Abstract
Adrenocortical carcinoma (ACC) is an end‐stage hormonal syndrome. Although profound attempts have been made to illuminate the pathogenesis, the molecular mechanisms of ACC remain to be clarified. To identify the important genes in the progression of ACC, microarray datasets GSE19775 was downloaded from the gene expression omnibus database. The differentially-expressed genes (DEGs) were identified, and pathway and GO enrichment analyses were performed. The protein–protein interaction (PPI) network was constructed and the module analysis was performed using the protein interaction network analysis and Cytoscape. Also constructed target genes–miRNA regulatory network and target genes–TF regulatory network. Correlation of the hub genes were analyzed in The Cancer Genome Atlas. The prognostic values of hub genes were further validated by online tool UALCAN. Mutation analysis was done by online tool CBio Cancer Genomics Portal. A total of 884 DEGs were identified, with 441 in up regulation and 443 in down regulation. Pathways in catecholamine biosynthesis, aldosterone synthesis and secretion, pyrimidine deoxyribonucleosides salvage and systemic lupus erythematosus were the most significantly enriched for DEGs (up and down regulated). Blood vessel morphogenesis and cell cycle phase transition were the most significantly enriched term in biological processes, while extracellular matrix and chromosome, centromeric region were in cellular component and heparin binding and protein dimerization activity were in molecular function. Among the PPI networks and its module, target genes–miRNA regulatory network and target genes–TF regulatory network, hub genes were YWHAZ, FN1, GRK5, VCAM1, GATA6, TXNIP, HSPA1A, and F11R. Hub genes such as YWHAZ, STAT1, ICAM1, SH3BP5, CD83, FN1, TK1, HIST1H1C, CABLES1, and MCM3 were associated with poor overall survival, while hub genes such as STAT1, ICAM1, CD83, FN1, TK1, HIST1H1C, and MCM3 were highly expressed in stage 4. In conclusion, DEGs and hub genes diagnosed in this study may deepen our understanding of molecular mechanisms underlying the progression of ACC, and provide important targets for diagnosis and treatment of ACC.
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Introduction
Adrenocortical carcinoma (ACC) is rare tumor cell form in the outer layer of the adrenal gland. 0.02–0.2% ACC related deaths were reported each year in the world [1]. 5-year survival rate of ACC patients was 16–47% [2]. Due to the inadequate diagnostic methods and tools, ACC is often diagnosed at an advanced stage. Despite the key improvements in surgical treatment, the 5-year survival rate for ACC patients diagnosed with metastatic is still low at approximately 33% [3]. Thus, it’s urgently needed for compassionate the molecular mechanisms of ACC progression and recognition of novel biomarkers and pathways are used for the early detection and prognosis assessment of ACC.
Over the past decades, advance of molecular biology have increased our compassionate of the pathogenesis of ACC. Earlier researches have indicated that ACC is a genetic disease, which depends on modification of several of oncogenes and tumor suppressor genes [4]. A growing number of genes and their coding proteins related to ACC have been examined. Earlier studies reported that they play key roles in a large number of physiological and pathological processes including cell proliferation [5], differentiation [6], apoptosis [7], and metastasis [8]. However, the precise molecular mechanisms of ACC are still far from being deep understood. Newly, several studies have identified a group of ACC related candidate biomarkers and pathways by microarray analysis [9]. Genes such as PDL1 [10], IGF and IGFBP [11], TARBP2 [12], IGFII and MIB1 [13], and TOP2A [14] were responsible for pathogenesis of ACC. Pathways such as Wnt/beta-catenin pathway [15], TGF-β pathway [16], and mTOR pathway [17] were important for progression of ACC. However, few studies have assessed the key genes associated in ACC using a bioinformatics approach. Accordingly, it was crucial to explore the molecular mechanisms in ACC and to detect novel and specific biomarkers, pathways, and targets.
In this study, we have downloaded GSE19775 from gene expression omnibus (GEO) database and utilized limma R bioconductor tool to identify the DEGs between ACC and normal whole adrenal gland. Additionally, other approaches including pathway enrichment analysis from different pathway databases, Gene Ontology (GO) enrichment analysis, protein–protein interaction (PPI) network construction, sub-modules analysis, target genes–miRNA, interaction construction, target genes–TF interaction construction, survival analysis, and validation of hub genes. The aims of our study were to identify novel biomarkers, pathways, and potential therapeutic targets for ACC.
Materials and methods
Data collection
The gene expression profile (GSE19775) included 15 ACC samples and 15 normal whole adrenal gland samples based on platform of GPL887Agilent-012097 Human 1A Microarray (V2) G4110B (Feature Number version) was downloaded from the GEO (http://www.ncbi.nlm.nih.gov/geo/) database, which was deposited by Demeure et al. [18].
Identification of DEGs
We utilized the “limma” R bioconductor package (R Software version 3.5.2 and R Packages limma 3.38.2) [19] to screen the DEGs between ACC samples and normal whole adrenal gland samples. The FDR (false discovery rate) P < 0.05 and |log2fold change (FC)| > 1.335 for up regulated genes and |log2fold change (FC)| < − 0.891 for down regulated genes were chosen as the cut-off criteria. FDR (false discovery rate) was applied to help correct false-positives.
Pathway enrichment analysis
BIOCYC (https://biocyc.org/) [20], Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/pathway.html) [21], Pathway Interaction Database (PID) (https://wiki.nci.nih.gov/pages/viewpage.action?pageId=315491760) [22], REACTOME (https://reactome.org/) [23], GenMAPP (http://www.genmapp.org/) [24], MSigDB C2 BIOCARTA (v6.0) (http://software.broadinstitute.org/gsea/msigdb/collections.jsp) [25], PantherDB (http://www.pantherdb.org/) [26], Pathway Ontology (http://www.obofoundry.org/ontology/pw.html) [27], and Small Molecule Pathway Database (SMPDB) (http://smpdb.ca/) [28] were reference knowledge bases involving systems information, genomic information, and chemical information. To further explore the biological significance of DEGs, we used a online tool ToppCluster ToppCluster (https://toppcluster.cchmc.org/) [29] with the ability to analyze and visualize data to perform enrichment analysis of pathways. P value < 0.05 was considered a significant pathway enrichment.
