Integrated Analysis of Gene Expression and Genomic Aberration Data in Osteosarcoma (OS)

ORIGINAL ARTICLE Integrated analysis of gene expression and genomic aberration data in osteosarcoma (OS)

Y Xiong, S Wu, Q Du, A Wang and Z Wang

Cytogenetic analyses have revealed that complex karyotypes with numerous and highly variable genomic aberrations including single-nucleotide polymorphisms (SNPs) and copy number variants (CNVs), are observed in most of the conventional osteosarcomas (OSs). Several genome-wide studies have reported that the dysregulated expression of many genes is correlated with genomic aberrations in OS. We first compared OS gene expression in Gene Expression Omnibus (GEO) data sets and genomic aberrations in International Cancer Genome Consortium (ICGC) database to identify differentially expressed genes (DEGs) associated with SNPs or CNVs in OS. Then the function annotation of SNP- or CNV-associated DEGs was performed in terms of gene ontology analysis, pathway analysis and protein–protein interactions (PPIs). Finally, the expression of genes correlated with both SNPs and CNVs were confirmed by quantitative reverse-transcription PCR. Eight publicly available GEO data sets were obtained, and a set of 979 DEGs were identified (472 upregulated and 507 downregulated DEGs). Moreover, we obtained 1039 SNPs mapped in 938 genes, and 583 CNV sites mapped in 2915 genes. Comparing genomic aberrations and DGEs, we found 41 SNP-associated DEGs and 124 CNV-associated DEGs, in which 7 DGEs were associated with both SNPs and CNVs, including WWP1, EXT1, LDHB, C8orf59, PLEKHA5, CCT3 and VWF. The result of function annotation showed that ossification, bone development and skeletal system development were the significantly enriched terms of biological processes for DEGs. PPI network analysis showed that CCT3, COPS3 and WWP1 were the significant hub proteins. We conclude that these genes, including CCT3, COPS3 and WWP1 are candidate driver genes of importance in OS tumorigenesis.

Cancer Gene Therapy (2015) 22, 524–529; doi:10.1038/cgt.2015.48; published online 2 October 2015

INTRODUCTION expression of many genes is correlated with genomic aberrations Osteosarcoma (OS) is one of the most prevalent forms of in OS. Indeed, recent studies have shown that alterations in SNPs malignant bone cancer that occurs predominantly in children and are of great importance as they can affect gene expression levels, adolescents.1,2 With current treatment, the 5-year survival rate for alternative splicing, DNA methylation and miRNA-mediated gene 9 people with localized OS is in the range of 60–80%. However, if expression levels in different types of cells. Similarly, CNVs have fi been associated with changes in gene expression values in various the OS has already spread when it is rst found, the 5-year survival 10,11 rate is ~ 15–30% (http://www.cancer.org/cancer/osteosarcoma). cell types. fi Because of its high metastatic potential, usually in lung, OS has In this paper, we identi ed the differentially expressed genes become the second highest cause of cancer-related death in these (DEGs) between OS and control tissues. Then, the important DEGs age groups.3 Thus, there is an urgent need for the understanding containing SNPs and CNVs were analyzed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of underlying mechanism of OS. There are considerable studies about exploring the mechanism enrichment. The whole-genome analysis will reveal new genetic of OS. The dysregulation of multiple intracellular signaling mutations associated with OS. pathways has been reported in the initiation and development of this disease, including phosphatidylinositol 3-kinase/Akt pathway, Wnt pathway and JAK2/STAT3 signaling pathway.4–6 MATERIALS AND METHODS The expression of the Notch1 intracellular domain in immature The gene expression and genomic aberrations data in OS osteoblasts was sufficient to drive the formation of bone tumors, The gene expression profiling of OS was obtained from Gene Expression including OS.7 Based on the studies, Notch activation may act as a Omnibus (GEO; available at http://www.ncbi.nlm.nih.gov/geo/) database.12 driver of OS. Despite the exciting progress on the disease, the The following key words and their combinations were used: homo sapiens etiology of OS is not fully elucidated. and OS. The data sets that were obtained from array were downloaded. Cytogenetic analyses have revealed that complex karyotypes Raw data were preprocessed via background correction and normalization, and the significance analysis of microarray was used to identify DGEs with numerous and highly variable genomic aberrations including between OS and the controls. Genes with false discovery rate o0.05 were single-nucleotide polymorphisms (SNPs) and copy number considered as DEGs. 8 variants (CNVs), are observed in most of the conventional OS. Moreover, raw data for SNP and copy number variation of OS Several genome-wide studies have reported that the dysregulated can be obtained from International Cancer Genome Consortium

