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Cancer Biomarkers 13 (2013) 367–375 DOI 10.3233/CBM-130367 IOS Press
Identification of novel human glioblastoma-specific transcripts by serial analysis of gene expression data mining Yanlin Sua , Jie Xionga , Zhitong Bingb , Xiaomin Zengc, Yong Zhanga, Xiaohua Fua and Xiaoning Penga,∗ a
Department of Internal Medicine, College of Medicine, Hunan Normal University, Changsha, Hunan, China Institute of Modern Physics of Chinese Academy of Sciences, Lanzhou, Gansu, China c Department of Statistics and Epidemiology, Public Health School, Central South University, Changsha, Hunan, China b
Abstract. BACKGROUND: Glioblastoma multiforme (GBM) remains the most common and aggressive primary brain tumor in adults with a poor median survival, and molecular biomarkers for GBM pathogenesis are in need. PURPOSE: The objective of this study is to identify potential novel genes for GBM pathogenesis by gene expression data mining. MATERIALS AND METHODS: Available SAGE libraries of GBM, astrocytoma, and normal brain tissues were collected from the Cancer Genome Anatomy Project (CGAP). Significance analysis for microarray (SAM) and CGAP-SAGE-Genie-DGED were used to identify differentially expressed tags, and specific tags that were differentially expressed only in GBM were further selected. Tags to genes association was performed by CGAP-SAGE-Genie-SAV. Immunohistochemistry was used to investigate distribution and validate expression of the interested gene. RESULTS: Three genes were significantly differentially expressed just in brain. up-regulated expression of STAB1 and downregulated expression of SH3GL2 and DNM3. Immunohistochemistry assay indicated that STAB1 mainly expressed in vascular endothelial cells and over-expressed in GBM samples compared to normal samples. CONCLUSIONS: Our study shows that data mining of public sources of gene expression is an effective way to identify novel tumor-associated genes, and this work may contribute to the identification of candidate genes for GBM angiogenesis. Keywords: GBM, astrocytoma, SAGE, specific gene, STAB1, angiogenesis
1. Introduction A glioma is a type of tumor that arises from neuroepithelial tissue. The most common site of gliomas is the brain [1]. Gliomas are named according to the specific cell type they share histological features with, but this is not necessarily associated with the originating ∗ Corresponding author: Xiaoning Peng, Mailing address: No.371, Tongzipo Road, Changsha, Hunan, China. Tel.: +86 731 88912598; Fax: +86 731 88912417; E-mail: [email protected].
cell. The main types of gliomas are: astrocytic tumors, ependymal tumors, oligodendroglial tumors, oligoastrocytic tumors according to the World Health Organization (WHO classification of central nervous system tumors) [2]. A glioma is defined as a primary brain tumor, accounting for 70% of cancers that begin as brain cells. The most frequent and malignant histological type in the adult population is glioblastoma multiforme (GBM), which accounts for 12–15% of all brain tumors and 50–60% of astrocytomas [3–5]. Prognosis for GBM patients remains very poor because it is difficult to treat. Complete removal
c 2013 – IOS Press and the authors. All rights reserved ISSN 1574-0153/13/$27.50
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by surgery is complicated by poor differentiation, rapid proliferation and strong invasion phenotype; chemotherapy and radiation therapy are not highly specific, killing both tumor and healthy cells, and may cause serious adverse reactions. Available information indicates that the median survival time for GBM patients is 12–18 months with comprehensive treatment combined with surgery, radiation, and the chemotherapy drug temozolomide [2,6,7]. A meta-analysis of 12 randomized clinical trials indicates that the overall survival rate of patients with GBM was 40% for one year, and only slightly higher (46%) after the addition of adjuvant therapies [8]. Although the incidence rate of GBM is relatively low (5–8/100,000) and only accounts for 2% of all cancers, GBM is the second leading cause of death for people less than 34 years of age and third for 35–54 years of age [2]. GBM remains a treatment-refractory disease in the field of neurosurgery, making it a considerable public health issue. Effective means of reducing GBM mortality are early detection and novel therapies to specific molecular targets. Currently, the molecular pathology of GBM and its correlation to behavior are not well known; many molecular aberrations detected in GBM may have diagnostic and/or prognostic implications. Amplification