Gene ontology (GO) enrichment analysis
Gene ontology (GO) (http://www.geneontology.org/) [30] is frequently used to annotate genes from high-throughput genome or transcriptome data. ToppCluster (https://toppcluster.cchmc.org/) [29] is an online functional annotation tool for multiple genes or proteins. P value < 0.05 was considered a significant GO enrichment.
PPI network and sub-modules analysis
Protein interaction network analysis (PINA) (http://omics.bjcancer.org/pina/home.do) [31] is an online software that contains comprehensive information of multiple proteins and is mainly used to evaluate the PPI information and this software integrates different PPI databases such as IntAct (https://www.ebi.ac.uk/intact/) [32], Molecular INTeraction Database (MINT, https://mint.bio.uniroma2.it/) [33], Biological General Repository for Interaction Datasets (BioGRID, https://thebiogrid.org/) [34], Database of Interacting Proteins (DIP, https://dip.doe-mbi.ucla.edu/dip/Main.cgi) [35], and Human Protein Reference Database (HPRD, http://www.hprd.org/) [36]. Cytoscape (http://www.cytoscape.org/) [37] version (3.7.1) was used to visualize the PPI networks of both up and down regulated genes. Confidence score ≥ 0.4 and maximum number of interactors = 0 were selected as the cut-off criteria. Five topological properties such as node degree [38], betweenness [39], stress [40], closeness [41], and clustering coefficient [42] were calculated in PPI networks. PEWCC1 [43] java plug-in of Cytoscape was utilized to illuminate the biological significance of gene modules in colon cancer with degree cut-off = 2, node score cut-off = 0.2, k-core = 2, and max. depth = 100.
Construction of target genes–miRNA regulatory network
Regulatory relationship between target genes (up and down regulated) and miRNA using online tool miRNet (https://www.mirnet.ca/) [44] which integrates 10 miRNA databases such as TarBase (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index) [45], miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/download.php) [46], miRecords (http://miRecords.umn.edu/miRecords) [47], miR2Disease (http://www.mir2disease.org/) [48], HMDD (http://www.cuilab.cn/hmdd) [49], PhenomiR (http://mips.helmholtz-muenchen.de/phenomir/) [50], SM2miR (http://bioinfo.hrbmu.edu.cn/SM2miR/) [51], PharmacomiR (http://www.pharmaco-mir.org/) [52], EpimiR (http://bioinfo.hrbmu.edu.cn/EpimiR/) [53], and starBase (http://starbase.sysu.edu.cn/) [54]. Target genes–miRNA regulatory network was visualized by Cytoscape (http://www.cytoscape.org/) [37].
Construction of target genes–TF regulatory network
Regulatory relationship between target genes (up and down regulated) and TF using online tool NetworkAnalyst (https://www.networkanalyst.ca/) [55] online tool which integrates one TFs database ENCODE (https://www.encodeproject.org/pipelines/ENCPL138KID/) [56]. Target genes–TF regulatory network was visualized by Cytoscape (http://www.cytoscape.org/) [37].
Survival analysis and stage-related expression analysis of hub genes
UALCAN (http://ualcan.path.uab.edu/cgi-bin/ualcan-res.pl) [57] is an online tool for analyzing the gene expression profiles of different tumor stages and survival analysis from the Cancer Genome Atlas (TCGA). Thus, we could verify the expression levels of hub genes in all stages of ACC as well as survival analysis based on the high and low expression of hub genes in ACC. P value < 0.05 was considered as having statistical significance.
Genetical modification of hub genes
Online tool CBio Cancer Genomics Portal (http://www.cbioportal.org) [58] is an open strong platform that is use full for visualization, analysis, and downloads of large-scale cancer genomic dataset of ACC. This online tool is use full for researchers to explore and compare genetic alterations across samples.
Results
Identification of DEGs
GSE19775 dataset (15 ACC samples and 15 normal whole adrenal gland samples) were first downloaded and preprocessed (normalized) is shown in Fig. 1a and b. After preprocessing and removing batch effects, we analyzed the DEGs of GSE19775 using the limma package, using FDR (false discovery rate) P < 0.05 and |log2fold change (FC)| > 1.335 for up regulated genes and |log2fold change (FC)| < − 0.891 for down regulated genes as the cut-off criterion. We screened 884 DEGs, including 441 up regulated genes and 443 down regulated genes in ACC samples compared to normal whole adrenal gland samples was shown in volcano plot (Fig. 2 and Table 1). The results of the cluster analysis of DEGs (up and down regulated) come to know that significant differences between the ACC samples and normal whole adrenal gland samples (Figs. 3 and 4).
Box plots of the expression profiles. A: Before normalization (red) and B: after normalization (yellow). (A1,A2, A3, A4, A5, A6, A7, A8, A9,A10, A11, A12, A13, A14, A15 = adrenocortical carcinoma samples, B1,B2, B3, B4, B5,B6, B7, B8, B9,B10, B11, B12, B13, B14, B15 = normal whole adrenal gland samples)
A volcano plot of differentially-expressed genes
Heat map of gene expression (Upregulated genes). The color key represents the logFC of DEGs. FC, fold change (A1,A2, A3, A4, A5, A6, A7, A8, A9,A10, A11, A12, A13, A14, A15 = adrenocortical carcinoma samples, B1,B2, B3, B4, B5,B6, B7, B8, B9,B10, B11, B12, B13, B14, B15 = normal whole adrenal gland samples)
Heat map of gene expression (Down-regulated genes). The color key represents the logFC of DEGs. FC, fold change (A1,A2, A3, A4, A5, A6, A7, A8, A9,A10, A11, A12, A13, A14, A15 = adrenocortical carcinoma samples, B1,B2, B3, B4, B5,B6, B7, B8, B9,B10, B11, B12, B13, B14, B15 = normal whole adrenal gland samples)
Pathway enrichment analysis
Pathway enrichment analysis from different pathway databases (BIOCYC, KEGG, PID, REACTOME, GenMAPP, MSigDB C2 BIOCARTA, PantherDB, and SMPDB) showed that the up regulated genes were mainly associated with catecholamine biosynthesis, glucocorticoid biosynthesis, aldosterone synthesis and secretion, tyrosine metabolism, IL6-mediated signaling events, IL23-mediated signaling events, metallothioneins bind metals, metabolism of steroid hormones, steroid hormone metabolism, tyrosine metabolism, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, biosynthesis of neurotransmitters, Dopamine receptor mediated signaling pathway, adrenaline and noradrenaline biosynthesis, leptin system, insulin-like growth factor signaling, catecholamine biosynthesis, and steroidogenesis are listed in Table 2, mean while down regulated genes were mainly associated with pyrimidine deoxyribonucleosides salvage, glutamate removal from folates, systemic lupus erythematosus, FOXM1 transcription factor network, E2F transcription factor network, SIRT1 negatively regulates rRNA Expression, epigenetic regulation of gene expression, methionine metabolism, pyrimidine metabolism, CDK Regulation of DNA Replication, ensemble of genes encoding core extracellular matrix including ECM glycoproteins, collagens and proteoglycans, integrin signaling pathway, serine glycine biosynthesis, mismatch repair pathway, nucleotide excision repair, and adenosine deaminase deficiency are listed in Table 3.