Department of Orthopedics, Daping Hospital, Third Military Medical University, Chongqing, China. Correspondence: Professor Ziming Wang, Department of Orthopedics, Daping Hospital, Third Military Medical University, Chongqing 400042, China. E-mail: [email protected] Received 13 June 2015; revised 28 August 2015; accepted 30 August 2015; published online 2 October 2015 Gene and genomic aberration in OS Y Xiong et al 525 (ICGC; https://dcc.icgc.org/) database. After screening the raw data of software (San Diego, USA).16 Biological General Repository for Interaction BOCA-UK (Bone Cancer UK) project in ICGC database, genes with SNPs or Datasets (BioGRID) was used for the analysis.17,18 CNVs were obtained. To know whether genomic aberrations affected gene expression in OS, Collection of clinical specimens we compared genes with SNPs or CNVs in OS and DEGs between OS and controls to identify SNP- or CNV-associated DEGs. In the present study, the The tissue samples were obtained from six patients diagnosed with OS, DGEs with both SNPs and CNVs were considered as key DGEs in OS. and patient consent was also obtained. In addition, the project was approved by our institutional ethics committee. The tumor tissues were dissected during operation, and the nearby normal tissues were used as GO and pathway enrichment analysis controls. Both tumor and normal tissue samples were immediately frozen The Database for Annotation, Visualization and Integrated Discovery in liquid nitrogen. (DAVID)13 is the most common tool to analyze the functional enrichment of genes. To identify the functions of SNP- or CNV-associated DEGs, the Quantitative reverse-transcription PCR fi 14 DAVID was used to identify the signi cantly enriched GO categories, and The total RNA from each sample was extracted using TRIzol method the pathway enrichment analysis was also conducted by using the KEGG (Beijing DingGuo ChangSheng Biotechnology Co., Ltd, Beijing, China). 15 o database. The P-value 0.05 was selected as the cutoff criterion. Primer 5.0 software (PREMIER Biosoft, Palo Alto, CA, USA) was used to design primers for SYBR Green experiments based on template sequences, Protein–protein interaction network construction and an ABI 7500 real-time PCR system (Applied Biosystems, Carlsbad, CA, USA) was used. For each replicate, cDNA was synthesized from 1 to 5 μg For the genes whose expression was associated with both SNPs and CNVs, RNA using SuperScript reverse transcriptase II (TOYOBO, Shanghai, China). and DEGs with more than one SNP or CNV in the study population, The quantitative reverse-transcription PCR reaction consisted of 12.5 μlof – protein protein interaction (PPI) analysis was performed using Cytoscape Power SYBR Green PCR master mix (Applied Biosystems/Life Technologies, Carlsbad, CA, USA), 1 μl diluted cDNA and 0.25 μl of each primer (10 μM) contributing a total volume of 25 μl. Reactions were conducted in triplicate, and run in 96-well plates with the following conditions: 94 °C for 2 min, 35 Table 1. Seven DEGs whose expression was associated with SNP cycles of 94 °C for 30 sec, 55 °C for 30 sec, 72 °C for 30 sec and 72 °C for and CNV 10 min. Relative gene expression was analyzed using DataAssist Software version 3.0 (Applied Biosystems/Life Technologies), using human actin Gene ID Gene Official full name gene as endogenous controls for RNA load and gene expression in symbol analysis. All experiments were performed in triplicate. Comparisons between treatment groups and controls were conducted 11059 WWP1 WW domain-containing E3 ubiquitin protein with Student's t‑test. Po0.05 was considered to indicate a statistically ligase 1 fi 2131 EXT1 Exostosin glycosyltransferase 1 signi cant difference. The sequences of PCR primers used for quantitative 3945 LDHB Lactate dehydrogenase B reverse-transcription PCR were listed in Supplementary Table S1. 401466 C8orf59 Chromosome 8 open reading frame 59 54477 PLEKHA5 Pleckstrin homology domain containing family A member 5 RESULTS 7203 CCT3 Chaperonin containing TCP1, subunit 3 (gamma) 7450 VWF von Willebrand factor The correlation between genomic aberrations and gene expression Abbreviations: CNV, copy number variant; DEGs, differentially expressed fi genes; SNP, single-nucleotide polymorphism. A total of eight expression pro ling studies met the inclusion criteria and were included. The characteristics of the individual