of oncogenes such as the epidermal growth factor receptor (EGFR) [5,9–11], as well as loss of tumor suppressors like PTEN, p53, or p16/INK4A are some of the most common genetic alterations in GBM [12–14]. It was recently reported that isocitrate dehydrogenase (IDH1) mutations and MGMT hypermethylation are associated with the prognosis of GBM [15–17]. However, this does not justify designation as GBM bio-marker for most of molecular changes because biomarkers should provide unique diagnostic, prognostic, or predictive information exceeding that provided by histological classification. Many oncogenes and tumor suppressor genes are involved in GBM development, so it is important to understand the differential gene expression between tumor and normal tissue. These data will help clarify the molecular mechanisms involved in GBM development, and could lead to the identification of new markers for GBM diagnosis and new treatment targets. Serial analysis of gene expression (SAGE) is a powerful tool that allows the analysis of overall gene expression patterns with digital analysis. Because SAGE does not require a preexisting clone, it can be used to identify and quantitate new and known genes [8–10]. This technique has been widely used in human studies, and various SAGE tags/SAGE libraries have been generated from differ-
ent cells and tissues [11]. The NCI’s Cancer Genome Anatomy Project (CGAP) currently provides SAGE library data sets from a variety of sources for tumor cells/tissues and corresponding normal controls online. In silico comparison of these SAGE libraries from diverse tissues can be used to discover differentially expressed transcripts, which has led to the identification of several novel and known cancer-related genes, such as MIA and MMP in gastric cancer, MIC-1, DMBTI, Neugrin74, CD74, CXCL, CEBI, Kallikrein6 in pancreatic cancer and HOXB13 in prostate cancer [12– 19]. These studies show that analysis of SAGE data is an effective method for identifying tumor-related genes and markers. Here we compare thirty-nine SAGE library datasets derived from normal brain and glioma tissues to screen for differentially expressed genes between GBM and normal brain tissue. We conclude that combined, multiple high-throughput analyses is an effective data mining strategy for cancer gene identification. This approach may improve the use of publicly available genomic data through strategic data mining of highthroughput analysis.
2. Methods 2.1. SAGE data collection Thirty-nine libraries (9 normal libraries, 30 tumor libraries) were found by SAGE Genie retrieval. Only short tag libraries with 10 bp tags and the same restriction enzymes (BsmF1 and NIaIII) were included in this study. GBM samples from children were omitted because GBM mainly occurs in adults and the tag number of children libraries may lead to outliers. GSM383776 library originated from non-glial cells, and was also omitted in this study [20]. The final normal sample set consisted of 5 libraries (GSM383772-GSM383775 and GSM383779), and the tumor sample set included 9 GBM libraries(GSM383720-GSM383729) and twenty astrocytoma (grade I–III) libraries (GSM383687-383 706). All libraries were produced by the lab of Gregory Riggins, Duke University Medical Center, USA. A database containing 1,066,151 different tags was generated from libraries that contain between 10,115 and 42,250 unique tags.
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2.2. Analysis of differentially expressed tags Selection was performed to reduce noise from the large number of collected tags. The criteria for selection were: i) “tags found in all normal libraries” vs. “tags found in all GBM libraries”; ii) “tags found in all normal libraries” vs. “tags found in all astrocytoma libraries”; iii) “tags found in all GBM libraries” vs. “tags found in all astrocytoma libraries”. The online tool CGAP-SAGE-Genie-DGED was used to compare the average number of tags in these three pools under different combinations. 2.3. Selection for GBM-specific tags GBM-specific tags were selected by the criterion that differentially expressed tags should be only found in GBM libraries [21]. Multi-Experiment Viewer [22] (MeV) was used to perform the Significance Analysis for Microarray [23] (SAM) to confirm the differential expression of specific tags. The association of tags to genes was performed by SAGE Genie SAV (http:// www.ncbi.nlm.nih.gov/SAGE/AnatomicViewer) [24] and a network spider complied by the Java programming language was applied. DAVID (http://david.abcc. ncifcrf.gov/) was applied to predict functional classes of annotated genes [25,26].