Gene ontology (GO) enrichment analysis
Gene ontology (GO) enrichment analysis from all three GO categories [biological process (BP), cellular component (CC), and molecular function (MF)] showed that the up regulated genes were mainly associated with blood vessel morphogenesis, response to other organism, extracellular matrix, extracellular space, heparin binding, and G protein-coupled receptor binding are listed in Table 4, mean while down regulated genes were mainly associated with cell cycle phase transition, negative regulation of chromosome segregation, chromosome, centromeric region, spindle, protein dimerization activity, and protein heterodimerization activity are listed in Table 5.
PPI network and sub-modules analysis
We constructed a PPI network to further explore the interaction between the common DEGs (up and down regulated) by using PINA database and Cytoscape. A total of 5050 nodes and 9534 edges were included in the PPI networks (up regulated) (Fig. 5). The hub genes with highest node degree in this PPI network such as YWHAZ (degree = 805), HSPA5 (degree = 394), STAT3 (degree = 213), APOE (degree = 183), and GRK5 (degree = 175) are listed in Table 6. A total of 5146 nodes and 11,499 edges were included in the PPI networks (down regulated) (Fig. 6). The hub genes with highest node degree in this PPI network such as FN1 (degree = 1012), VCAM1 (degree = 671), UBD (degree = 664), BARD1 (degree = 252), and TK1 (degree = 169) are listed in Table 6. R square = 0.761 and correlation coefficient = 0.967 for node degree (up regulated) (Fig. 7a), meanwhile R square = 0.761 and correlation coefficient = 0.978 for node degree (down regulated) (Fig. 7b). The hub genes with highest betweenness in PPI network for up regulated genes such as YWHAZ (betweenness = 0.26018915), HSPA5 (betweenness = 0.10211544), STAT3 (betweenness = 0.05179592), TH (betweenness = 0.05146886), and CD83 (betweenness = 0.04395976) are listed in Table 6. R square = 0.602 and correlation coefficient = 0.105 for betweenness (Fig. 8a). The hub genes with highest stress in PPI network for up regulated genes such as YWHAZ (stress = 110811588), HSPA5 (stress = 43035116), ICAM1 (stress = 27529814), HOXB2 (stress = 23,027,092), and STAT3 (stress = 22,678,172) are listed in Table 6. R square = 0.001 and correlation coefficient = 0.111 for stress (Fig. 8b). The hub genes with highest closeness in PPI network for up regulated genes such as YWHAZ (closeness = 0.40211725), HSPA5 (closeness = 0.38225204), NR4A1 (closeness = 0.36144752), IRS1 (closeness = 0.35719883), and MAP3K5 (closeness = 0.35502372) are listed in Table 6. R square = 0.186 and correlation coefficient = 0.338 for closeness (Fig. 8c). The hub genes with lowest clustering coefficient in PPI network for up regulated genes such as CRLF1 (clustering coefficient = 0), CRH (clustering coefficient = 0), ARHGAP18 (clustering coefficient = 0), FJX1 (clustering coefficient = 0), and FMO3 (clustering coefficient = 0) are listed in Table 6. R square = 0.447 and correlation coefficient = 0.686 for clustering coefficient (Fig. 8d). The hub genes with highest betweenness in PPI network for down regulated genes such as FN1 (betweenness = 0.24710836), UBD (betweenness = 0.14892195), VCAM1 (betweenness = 0.11467148), BARD1 (betweenness = 0.05118512), and TK1 (betweenness = 0.04488624) are listed in Table 6. R square = 0.574 and correlation coefficient = 0.109 for betweenness (Fig. 9a). The hub genes with highest stress in PPI network for down regulated genes such as FN1 (stress = 358,695,218), UBD (stress = 286,343,980), VCAM1 (stress = 222,971,648), SLPI (stress = 46,566,718), and BARD1 (stress = 43,410,948) are listed in Table 6. R square = 0.011 and correlation coefficient = 0.090 for stress (Fig. 9b). The hub genes with highest closeness in PPI network for down regulated genes such as FN1 (closeness = 0.42073523), VCAM1 (closeness = 0.39252408), MCM3 (closeness = 0.39092723), PHGDH (closeness = 0.38138386), and MCM6 (closeness = 0.3806143) are listed in Table 6. R square = 0.198 and correlation coefficient = 0.293 for closeness (Fig. 9c). The hub genes with lowest clustering coefficient in PPI network for down regulated genes such as CYP3A5 (clustering coefficient = 0), POPDC2 (clustering coefficient = 0), PNLIPRP2 (clustering coefficient = 0), KIAA1407 (clustering coefficient = 0), and CX3CR1 (clustering coefficient = 0) are listed in Table 6. R square = 0.663 and correlation coefficient = 0.810 for clustering coefficient (Fig. 9d).