Table 2. Top 15 functional enrichment of SNP-associated DEGs

GO term GO name No. of genes P-value

Molecular functions GO:0042802 Identical protein binding 9 1.55E − 04 GO:0042803 Protein homodimerization activity 6 0.0014351 GO:0046983 Protein dimerization activity 6 0.0112222 GO:0005201 Extracellular matrix structural constituent 3 0.0200474 GO:0005518 Collagen binding 2 0.0876619 GO:0001948 Glycoprotein binding 2 0.0899873

Biological processes GO:0001503 Ossiﬁcation 4 0.0020554 GO:0060348 Bone development 4 0.0024893 GO:0007155 Cell adhesion 7 0.0038399 GO:0022610 Biological adhesion 7 0.0038671 GO:0001501 Skeletal system development 5 0.005125 GO:0043069 Negative regulation of programmed cell death 5 0.0077475 GO:0060548 Negative regulation of cell death 5 0.0078226 GO:0045934 Negative regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 5 0.0254824 GO:0051172 Negative regulation of nitrogen compound metabolic process 5 0.0266224 GO:0043067 Regulation of programmed cell death 6 0.0314205 GO:0010941 Regulation of cell death 6 0.0318579 GO:0022604 Regulation of cell morphogenesis 3 0.0339233 GO:0043066 Negative regulation of apoptosis 4 0.0428292 GO:0030199 Collagen ﬁbril organization 2 0.0624163 GO:0006916 Anti-apoptosis 3 0.0760071 Abbreviations: DEGs, differentially expressed genes; GO, gene ontology; SNP, single-nucleotide polymorphism.

Table 3. Top 15 functional enrichment of CNV-associated DEGs Table 4. The signal pathway enrichment of SNP-associated DEGs