Fig. 1. Venn diagram indicating the number of differentially expressed tags from the comparisons of GBM, Astrocytoma and Normal libraries. All tags that were differentially expressed with a fold value > 3.0 and Q-value < 0.05 were used to create a Venn diagram: 248 tags were most discriminative for GBM, being differentially expressed both between GBM libraries and normal libraries and between GBM libraries and Astrocytoma libraries; 329 tags were differentially expressed in both GBM libraries and Astrocytoma libraries when compared with normal libraries, but they were not found to discriminate between GBM libraries and Astrocytoma libraries; 40 tags were differentially expressed among three pools.
2.4. Virtual northern Virtual northern blot analysis enables researchers to view the expression of a specific gene in all EST and SAGE libraries. In the CGAP database. The Gene Finder tool (http://cgap.nci.nih.gov/Genes/GeneFind er) was applied to check gene expression across all available libraries classified according to their tissue origins. 2.5. Cross-validation The leave-one-out cross-validation method was used to validate the newly identified specific genes in GBM library GSM383726. In this study, tumor samples and normal samples are mixed to construct a tumor pool and normal pool, respectively. GSM383726 consisted of five pooled Duke GBM primary tumors and was suitable for leave-one-out cross-validation. 2.6. Immunohistochemical assay of STAB1 A total of 16 unifocal, primary GBM surgically resected from patients at Second people’s hospital of hu-
nan province from 2003 to 2013 were used in this study. We excluded patients received neoadjuvant or adjuvant chemotherapy and/or radiotherapy, and patients with a history of concurrent malignant disease or other previous primary cancers. There were no other minor participants in our study. The anonymity of all patients was maintained, and all specimens were analyzed in a blinded manner. Tissue specimens were fixed promptly with 100 g/ L formaldehyde solution, embedded in paraffin and cut into 3 µm sections. Two-Step method was used to detect the expression of STAB1. The rabbit polyclonal antibody against human STAB1 was purchased from Santa Cruz Biotechnology Company, USA (SC98788). NovoLinkTM Polymer Detection System (RE 7290-K) Novocastra Laboratories, Newcastle, UK. These systems can be used for the visualization of mouse IgG, mouse IgM and rabbit IgG primary antibodies. These detection systems contain Peroxidase Block, Protein Block, Post Primary Block, Novolink Polymer, DAB Chromogen, Novolink DAB Substrate Buffer (Polymer) and Hematoxylin.