Protein–protein interaction (PPI) networks for differentially-expressed genes (DEGs). (Green round shape node represents upregulated genes). Green dots are also genes which are not hub or differential expressed genes
Protein–protein interaction (PPI) networks for differentially-expressed genes (DEGs). (Pink round shape node represents down-regulated genes). Green dots are also genes which are not hub or differential expressed genes
Node degree distribution for up and down regulated genes. Red lines: regression lines
Regression diagrams for upregulated genes (a betweenness centrality; b stress centrality; c closeness centrality; d clustering coefficient). The red straight lines and the red curve lines: regression lines
Regression diagrams for down-regulated genes (a betweenness centrality; b stress centrality; c closeness centrality; d clustering coefficient). The red straight lines and the red curve lines: regression lines
Subsequently, we performed module analysis of the whole network by the PEWCC1 plug-in. A total of 773 modules were obtained from the PPI network (up regulated). The four most significant modules were selected in this PPI network such as module 6, module 30, module 37, and module 55 (Fig. 10). Module 6 had 48 nodes and 119 edges. Hub genes in this modules such as YWHAZ (degree = 805), GRK5 (degree = 175), and NOLC1 (degree = 99). Module 30 had 10 nodes and 19 edges. Hub genes in this modules such as ID2 (degree = 46), STAT3 (degree = 213), HES1 (degree = 43), NR4A1 (degree = 116), ZNF331 (degree = 11), and YWHAZ (degree = 805). Module 37 had 8 nodes and 22 edges. Hub genes in this modules such as APOE (degree = 183), PLAT (degree = 34), C1S (degree = 12), SERPING1 (degree = 137), HP (degree = 160), APOC1 (degree = 151), and SH3BP5 (degree = 142). Module 55 had 7 nodes and 12 edges. Hub genes in this modules such as STAT1 (degree = 174), IPO7 (degree = 47), and HSPA4L (degree = 40). Meanwhile, total of 923 modules were obtained from the PPI network (down regulated). The four most significant modules were selected in this PPI network such as module 2, module 8, module 10, and module 27 (Fig. 11). Module 2 had 69 nodes and 239 edges. Hub genes in this modules such as VCAM1 (degree = 671), FN1 (degree = 1012), MCM3 (degree = 126), MCM2 (degree = 110), and MCM6 (degree = 100). Module 8 had 36 nodes and 74 edges. Hub genes in this modules such as UBD (degree = 664), MAGED2 (degree = 37), HIST1H1C (degree = 144), MAD2L1 (degree = 60), and PHGDH (degree = 76). Module 10 had 29 nodes and 89 edges. Hub genes in this modules such as HSPB1 (degree = 129), HIST1H2BJ (degree = 23), VCAM1 (degree = 671), UHRF1 (degree = 35), MCM3 (degree = 126), NCOR2 (degree = 140), HIST1H1C (degree = 144), TOP2A (degree = 110), FN1 (degree = 1012), PBK (degree = 91), ASNS (degree = 43), MCM2 (degree = 110), TMPO (degree = 58), MCM6 (degree = 100), HMGB2 (degree = 39), HIST1H2BL (degree = 21), TYMS (degree = 39), CHAF1B (degree = 26), and PHGDH (degree = 76). Module 27 had 17 nodes and 47 edges. Hub genes in this modules such as CCNE1 (degree = 84), POLD1 (degree = 67), CDT1 (degree = 55), and PRC1 (degree = 27).
Modules in PPI network. The pink round nodes denote down-regulated genes. Green dots are also genes which are not hub or differential expressed genes
Modules in PPI network. The pink round nodes denote down-regulated genes. Green dots are also genes which are not hub or differential expressed genes
Construction of target genes–miRNA regulatory network
Target genes (up and down regulated) interacts with miRNA are shown Figs. 12 and 13. Top five up regulated targeted genes such as YWHAZ interacts with 212 miRNAs, GATA6 interacts with 207 miRNAs, LDLR interacts with 167 miRNAs, BZW1 interacts with 149 miRNAs, and IGFBP5 interacts with 144 miRNAs. Meanwhile, top five down regulated targeted genes such as TXNIP interacts with 228 miRNAs, MAPKAPK5 interacts with 179 miRNAs, PMAIP1 interacts with 147 miRNAs, RAD51 interacts with 147 miRNAs, and MICA interacts with 142 miRNAs (Table 7).
The network of upregulated DEGs and their related miRNAs. The yellow circle nodes are the down-regulated target gene and blue diamond nodes are miRNAs. Black lines means interaction with adjutant genes or miRNAs
The network of down-regulated DEGs and their related miRNAs. The red circle nodes are the down-regulated target gene and blue diamond nodes are miRNAs. Black lines means interaction with adjutant genes or miRNAs
Construction of target genes–TF regulatory network
Target genes (up and down regulated) interacts with TF are shown Figs. 14 and 15. Top five up regulated targeted genes such as HSPA1A interacts with 67 TFs, PHOX2B interacts with 54 TFs, H19 interacts with 52 TFs, CEBPD interacts with 51 TFs, and DNAJC3 interacts with 50 TFs. Meanwhile, top five down regulated targeted genes such as F11R interacts with 91 TFs, CHAF1B interacts with 85 TFs, FANCG interacts with 82 TFs, CENPF interacts with 82 TFs, and XRCC1 interacts with 82 TFs (Table 8).