GO term GO name No. of P-value KEGG ID KEGG name No. of genes P-value genes hsa04512 ECM-receptor interaction 4 0.0017144 Molecular functions hsa04510 Focal adhesion 5 0.0022941 GO:0042803 Protein homodimerization 9 0.0030755 activity Abbreviations: DEGs, differentially expressed genes; ECM, extra-cellular GO:0004252 Serine-type endopeptidase 6 0.0054157 matrix; KEGG, Kyoto Encyclopedia of Genes and Genomes; SNP, single- activity nucleotide polymorphism. GO:0004175 Endopeptidase activity 9 0.0061472 GO:0046983 Protein dimerization activity 11 0.0061968 GO:0002020 Protease binding 3 0.0083548 Table 5. The signal pathway enrichment of CNV-associated DEGs GO:0008236 Serine-type peptidase activity 6 0.0098353 GO:0017171 Serine hydrolase activity 6 0.0102896 KEGG ID KEGG name No. of P-value GO:0005509 Calcium ion binding 14 0.0161278 genes GO:0005198 Structural molecule activity 11 0.0173601 GO:0042802 Identical protein binding 11 0.0184161 hsa04540 Gap junction 6 0.001525219 GO:0070011 Peptidase activity, acting on 10 0.0189319 hsa00910 Nitrogen metabolism 3 0.020231496 L-amino acid peptides hsa04610 Complement and coagulation 4 0.028248095 GO:0008233 Peptidase activity 10 0.0244391 cascades GO:0005201 Extracellular matrix structural 4 0.0250996 hsa05218 Melanoma 4 0.030407188 constituent hsa05215 Prostate cancer 4 0.053615702 GO:0005518 Collagen binding 3 0.0284069 GO:0005161 Platelet-derived growth factor 2 0.0776376 Abbreviations: CNV, copy number variant; DEGs, differentially expressed receptor binding genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Biological processes GO:0001501 Skeletal system development 11 1.22E − 04 GO:0009719 Response to endogenous 12 1.96E − 04 C8orf59, PLEKHA5, CCT3 (chaperonin containing TCP1, subunit 3) stimulus and VWF (Table 1). GO:0010035 Response to inorganic 8 8.06E − 04 substance GO:0042493 Response to drug 8 0.0010945 GO:0009725 Response to hormone 10 0.001564 Functional and signal pathway annotation stimulus The functional enrichment analysis was conducted for the SNP GO:0010033 Response to organic 14 0.0024589 substance and CNV corresponding genes, respectively. In the analysis, six GO:0030198 Extracellular matrix 5 0.0073529 molecular functions and 15 biological processes were enriched for organization genes related to SNPs (Table 2). The functional annotation of GO:0043627 Response to estrogen 5 0.0076015 CNV-related genes was also presented (Table 3). The result stimulus showed that ossification, bone development and skeletal system GO:0009612 Response to mechanical 4 0.008183 fi stimulus development were the signi cantly enriched terms of biological GO:0048008 Platelet-derived growth factor 3 0.0085233 processes for DEGs. KEGG pathway enrichment analysis showed receptor signaling pathway that SNP-related genes were highly enriched in extra-cellular GO:0006541 Glutamine metabolic process 3 0.0085233 matrix-receptor interaction and focal adhesion, and CNV corre- GO:0001503 Ossification 5 0.0103972 sponding genes were mostly enriched in gap junction, nitrogen GO:0060348 Bone development 5 0.0130591 metabolism, complement and coagulation cascades, melanoma GO:0010038 Response to metal ion 5 0.015317 GO:0031667 Response to nutrient levels 6 0.0153852 and prostate cancer (Tables 4 and 5). Abbreviations: CNV, copy number variant; DEGs, differentially expressed genes; GO, gene ontology. PPI network construction By using Cytoscape software, we established PPI networks containing 303 nodes and 328 edges for the genes whose studies for integrated analysis were displayed in Supplementary expression was associated with both SNPs and CNVs, and DEGs Table S2. Of these studies, 240 cases of OS and 35 cases of controls with more than one SNP or CNV in the study population. The were contained. By integrated analysis, a set of 979 DEGs were significant hub proteins contained CCT3 (degree = 78), COPS3 identified in OS compared with normal tissues, including 472 (degree = 54) and WWP1 (degree = 52; Figure 1). All the genes upregulated and 507 downregulated DEGs (Supplementary except COL2A1, ELN and EXT1 had indirect interactions with Table S3). others. After screening the data in BOCA–UK (Bone Cancer UK) project from ICGC repositories, we obtained 1039 SNPs mapped in 938 genes from 65 donors (Supplementary Table S4), and 583 CNVs sites mapped in 2915 genes from 48 donors (Supplementary The expression of key genes associated with both SNP and CNV Table S5). To verify the differential expression of key genes associated with Comparing with the genes with SNPs or CNVs in OS, we found both SNPs and CNVs, quantitative reverse-transcription PCR was 41 SNP-associated DEGs, 124 CNV-associated DEGs, in which performed. As displayed in Figure 2, WWP1, EXT1, LDHB, C8orf59, seven DGEs occurred in both SNPs and CNVs, including WWP1 PLEKHA5 and CCT3 were greatly upregulated in OS compared (WW domain-containing E3 ubiquitin protein ligase 1), exostosin with that in controls, while the expression of VWF was glycosyltransferase 1 (EXT1), lactate dehydrogenase B (LDHB), downregulated in OS.