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Fig. 2. Significance Analysis for Microarray of 162 specific tags between GBM and normal SAGE libraries. Left down tail which shown in green color represent the significant tags with higher expression in the normal libraries and the other tail with red color represent the significant tags with higher expression in the GBM libraries. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/CBM-130367)
All sections were deparaffinnized and dehydrated with graded alcohol, and washed in phosphate buffered saline (PBS, pH 7.4) Appropriate endogenous peroxidase blocking was conducted before histochemical processing using the Novocastra Peroxidase Block RE7101. Antigen retrieval was performed by treating the slides in citrate buffer in a microwave for 15 min. The sections were cooled for 20 min at room temperature, and then been washed with distilledwater for 3 × 5 min. The slides were treated with Novocastra Protein Block( RE7102) to minimize non-specific staining (background) .The slides were incubated in a moist chamber with STAB1 rabbit polyclonal antibody (1:300) at 37◦ C for 2 hours. Liver, placenta samples were used as positive controls, PBS was used to substitute the primary antibody as a negative controls. After a complete wash in PBS,the slides were treated with the Novolink Polymer (RE7200-K) for 25 min at 37◦ C. After washed in PBS completely, the slides were developed in 0.05% freshly prepared diaminobenzedine solution (DAB) for 10 min, and then counterstained with hematoxylin. Finally, slides were dehydrated in ascending concentrations of ethanol, airdried, and mounted. Positive staining with STAB1 was
defined as brown staining of endothelial cell cytoplasm. 3. Results 3.1. Differentially expressed SAGE tags identified by DGED Gene expression profiles were collected from 8 GBM libraries, 20 astrocytoma (grade I–III) libraries, and 5 control normal brain libraries. CGAP-SAGEGenie-DGED was used to compare the average number of tags between pairwise pools. A difference threshold of greater than 3-fold change and a Q-value < 0.05 is considered significantly differentially expressed. There were 1961 differentially expressed tags between GBM and normal libraries, 1452 differentially expressed tags between astrocytoma libraries and normal libraries, and 728 tags differentially expressed between GBM and astrocytoma libraries. 3.2. GBM-specific tags Two hundred and forty-eight specific tags that were most discriminative for GBM were filtered out by
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Table 1 Specific tags that associated to genes Tag TGGAGTGAAA TACCTATTGT TCACCAAAAA GAAAGTCTCT AAATGGTTCT ATGACCACTG CCCTTCCTTT GATTAAGTGA GCCCTGTATT GGAGGGGACA TGTTTGGGGG CGGGGAGATG TGCCTCTGCG TGTCCACACA ACCAAAAACC AGTGGTGGCT ATCAAGAATC ATGTCTTTTC CAGGCCCCAC CCCCCACCTA CCGTGACTCT CGCAAGCTGG CTCCCCTGCC GACCGCAGGA GCCCCCAATA GCCCTTTCTC GTTTATGGAT TAAGAGTCTG TGGAAATGAC
Gene symbol SH3GL2 DNM3 STAB1 NTRK2 CCDC93 B3GAT1 ATP6V1G2 OCIAD2 ZDHHC22 SCG3 GLT25D1 NDRG2 CD151 ANTXR2 COL1A1 FMOD IFI30 IGFBP4 S100A11 PLP2 FSTL1 LMNA CAPG COL4A1 LGALS1 MRC2 MGP ATP9A COL1A1
Protein Expression Virtual northern name state in GBM in brain SH3-domain GRB2-like 2 down 1 dynamin 3 down 1 stabilin 1 up 1 neurotrophic tyrosine kinase, receptor, type 2 down 1 coiled-coil domain containing 93 down 0 beta-1,3-glucuronyltransferase 1 down 0 ATPase, H+ transporting, lysosomal 13kDa, V1 subunit G2 down 0 OCIA domain containing 2 up 0 zinc finger, DHHC-type containing 22 down 0 secretogranin III down 0 glycosyltransferase 25 domain containing 1 up 0 NDRG family member 2 down 0 CD151 molecule (Raph blood group) up 0 anthrax toxin receptor 2 up 0 collagen, type I, alpha 1 up 1 fibromodulin up 1 interferon, gamma-inducible protein 30 up 1 insulin-like growth factor binding protein 4 up 1 S100 calcium binding protein A11 up 1 proteolipid protein 2 (colonic epithelium-enriched) up 1 follistatin-like 1 up 1 lamin A/C up 1 capping protein (actin filament), gelsolin-like up 1 collagen, type IV, alpha 1 up 1 lectin, galactoside-binding, soluble, 1 up 1 mannose receptor, C type 2 up 1 matrix Gla protein up 1 ATPase, class II, type 9A down 1 collagen, type I, alpha 1 up 1
Virtual northern in other organs
kidney, ect kidney, ect prostate eye, ect skin, thyroid lung, ect eye, ect liver, ect mammary gland eye, ect eye, ect kidney, ect eye, ect lung, ect prostate kidney, ect skin lung, ect
In the “Virtual Northern in brain” column “1” indicates tags were differentially expressed in tissue brain while “0” indicates tags were not differentially expressed in brain, in the “Virtual Northern in other organs” column other organs were listed if the tags were differentially expressed.