TF–gene network of predicted target up regulated genes. Pink triangles are TFs and green circles are target upregulated genes. Yellow lines means interaction with adjutant genes or TFs
TF–gene network of predicted target down-regulated genes. Blue triangles are TFs and pink circles are target down-regulated genes. Yellow lines means interaction with adjutant genes or TFs
Survival analysis and stage-related expression analysis of hub genes
TCGA data of ACC patients are used via the UALCAN data portal. Kaplan–Meier curve for overall survival of TCGA patients with ACC is obtained according to the low and high expression of each gene. The results showed that patients in the high mRNA expression group for YWHAZ had worse overall survival than those in the low expression group (P = 0.012) (Fig. 16a), the high mRNA expression group for STAT1 had worse overall survival than those in the low expression group (P = 0.024) (Fig. 16b), the high mRNA expression group for ICAM1 had worse overall survival than those in the low expression group (P = 0.008) (Fig. 16c, a), the high mRNA expression group for SH3BP5 had worse overall survival than those in the low expression group (P = 0.016) (Fig. 16d), the high mRNA expression group for CD83 had worse overall survival than those in the low expression group (P = 0.00016) (Fig. 16e), the high mRNA expression group for FN1 had worse overall survival than those in the low expression group (P = 0.0069) (Fig. 16f), the high mRNA expression group for TK1 had worse overall survival than those in the low expression group (P = 0.0001) (Fig. 16g), the high mRNA expression group for HIST1H1C had worse overall survival than those in the low expression group (P = 0.0057) (Fig. 16h), the high mRNA expression group for CABLES1 had worse overall survival than those in the low expression group (P = 0.017) (Fig. 16i), and the high mRNA expression group for MCM3 had worse overall survival than those in the low expression group (P = 0.00011) (Fig. 16j). TCGA data analysis showed that hub genes such as YWHAZ, STAT1, ICAM1, CD83, FN1, TK1, HIST1H1C, and MCM3 were highly expressed in stages 4 (Fig. 17), while hub genes such as SH3BP5 and CABLES1 were highly expressed in stages 2 and stages 1 (Fig. 18).
Kaplan–Meier survival curves using TCGA data validate the prognostic value of genes expressed in ACC. (Blue is low expression and Red is high expression)
Validation of the expression of hub genes in the TCGA database. High expression of hub genes in stages 4. ACC:Adrenocortical carcinoma
Validation of the expression of hub genes in the TCGA database. High expression of hub genes in stages 2 and 1. ACC:Adrenocortical carcinoma
Genetical alteration of the five hub genes
Five hub genes’ alteration statuses in TCGA bladder cancer patients were analyzed using the CbioPortal database. The five hub genes such as YWHAZ, STAT1, SH3BP5, TK1, and HIST1H1C altered in 0% patients, while five hub genes such as ICAM1, CD83, FN1, CABLES1, and MCM3 altered in 3% (missence mutation and amplification) and 1.1% (missence mutation and amplification) patients and the frequency of alteration of each hub gene are shown in Fig. 19.
Genetical alteration analysis (genetical alterations towards these ten genes)
Discussion
In recent years, although the morbidity of ACC has less, it is still malignant tumors worldwide. Specific biomarkers and effective targets are still needed to be diagnosed.
In this study, a total of 884 DEGs were picked out from the profile of GSE19775, including 441 up regulated genes and 443 down regulated genes. Gene such as phenylethanolamine N-methyltransferase (PNMT) [59] and cocaine- and amphetamine-regulated transcript (CART) [60] were associated with development of adrenal pheochromocytoma, but these genes may be responsible for progression of ACC. Methylation inactivation of tumor suppressor gene proenkephalin (PENK) was responsible for development of colorectal cancer [61], but inactivation of this gene may be identified with growth of ACC. Chromogranin A (parathyroid secretory protein 1) (CHGA) was important for progression of gastroenteropancreatic neuroendocrine tumors [62], but this gene may be diagnosed with development of ACC. Genes such as FGF23 [63] and NEK3 [64] were liable for invasion of prostate cancer cells, but these genes may be associated with invasion of ACC cells. Single nucleotide polymorphism (SNP) in SLC2A4RG was linked with development of glioblastoma [65], but SNP in this gene may be liable for advancement of ACC. Alteration in CHTF18 was linked with development of endometrial cancer [66], but mutation in this gene may be responsible for pathogenesis of ACC. HMGB2 was identified with development chemoresistance in gastric cancer [67], but this gene may be responsible for drug resistance in ACC. EZH2 was reasonable for pathogenesis of ACC [68].
In pathway enrichment analysis, up regulated genes were mainly associated with several pathways from different pathway databases (BIOCYC, KEGG, PID, REACTOME, GenMAPP, MSigDB C2 BIOCARTA, PantherDB, and SMPDB) such as catecholamine biosynthesis, aldosterone synthesis and secretion, IL6-mediated signaling events, metallothioneins bind metals, steroid hormone metabolism, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins, dopamine receptor mediated signaling pathway, and leptin system. SNP in genes such as CYP11A1 [69], HSD3B1 [70], SCARB1 [71], lumican (LUM) [72], and IRS1 [73] were liable for progression of different cancers such as colorectal cancer, prostate cancer, thyroid cancer and breast cancer, but SNP in these genes may be involved in development of ACC. Gene such as CYP11B2 [74], HSD3B2 [75], STAT3 [76], CYP17A1 [77], CCN3 [78], IGFBP2 [79], IGFBP5 [80], IGFBP6 [80], and PRKAR2B [81] were diagnosed with growth of ACC. Genes such as MC2R [82], CCAAT/enhancer binding protein (C/EBP), beta (CEBPB) [83], and ANGPTL4 [84] were involved in proliferation of different cancer cells such as prostate cancer, breast cancer, and colorectal cancer, but these genes may be associated with proliferation of ACC cells. Gene such as NR4A1 [85], NR4A2 [86], PRKD3 [87], ADAMTS1 [88], CCL13 [89], CRLF1 [90], CXCL12 [91], CXCL2 [92], decorin (DCN) [93], FGF9 [94], GDF15 [95], plasminogen activator, tissue (PLAT) [96], S100A8 [97], S100A9 [97], SEMA6A [98], SRPX2 [99], and tenascin XB (TNXB) [100] were important for invasion of different cancer cells such as breast cancer, colorectal cancer, prostate cancer, thyroid cancer, ovarian cancer, endometrial cancer, gastric cancer, glioblastoma, and mesothelioma, but these genes may be linked with invasion of ACC cells. IL6 was responsible for inflammation in breast cancer [101], but this gene may be linked with inflammation in ACC. High expression of jun B proto-oncogene (JUNB) [102], STAT1 [103], MT1E [104], FGF7 [105], fibromodulin (FMOD) [106], SPON2 [107], and THBS1 [108] were important for advancement of different cancers such as renal cancer, colon cancer, breast cancer, prostate cancer and esophageal squamous cell carcinoma, but high expression of these genes may be identified with growth of ACC. Methylation inactivation of tumor suppressor genes such as SOCS3 [109], MT1G [110], MT1H [111], ADAMTS9 [112], ANGPTL1 [113], FBLN1 [114], FCN2 [115], SEMA3B [116], and TIMP4 [117] were diagnosed with growth of different cancers such as prostate cancer, gastric cancer hepatocellular carcinoma, and lung cancer, but loss of these genes may be responsible for pathogenesis of ACC. Decrease expression of tumor suppressor gene MT1F was liable for progression of hepatocellular carcinoma [118], but less expression of this gene may be identified with growth of ACC. Genes such as CCL2 [119], CXCL1 [120], and EFEMP1 [121] were associated with angiogenesis in different cancers such as breast cancer and colorectal cancer, but these genes may be linked with angiogenesis in ACC. Tyrosine hydroxylase (TH) [122] and neuropeptide Y (NPY) [123] were responsible for pathogenesis of pheochromocytoma, but these genes may be associated with development of ACC. Dopa decarboxylase (aromatic l-amino acid decarboxylase) (DDC), CYP21A2, KCNK3, low density lipoprotein receptor (LDLR), CCAAT/enhancer binding protein (C/EBP), delta (CEBPD), MT1A, MT1B, MT1X, CTSD, FBLN5, FGF12, MFAP4, SERPING1, SPOCK3, dopamine beta-hydroxylase (dopamine beta-monooxygenase) (DBH) EPB41L1 and guanine nucleotide binding protein (G protein), alpha z polypeptide (GNAZ) were novel biomarkers for pathogenesis of ACC in these pathways. Meanwhile down regulated genes were mainly associated with several pathways from different pathway databases such as pyrimidine deoxyribonucleosides salvage, systemic lupus erythematosus, FOXM1 transcription factor network, SIRT1 negatively regulates rRNA expression, methionine metabolism, CDK regulation of DNA replication, integrin signaling pathway, mismatch repair pathway, and adenosine deaminase deficiency. High expression of genes such as TK1 [124], thymidylatesynthetase (TYMS) [125], CCNE1 [126], CDT1 [127], FN1 [128], NTN4 [129], and adenosine deaminase (ADA) [130] were associated with pathogenesis of different cancer such as breast cancer, colorectal cancer, ovarian cancer, and prostate cancer, but elevated expression of these genes may be liable for development of ACC. Genes such as HLA-DQB1 [131], MCM2 [132], and MSH2 [133] were involved in pathogenesis of ACC. Metylation inactivation of tumor suppressor gene DNMT1 was responsible for progression of colon cancer [134], but loss of this gene may be associated with pathogenesis of ACC. Genes such as MCM3 [135] and MCM6 [136] were associated with proliferation of different cancer cells such as ovarian cancer and lung cancer, but these genes may be liable for advancement of ACC. Genes such as COL1A1 [137], COL3A1 [138], LAMA3 [139], and LAMB1 [140] were linked with invasion of different cancer cells such as gastric cancer, bladder cancer, head and neck cancers, and colorectal cancer, but these genes may be diagnosed with growth of ACC. Mutation in genes such as EXO1 [141] and POLD1 [142] were associated with pathogenesis of colorectal cancer, but alteration in this gene may be identified with growth of ACC. SNP in LIG1 was involved in progression of lung cancer [143], but this gene may be responsible for development of ACC. HIST1H2BB, HIST1H2BC, HIST1H2BD, HIST1H2BE, HIST1H2BF, HIST1H2BH, HIST1H2BI, HIST1H2BJ, HIST1H2BL, HIST1H2BN, HIST1H2BO, HIST1H4F, HIST1H4H, HIST1H4J, HIST1H4K, HLA-DRB4, MAT1A, COL14A1, COL27A1, COL4A5, COL5A2, RFC4, and RFC5 were novel biomarkers for pathogenesis of ACC in these pathways.
In GO enrichment analysis, up regulated genes were mainly associated with all GO categories (BP, CC and MF) such as blood vessel morphogenesis, extracellular matrix, and heparin binding. Genes such as adrenomedullin (ADM) [144], apolipoprotein E (APOE) [145], GATA6 [146], and RARRES2 [147] were responsible for pathogenesis of ACC. Genes such as CDH2 [148], CITED1 [149], FZD4 [150], STARD13 [151], XBP1 [152], and latexin (LXN) [153] were associated with invasion of different cancer cells such as lung cancer, colorectal cancer, prostate cancer, breast cancer, and thyroid cancer, but these genes may be liable for invasion of ACC cells. Methylation inactivation of tumor suppressor genes such as DLL1 [154], HAND2 [155], and UNC5B [156] were identified with progression of different cancer such as lung cancer endometrial cancer and colorectal cancer, but loss of these genes may be involved in development of ACC. High expression of genes such as ENPP2 [157], GJA1 [158], HES1 [159], RAMP1 [160], SIRT1 [161], TFAP2B [162], TNFRSF11B [163], and SAA1 [164] were important for advancement of different cancer such as hepatocellular carcinoma, endometrial cancer, colon cancer, prostate cancer, lung cancer, and gastric cancer, but elevated expression of these genes may be diagnosed with ACC. Genes such as HOXA5 [165] and HOXA7 [166] were linked with cell cycle progression in different cancer such as breast cancer and ovarian cancer, but these genes may be involved in cell cycle progression in ACC. SNP in NRXN3 was important for development of breast cancer [167], but this polymorphic gene may be involved in development of ACC. Low expression of TGFBR3 was responsible for advancement of prostate cancer [168], but loss of this gene may associated with growth of ACC. Platelet-derived growth factor receptor, alpha polypeptide (PDGFRA), SCG2, TCDD-inducible poly(ADP-ribose) polymerase (TIPARP), and IL1RL1 were novel biomarkers for pathogenesis of ACC in these GO categories. Meanwhile, down regulated genes were mainly associated with all GO categories such as cell cycle phase transition, chromosome, centromeric region, and protein dimerization activity. High expression of genes such as anillin, actin binding protein (ANLN) [169], NEK2 [170], timeless homolog (TIMELESS) [171], TPX2 [172], TTK protein kinase (TTK) [173], CDCA8 [174], KIF18A [175], OIP5 [176], SPC25 [177], ZWINT [178], asparagine synthetase (ASNS) [179], E2F8 [180], LCN2 [181], and RAB11FIP4 [182] were important for progression of different cancers such as pancreatic cancer, breast cancer, lung cancer, colorectal cancer, gastric cancer, ovarian cancer, and hepatocellular carcinoma, but increased expression of these genes may be associated with development of ACC. Genes such as BIRC5 [183], BUB1 [184], CCNB1 [185], CCNB2 [186], CDCA5 [187], CDKN3 [188], centromere protein F (CENPF) [189], and SPAG5 [190] were associated with proliferation of different cancer cells such as breast cancer, gastric cancer, hepatocellular carcinoma, and ovarian cancer, but these genes may be linked with proliferation of ACC cells. Methylation inactivation of tumor suppressor genes such as CABLES1 [191], GADD45A [192], PLK2 [193], and NR3C1 [194] were involved in pathogenesis of different cancers such as ovarian cancer, breast cancer, and colorectal cancer, but loss these genes may be identified with development of ACC. SNP in genes such as hyaluronan-mediated motility receptor (HMMR) [195]. DNMT3B [196], aryl hydrocarbon receptor (AHR) [197] hydroxyprostaglandin dehydrogenase 15-(NAD) (HPGD) [198], and UGT1A6 [199] were liable for advancement of different cancer such as breast cancer, lung cancer, and colorectal cancer, but these polymorphic genes may be linked with development of ACC. Alteration in genes such as MAD2L1 [200] and BARD1 [201] were associated with pathogenesis of breast cancer, but mutation in this gene may be involved in development of ACC. Genes such as maternal embryonic leucine zipper kinase (MELK) [202], RRM2 [203], NR0B1 [204], and TOP2A [14] were important for pathogenesis of ACC. Alteration in tumor suppressor gene PLAGL1 was liable for growth of pheochromocytomas [205], but mutation in this gene may be associated with pathogenesis of ACC. CEP192, KNTC1, MCM8, KIF22, NUP107, STAG3, BTBD11, CD2, CD3D, DPP4, fibroblast activation protein, alpha (FAP) granzyme A (granzyme 1, cytotoxic T-lymphocyte-associated serine esterase 3) (GZMA), H2BFS, hematopoietically expressed homeobox (HHEX), HOMER1, v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT), NCOA6, SRGAP2, and TCFL5 were novel biomarkers for pathogenesis of ACC in these GO categories.
In this study, we finally identify five hub genes (up regulated) were in PPI network with high node degree such as YWHAZ, HSPA5, STAT3, APOE, and GRK5. High expression of tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ) was responsible for progression of head and neck cancer [206], but elevated expression of this gene may be identified with growth of ACC. Genes such as HSPA5 [207] and GRK5 [208] were associated with invasion of different cancer cells such as colorectal cancer and prostate cancer, but these genes may be liable for invasion of ACC cells. Five hub genes (up regulated) were in PPI network with high betweenness such as YWHAZ, HSPA5, STAT3, TH, and CD83. SNP in CD83 was diagnosed with growth of cervical cancer [209], but this polymorphic gene may be involved in development of ACC. Five hub genes (up regulated) were in PPI network with high stress such as YWHAZ, HSPA5, ICAM1, HOXB2, and STAT3. ICAM1 was linked with invasion of breast cancer cells [210], but this gene may be associated with invasion of ACC cells. High expression of HOXB2 was involved in pathogenesis of cervical cancer [211], but elevated expression of this gene may be responsible for progression of ACC. Five hub genes (up regulated) were in PPI network with high closeness such as YWHAZ, HSPA5, NR4A1, IRS1, and MAP3K5. MAP3K5 was important for invasion of prostate cancer cells [212], but this gene may be linked with invasion of ACC cells. Five hub genes (up regulated) were in PPI network with low clustering coefficient such as CRLF1, CRH, ARHGAP18, FJX1, and FMO3. Corticotropin releasing hormone (CRH) was liable for progression of pheochromocytomas [213], but this gene may be associated with advancement of ACC. ARHGAP18 was identified with invasion of breast cancer cells [214], but this gene may be involved in invasion of ACC cells. High expression of FJX1 was diagnosed with growth of nasopharyngeal carcinoma [215], but over expression of this gene may be identified with progression of ACC. SNP in FMO3 was responsible for advancement of colorectal cancer [216], but this polymorphic gene may be associated with development of ACC. Meanwhile, five hub genes (down regulated) were in PPI network with high node degree such as FN1, VCAM1, UBD, BARD1, and TK1. High expression of genes such as VCAM1 [217] and ubiquitin D (UBD) [218] were responsible for pathogenesis of different cancers such as gastric cancer and colon cancer, but elevated expression of these genes may be liable for growth of ACC. Five hub genes (down regulated) were in PPI network with high betweenness such as FN1, UBD, VCAM1, BARD1, and TK1. Five hub genes (down regulated) were in PPI network with high stress such as FN1, UBD, VCAM1, SLPI, and BARD1. Secretary leukocyte protease inhibitor (SLPI) was linked with invasion of ovarian cancer cells [219], but this gene may be involved in invasion of ACC cells. Five hub genes (down regulated) were in PPI network with high closeness such as FN1, VCAM1, MCM3, PHGDH, and MCM6. High expression phosphoglycerate dehydrogenase (PHGDH) was responsible for pathogenesis of melanoma [220], but over expression of this gene may be identified with growth of ACC. Five hub genes (down regulated) were in PPI network with low clustering coefficient such as CYP3A5, POPDC2, PNLIPRP2, KIAA1407, and CX3CR1. SNP in CYP3A5 was important for pathogenesis of breast cancer [221], but this polymorphic gene may associated with development of ACC. CX3CR1 was linked with invasion of prostate cancer cells [222], but this gene may be involved in invasion of ACC cells. POPDC2, PNLIPRP2, and KIAA1407 were novel biomarkers for pathogenesis of ACC in these PPI network.
Hub genes (up regulated) such as YWHAZ, GRK5, NOLC1, ID2, STAT3, HES1, NR4A1, ZNF331, APOE, PLAT, C1S, SERPING1, HP, APOC1, SH3BP5, STAT1, IPO7, and HSPA4L were in all four modules. NOLC1 was associated with proliferation of hepatocellular carcinoma cell [223], but this gene may be responsible for progression of ACC cells. High expression of genes such as ID2 [224], APOC1 [225], and HSPA4L [226] were involved in pathogenesis of different cancer such as pancreatic cancer, lung cancer, and hepatocellular carcinoma, but increase expression these genes may be linked with growth of ACC. Methylation inactivation of tumor suppressor gene ZNF331 was important for development of colorectal cancer [227], but loss of this gene may be liable for growth of ACC. SNP in haptoglobin (HP) was associated with pathogenesis of breast cancer [228], but this polymorphic gene may be liable for growth of ACC. IPO7 was involved in proliferation of prostate cancer cells [229], but this gene may be associated with proliferation of ACC cells. C1S and SH3BP5 were novel biomarkers for pathogenesis of ACC in these modules. Meanwhile, hub genes (down regulated) such as VCAM1, FN1, MCM3, MCM2, MCM6, UBD, MAGED2, HIST1H1C, MAD2L1, PHGDH, HSPB1, HIST1H2BJ, UHRF1, NCOR2, TOP2A, PBK, ASNS, TMPO, HMGB2, HIST1H2BL, TYMS, CHAF1B, CCNE1, POLD1, CDT1, and PRC1 were in all four modules. Methylation inactivation of tumor suppressor gene HSPB1 was responsible for progression of prostate cancer [230], but loss of this gene may be identified with pathogenesis of ACC. High expression of genes such as UHRF1 [231] and PDZ binding kinase (PBK) [232] were important for advancement of different cancer such as lung cancer and prostate cancer, but elevated expression of these genes may be associated with development of ACC. NCOR2 was responsible for proliferation of glioblastoma cells [233], but this gene may be linked with proliferation of ACC cells. MAGED2 HIST1H1C, thymopoietin (TMPO), CHAF1B, and PRC1 were novel biomarkers for pathogenesis of ACC in these modules.
Target genes (up regulated) such as YWHAZ, GATA6, LDLR, BZW1, and IGFBP5 were in target genes–miRNA regulatory network. BZW1 was novel biomarker for pathogenesis of ACC in this network. Meanwhile, target genes (down regulated) such as TXNIP, MAPKAPK5, PMAIP1, RAD51, and MICA were in target genes–miRNA regulatory network. Decrease expression of tumor suppressor genes such as thioredoxin interacting protein (TXNIP) [234] and PMAIP1 [235] was diagnosed with growth of different cancer types such as thyroid cancer and pancreatic cancer, but loss of expression of these genes may liable for pathogenesis of ACC. High expression of RAD51 was involved in pathogenesis of breast cancer [236], but elevated expression of this gene may be linked with growth of ACC. MAPKAPK5 and MHC class I polypeptide-related sequence A (MICA) were novel biomarkers for pathogenesis of ACC in this network.
Target genes (up regulated) such as HSPA1A, PHOX2B, H19, CEBPD, and DNAJC3 were in target genes–TF regulatory network. HSPA1A was associated with pathogenesis of colorectal cancer [237], but this gene may be important for development of ACC. Inactivation of H19 was involved in pathogenesis of ACC [238]. PHOX2B and DNAJC3 were novel biomarkers for pathogenesis of ACC in this network. Meanwhile, target genes (down regulated) such as F11R, CHAF1B, FANCG, CENPF, and XRCC1 were in target genes–TF regulatory network. F11R was linked with advancement of glioma [239], but this gene may be associated with development of ACC. SNP in genes such as fanconianemia, complementation group G (FANCG) [240], and XRCC1 [241] were liable for progression of different cancers such as pancreatic cancer and breast cancer, but these genes may be liable for growth of ACC.
Survival analysis revealed that genes such as YWHAZ, STAT1, ICAM1, SH3BP5, CD83, FN1, TK1, HIST1H1C, CABLES1, and MCM3 were predicting shorter survival of ACC. Expression levels revealed that hub genes such as STAT1, ICAM1, CD83, FN1, TK1, HIST1H1C, and MCM3 were highly expressed in ACC (stages 4), meanwhile those such as SH3BP5 and CABLES1 were highly expressed in ACC (stage 2 and stage 1).
In conclusion, we diagnosed several hub genes and pathways that were closely related to the initiation and progression of ACC using a series of bioinformatics analyses of DEGs in cancer and normal samples. Identification of the associated genes, YWHAZ, FN1, GRK5, VCAM1, GATA6, TXNIP, HSPA1A, and F11R, provided greater insight into the specific molecular mechanisms underlying ACC occurrence and development, especially in terms of the pathways involved in catecholamine biosynthesis, aldosterone synthesis and secretion, pyrimidine deoxyribonucleosides salvage, and systemic lupus erythematosus. Upon further experimental validation of these results, these genes may serve as potential research targets for therapy and thereby contribute to greater understanding of the molecular mechanisms underlying the progression of ACC.
Availability of data and materials
The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE19775) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE19775)].
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Acknowledgements
I thank Demeure, Arizona State University, 1001 S. McAllister Ave, Tempe, AZ, USA very much, the author who deposited their microarray dataset, GSE19775, into the public GEO database.
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Ali Mohamed Alshabi associated with project administration, methodology, and review and editing; Ali Mohamed Alshabi, Ibrahim Ahmed Shaikh, and Basavaraj Vastrad participated in writing original draft and investigation; and Chanabasayya Vastrad performed software, supervision, formal analysis, and validation. Basavaraj Vastrad and Ali Mohamed Alshabi made substantial contributions to acquisition of resources, formal analysis, and validation.
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Alshabi, A.M., Vastrad, B., Shaikh, I.A. et al. Identification of important invasion and proliferation related genes in adrenocortical carcinoma. Med Oncol 36, 73 (2019). https://doi.org/10.1007/s12032-019-1296-7
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DOI: https://doi.org/10.1007/s12032-019-1296-7