Figure 1. Protein–protein interaction (PPI) network of key DGEs with single-nucleotide polymorphism (SNP) or copy number variant (CNV).

Figure 2. The mRNA expression of seven differentially expressed genes (DEGs) with both single-nucleotide polymorphism (SNP) and copy number variant (CNV) in six pairs of tumor and normal tissue samples. *Po0.05, ***Po0.01. t-Test was used to compare two groups.

DISCUSSION To better understand OS genesis, many studies suggested OS is a very heterogeneous and genomically unstable tumor. potential candidate genes and showed genomic alterations Co-occurring genomic alterations and gene expression changes driving OS development, and some interesting findings were – can be determined to identify putative driver genes in OS because observed from bioinformatics.19 23 A previous research showed passenger- and tissue-specific genes will be largely eliminated. In that a total of 1170 DEGs were screened (530 upregulated and 640 this study, we performed an integrated analysis of eight publicly downregulated) from GSE28424 microarray, and RPL8, PLCG1, available GEO data sets and genomic alteration data of SNPs or PLCG2, SYK, MAD2L1, AURKA, CDCA8, BUB1 and MELK were CNVs to identify biological mechanisms involved in the supposed to be involved in OS.22 In our study, a set of 979 DEGs pathogenesis of OS. were identified in OS compared with normal tissues, including 472

© 2015 Nature America, Inc. Cancer Gene Therapy (2015), 524 – 529 Gene and genomic aberration in OS Y Xiong et al 528 upregulated and 507 downregulated DEGs. The result of function CONCLUSION annotation showed that ossification, bone development and To conclude, our systematic screening of gene expression data skeletal system development were the significantly enriched sets and genomic aberration data have yielded 41 SNP-associated terms of biological processes for DEGs, which may be involved in DEGs and 124 CNV-associated DEGs in OS, in which 7 genes were the progression of OS. associated with both SNPs and CNVs, including WWP1, EXT1, It was reported that several genomic alterations were most LDHB, C8orf59, PLEKHA5, CCT3 and VWF. Most of them were the associated with OS, including LRP1-SNRNP25 and KCNMB4-CCND3 important driver genes in OS. Moreover, the three hub proteins, fusion genes, VEGF and Wnt signaling pathway alterations, WWOX CCT3, COPS3 and WWP1, may play an important role in the fi 23 deletion and ampli cation of APEX1 and RUNX2. In addition, it development of OS. Our study revealed that studying tumor was suggested that EZR, CDKN2A and MAP3K5 were with possible biology and pathology in a systematic manner can result in a fi 21 prognostic signi cance in OS. Even then, no new therapies with better understanding of osteosarcomagenesis, and can potentially a significant impact on survival have been developed in the past 20 identify new biomarkers for improvement of diagnosis and as decades. In the present study, by comparing the genes with possible targets for therapeutic intervention in OS. SNPs or CNVs in OS and 979 DEGs between OS and control, 41 SNP-associated DEGs and 124 CNV-associated DEGs were obtained, in which 7 DEGs occurred in both SNPs and CNVs, CONFLICT OF INTEREST including WWP1, EXT1, LDHB, C8orf59, PLEKHA5, CCT3 and VWF, The authors declare no conflict of interest. indicating that they may be important driver genes in OS. WWP1 is an E3 ubiquitin ligase and was first identified by its WW domain.24 Recently, it was reported that knockdown of WWP1 REFERENCES can inhibit growth and induce apoptosis in hepatoma carcinoma 1 Kansara M, Teng MW, Smyth MJ, Thomas DM. Translational biology of osteo- cells through the activation of caspase-3 and p53.25 The results sarcoma. Nat Rev Cancer 2014; 14:722–735. provided a hint that WWP1 may take part in the process of OS. 2 Moore DD, Luu HH. Osteosarcoma. Cancer Treat Res 2014; 162:65–92. EXT1 encodes an endoplasmic reticulum-resident type II 3 Ta HT, Dass CR, Choong PF, Dunstan DE. 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Supplementary Information accompanies the paper on Cancer Gene Therapy website (http://www.nature.com/cgt)