the criterion for tags that were only differentially expressed in GBM. These tags were found to be differentially expressed by comparing of GBM with normal and with astrocytoma, but not differentially expressed by comparing astrocytoma with normal (Fig. 1). We found 985 tags that were aberrantly expressed in both GBM and Astrocytoma libraries when compared with normal libraries, but the expression of these tags was not significantly different between GBM and astrocytoma libraries (Fig. 1). Thus, these tags are likely related to the presence of a tumor but do not specifically discriminate either GBM or astocytoma. Overall, 40 tags had statistically significant differential expression among three pools (Fig. 1). The vast majority of tags were only present in few libraries; to avoid differentially expressed tags merely caused by seldom, highexpression libraries, 162 GBM specific tags expressed in the majority ( 80%) of libraries were filtered out for the next analysis. Subsequent confirmation of GBM-specific tags was performed by SAM analysis. An expression matrix of 162 specific tags was constructed, and the frequency
of each specific tag was normalized by dividing it by the total number of tags in the corresponding library and multiplying by 200,000 (CGAP normalization format); a log2 transformation was implemented. SAM analysis with suitable delta values (maintaining false discovery rate near 0) was adopted to confirm both the differences when comparing GBM vs. normal and GBM vs. Astrocytoma and the absence of any significant differences when comparing astrocytoma and normal (based on a threshold fold change > 3.0). 75 tags were confirmed to be differentially expressed in GBM vs. normal (Fig. 2); 91 tags were confirmed to be differentially expressed in GBM vs. astrocytoma; the absence of differences between astrocytoma and normal was almost completely confirmed, except for one differentially expressed tag. As expected, clustering heatmaps of differentially expressed tags show a clear separation between GBM and normal (Fig. 3) and GBM and astrocytoma, but no obvious separation between astrocytoma and normal. Finally, an overlapping tags set including 54 tags was generated from 75 confirmed differentially expressed tags between GBM and
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rection was used. The significance level was set at a p-value = 0.05. DAVID analysis showed that 54 specific genes that had significantly increased expression in GBM were related to “negative regulation of cellsubstrate adhesion” (GO:10812), “carbohydrate binding” (GO:30246) and “negative regulation of biological process” (GO:48519), while GO analysis of the down-regulated GBM-specific genes revealed no significant overrepresentation of GO annotations. 3.4. Virtual northern and cross-validation
Fig. 3. Clustering for GBM and Normal SAGE libraries. “N” represents normal brain and “G” represents GBM. The analysis shows a clear separation between GBM and normal with 75 tags which were most discriminative for GBM. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/CBM-130367)
normal, 91 confirmed differentially expressed tags between GBM and astrocytoma and 161 absent differences tags between astrocytoma and normal. 3.3. Tags to genes association The CGAP-SAGE Genie-SAGE Anatomic Viewer (SAV) was used to map specific differentially expressed SAGE tags to genes and a net spider program complied by the Java programming language was applied. Tags represent the 3’ end of known genes and ranks greater than 90% were considered to be successful associations. Ambiguous tags that represent different genes or EST clusters where at least two genes met the above conditions were removed. Among 54 specific differential tags for GBM, 29 tags were successfully assigned to 28 specific genes; 18 genes were up-regulated and 10 genes were downregulated in GBM (Table 1). DAVID was applied to predict the functional classes of annotated genes, and the Benjamini and Hochberg false discovery rate cor-
By analysis of SAGE libraries we found 28 genes specific for GBM. We then adopted Virtual Northern to detect the expression pattern of these genes across tissues. Genes with expression levels in EST and SAGE libraries of GBM that were 3-fold higher compared to normal brain and with a P-value < 0.05 were designated as positive. Fifteen genes were confirmed to be up-regulated in GBM and 3 genes were confirmed to be down-regulated in GBM (Table 1). Most importantly, STAB1, SH3GL2, DNM3 and NTRK2 were only differentially expressed in the brain; STAB1 was up-regulated in GBM, while SH3GL2 and DNM3 were down-regulated. The differential expression pattern of NTRK2 was inconsistent between SAGE DEGE analysis (down-regulated) and Virtual Northern (upregulated, Table 1), thus, NTRK2 was excluded from the list of GBM-specific genes (Table 2). To further validate Virtual Northern results, all other cancer SAGE data in CGAP were compared by SAGEGenie-DGED and the results showed that STAB1, SH3GL2, DNM3 were only differentially expressed in the brain. These genes have potential diagnostic and therapeutic implications in GBM. A comparison between GSM383726 and normal pool was also conducted for cross-validation and STAB1 was confirmed differentially expressed. 3.5. Immunohistochemistry GBM samples were diagnosed by two experienced pathologists. To validate the differentially expressed pattern of STAB1 at the protein level, immunohistochemistry was performed under standard protocol which indicated STAB1 mainly expressed in vascular endothelial cells (Fig. 4A) and differentially expressed between GBM and normal brain (P < 0.0001; Fig. 4B).
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Table 2 Virtual northern of GBM candidate genes
Four genes were only differentially expressed in brain and the differential expression patterns of were consistent with SAGE DEGE analysis except NTRK2.
Fig. 4. Immunohistochemical DAB-hematoxylin staining of STAB1 in GBM (A) and normal brain tissue (B). (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/CBM-130367)
4. Discussion Combining multiple analytical approaches would reduce the possibility of artifacts in identification of differentially expressed genes. In this study, we use SAGE-Genie-DGED, SAM and Virtual Northern approaches to compare gene expression profiles between GBM, normal brain and astrocytoma libraries to identify novel GBM-specific genes. We compared the gene expression profiles of 9 GBM, 20 astrocytomas and 5 normal brain tissues in different combinations. Fifty-four tags that successfully matched to 30 genes most discriminated GBM from other tissues. Among these genes, many transcription factors like OTX2 and EGFR were found to have higher expression in GBM when compared to normal cerebellum. Other important, GBM-associated genes like S100A11 were up-regulated in GBM and also differentially expressed in bone marrow, eye, kidney, liver, lymph node and skin. CAPG was upregulated in GBM, Kidney and Liver and downregulated in Prostate; NDRG2 was up-regulated in
Skin, Kidney and Liver and down-regulated in GBM, Colon and Lung (Table 1). All of these validate earlier findings. To the best of our knowledge, STAB1 and DNM3 have never been reported in GBM, while SH3GL2 was newly reported as a GBM suppressor, consistent with our analysis. SH3 domain-containing GRB2-like protein 2 (SH3 GL2) is implicated in synaptic vesicle endocytosis. SH3GL2 is mainly distributed in the central nervous system, particularly enriched in the presynaptic ganglion [27]. A recent study showed that SH3GL2 was a target of miR-330 that plays an oncogenic role in human GBM [28]. SH3GL2 is a candidate tumor suppressor gene and particularly highly expressed in the central nervous system [29]. From previous studies and our findings, we inferred that SH3GL2 may be a marker of GBM. Dynamin-3 (DNM3) is involved in producing bundles and is able to bind and hydrolyze GTP; its role in cancer is currently unclear. STAB1 is the only up-regulated GBM-specific gene found by this study, and its differential expression pattern in GBM was confirmed by the live-one-out val-
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idation and immunohistochemistry. STAB1 acts as a scavenger receptor for acetylated low density lipoprotein, and is also involved in angiogenesis and cell-cell interactions [30,31]. Previous studies have proposed that angiogenesis, invasion and metastasis were hallmarks of cancer [32]. It is possible that STAB1 can modulate the tube formation by interacting with various growth factors [30]. To the best of our knowledge, GBM has one of the highest rates of new blood vessel formation and require angiogenesis for transformation, proliferation, migration and survival [33]. Overexpression of STAB1 in vascular endothelial cells is observed in GBM by immunohistochemistry, which indicates that STAB1 may have a pivotal role in malignant GBM. At present, the use anti-angiogenic therapy for inhibiting the growth of GBM is widely accepted [34] and inhibitors of angiogenesis are currently the focus of drug development in GBM [35]. Although some known important genes like VEGF and IL-6 were associated with angiogenesis, these genes were found to be significantly changed in GBM vs. normal and astrocytoma vs normal but absent differences in GBM vs. astrocytoma, which implies VEGF and IL-6 may be common angiogenic factors in glioma and STAB1 maybe angiogenic accelerator in the most malignant glioma. After comparison of these data and validation by immunohistochemistry, our results indicated that STAB1 may be a vital factor for GBM through promoting angiogenesis, and this mechanism warrants further study. In summary, this work confirms that public SAGE databases can be used to effectively identify genes that are differentially expressed between tumors or tissues. Three potential markers of GBM were identified, and STAB1 may be a potential novel factor involved in rapid angiogenesis in GBM.
Acknowledgments We thank Gregory Riggins’s laboratory of Duke University Medical Center for generating the data and made data public. We also thank rongrong zhang of Second people’s hospital of hunan province for assisting us to achieve immunohistochemistry. This work was supported by grants from the National Natural Foundation of China (No. 81071628), Natural Science foundation of Hunan grants (No. 11JJ2013) and grants from the Foundation for Young Talents of Gansu (1208RJYA013).
References [1]
Mamelak AN, Jacoby DB. Targeted delivery of antitumoral therapy to glioma and other malignancies with synthetic chlorotoxin (TM-601). Expert Opinion on Drug Delivery. 2007; 4: 175-86. [2] Louis D, Ohgaki H, Wiestler O, Cavenee W, Burger P, Jouvet A, et al. The 2007 WHO Classification of Tumours of the Central Nervous System. Acta Neuropathol. 2007; 114: 97109. [3] Jeuken JM, Deimling A, Wesseling P. Molecular pathogenesis of oligodendroglial tumors. J Neurooncol. 2004; 70: 161-81. [4] Hegi ME, Diserens A-C, Gorlia T, Hamou M-F, de Tribolet N, Weller M, et al. MGMT Gene Silencing and Benefit from Temozolomide in Glioblastoma. New Engl J Med. 2005; 352: 997-1003. [5] Parsons DW, Jones S, Zhang X, Lin JC-H, Leary RJ, Angenendt P, et al. An Integrated Genomic Analysis of Human Glioblastoma Multiforme. Science. 2008; 321: 1807-12. [6] Pfister S, Janzarik WG, Remke M, Ernst A, Werft W, Becker N, et al. BRAF gene duplication constitutes a mechanism of MAPK pathway activation in low-grade astrocytomas. The Journal of Clinical Investigation. 2008; 118: 1739-49. [7] Riemenschneider M, Jeuken JM, Wesseling P, Reifenberger G. Molecular diagnostics of gliomas: state of the art. Acta Neuropathol. 2010; 120: 567-84. [8] Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial Analysis of Gene Expression. Science. 1995; 270: 484-7. [9] Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett Jr DE, et al. Characterization of the Yeast Transcriptome. Cell. 1997; 88: 243-51. [10] Murray D, Doran P, MacMathuna P, Moss A. In silico gene expression analysis – an overview. Molecular Cancer. 2007; 6: 50. [11] Tuteja R, Tuteja N. Serial Analysis of Gene Expression: Applications in Human Studies. Journal of Biomedicine and Biotechnology. 2004; 2004: 113-20. [12] El-Meanawy MA, Barathan S, Hayden PS, Iyengar SK, Schelling JR, Sedor JR. Serial analysis of gene expression. