.696
3
20%
20%
20%
Log rank Test P-value: 0.231
12%
10%
12%
-
0
-
0
10
20
140 150 180
40 50 60 70 80 00 100 110 120 130 3
0
160
0%
Months survival
Months survival
10
20
KC
40
60
M
&
9
20 130 54C
0
NO
20
30
AC
SO
60 70 80 0
100
0 140 150 18
Months survival
110 120 130
Months survival
100%
100%
100%
90%
90%
FBLN5
MFAP4
MYLK
-
80%
80%
MYH11
Overall survival
70%
70%
70%
70%
80%
-
60%
50%
50%
-
-
40%
40%
3
Log rank Test P-value: 0.515
-
Log rank Test P-value: 0.760
Log rank Test P-value: 0.167
30%
Log rank Test P-value: 0.844
20%
20%
20%
20%
10%
10%
OS
0
no
10
K
0
9
50
1
fax
0
9
10
20
30
AO
0
0
80
100
150
0%
a
10
20
30
40
60
70 80 00 100 110 120 130 140 150 160
Months survival
Months survival
Months survival
Months survival
B
100%
Disease/progression-free survival
100%
CNN1
2
100%
100%
90%
CSRP1
EFEP2
1
FBLN1
00%
80%
70%
-
60%
-
60%
50%
50%
-
Log rank Test P-value: 0.548
-
Log rank Test P-value: 0.431
40%
30%
30%
-
Log rank Test P-value: 0.0298
-
Log rank Test P-value: 0.315
20%
20%
20%
10%
10%
10%
6
5
35
5
5
Months disease/progression free
20
»
10
20 30 40 50 60 70 80 90 NOO THỌ 120 130 ĐÀO TẠO ĐẢO Months disease/progression free
0
10
20
30
40
50
60
10
1 00 100 110 120 130 140
160 960
a
Months disease/progression free
10 20 30 40 50 60 70 40 00 100 110 120 NÀO SẢO 150 NÃO Months disease/progression free
100%
100%
100%
100%
90%
FBLN5
MFAP4
MYH11
90%
MYLK
80%
80%
-
70%
70%
70%
70%
60%
60%
4%
60%
50%
.
50%
50%
·
50%
40%
40%
-
40%
30%
Log rank Test P-value: 0.538
30%
Log rank Test P-value: 0.500
Log rank Test P-value: 0.0858
Log rank Test P-value: 0.255
3
20%
20%
20%
20%
10%
10%
10%
10%
0
10 20 30 40 50 60 70 50 0 00 10 120 130 140 150 MẮT Months disease/progression free
0%
10
O
10
20
30
8
10
20
Months disease/progression free
0 40 50 60 70 80 50 100 10 120 150 140 150 160
0%
Months disease/progression free
Months disease/progression free
noma and gastric cancer, which indicates this gene may greatly impact on carcinogenesis and malignant progres- sion (46,47).
ACC is difficult to diagnose, even with postoperative pathological analysis. Previous studies and published guide- lines (48-50) indicated that histopathological features alone
| Median Rank | p-Value | Gene | ||
|---|---|---|---|---|
| A | 261.0 | 0.004 | CSRP1 | |
| 1 2 |
Legend
1. Adrenal Cortex Carcinoma vs. Normal Giordano Adrenal, Am J Pathol, 2003
2. Adrenal Cortex Carcinoma vs. Normal Giordano Adrenal 2, Clin Cancer Res, 2009
1
5
10
25
25
10 5
1
☒
Not measured
%
-
| Median Rank | p-Value | Gene | |
|---|---|---|---|
| B 186.5 | 0.002 | MFAP4 | |
| 1 2 |
A
Capsular invasion
Grade
Vascular invasion
4.5
4.5
4.5
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4.0
1.5
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10
10
10
2.$
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$
0.5
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.
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3
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3
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Legend
Legend
Legend 1. HA (10)
3. Le= (1)
3. Ves (54
2. Hiện Hội
2. No 14)
B
Capsular invasion
Grade
Vascular invasion
10
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2
20
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log2 medan-centered irmanuty
00
00
B
o
3-4.5
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5
6.5
-2.5
70
-8.5
.8.0
-8.5
1
4
3
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2
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2
3
Legend
Legend 1. NA IN)
Legend 1. HÀ (10)
3. VI (5)
Figure 8. Association between the expression level of CSRP1 and MFAP4 and capsular invasion, grade and vascular invasion in the Giordano et al (14) adrenal dataset. (A) CSRP1 mRNA expression in ACC, compared with normal adrenal tissues. (B) MFAP4 mRNA expression in ACC samples. CSRP1, cysteine and glycine rich protein 1; MFPA4, microfibril associated protein 4; ACC, adrenocortical carcinoma.
cannot predict malignant or metastatic occurrence and that regular and long-term follow-up is necessary for most cases. Thus, there is a clear need to find rapid and accurate tools for diagnosis of adrenocortical cancer.
Certain previous studies have revealed that Ki-67 and minichromosome maintenance protein are reliable indi- cators of benign and malignant adrenal tumors (51,52). Additionally, various studies have used pituitary-tumor transforming gene 1 (53), telomerase activity (54) and vascular endothelial growth factor (55) as diagnostic markers of ACC. However, studies of these ACC diagnostic markers have not reached a uniform and reliable conclusion. Nowadays, gene expression analysis has been used to screen molecular markers for cancer diagnosis and prognosis. Microarray technology may become the method of choice for the detection of malignant adrenal tissue. In the present study, bioinformatics analysis was used to identify eight key downregulated genes. It was verified that these genes were associated with ACC in terms of molecular function, biological processes and cytology. Moreover, using cluster analysis in the USCS Cancer Genomics Browser, it was demonstrated that the selected hub genes can distinguish normal adrenal tissues from ACC tissues. However, there were many samples that did not display expression of the hub genes, which suggested that these hub genes are not
differentially expressed in all ACC tissues. This condition will impose some limitations on diagnosis.
Analysis of the relationships between gene expression levels and clinical phenotypes is another important issue in oncology. Using Kaplan-Meier analysis, the association between overall survival rate, disease-free survival rate and the downregulated hub genes was assessed. This analysis showed that alterations of all hub genes did not affect overall survival rate. However, downregulated EFEMP2 resulted in a decrease on disease-free survival rate. The lack of effects on survival may be due to several reasons. Firstly, survival analyses in cBioPortal database were performed on the basis of the relationship between gene mutation and prognosis, whereas genetic low expression may result from promoter methylation, histone modification or protein acetylation, not just mutation. Thus, low expression of the eight hub genes in ACC may possessed low frequency of mutation, which led the prognostic difference insignificant. Other previous studies demonstrated a similar lack of conformity. For example, in bioinformatics analysis conducted by Li et al (56), the TOP2A oncogene did not affect overall and disease-free survival rates. However, some previous clinical studies demonstrated that TOP2A was significantly related to the survival rate of patients with hepatocellular carcinoma (57,58). Secondly, carcinogen- esis and progression of tumors are the result of multi-gene
dysregulation and different genes can have various effects on tumor prognosis. Although the low-expression genes identified in ACC in the present study are involved in multiple key steps of tumorigenesis and progression, their effects on prognosis may be less than the effects of high-expression genes identi- fied in previous studies (22,59). Compared with other urologic neoplasms, ACC has a low incidence (0.7-2/million), which may lead the insufficiency of datasets and samples. Small sample size may skew the results of prognostic analysis (60).
Capsular invasion, histological grade and vascular invasion are common yet informative clinicopathological parameters. These indicators can reflect the tendency of tumor progres- sion and reveal the differentiated degree of neoplasms. Thus, several previous studies have analyzed the relationships between gene expression and these clinical parameters in different cancer, such as hepatocellular carcinoma, thyroid carcinoma and pheochromocytoma (61-63). Therefore, these clinicopathological features are commonly used in cancer research. In the present study, the expression levels of CSRP1 and MFAP4 were associated with capsular invasion, grade and vascular invasion, which suggested that CSRP1 and MFAP4 may promote progression of ACC.
The present study also has some limitations. First, the clinical data of ACC are insufficient. Due to the low incidence of ACC, there were few qualified datasets for bioinformatics analysis. Moreover, subtypes of ACC were not considered in the bioinformatics analysis. The main subtype of ACC is adrenal epithelial cell carcinoma, which accounts for >95% of all subtypes. Other rare subtypes include oncocytic adrenal neoplasms, myxoid adrenal cortical carcinoma and adrenal carcinosarcoma. Different subtypes may have different mechanisms of carcinogenesis and progression, but there is a lack of data and relevant research to verify this possibility.
In conclusion, the hub genes screened in the present study were downregulated and these genes were associated with ACC carcinogenesis and progression. Identification of these hub genes improves the gene expression profile of ACC and provides important molecular biological insight for the diag- nosis, treatment and prognosis of ACC. Nevertheless, further studies are needed to elucidate how the biological functions of these genes contribute to ACC.
Acknowledgements
Not applicable.
Funding
No funding was received.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors’ contributions
TC designed the study and revised the manuscript. FX performed the Gene Expression Omnibus database analysis,
analyzed the data. FX and PZ performed bioinformatics analyses and assisted with analysis of other data. FX, MY and XY wrote the manuscript, collected data, performed revision of the manuscript and created the figures. All authors have read and approved the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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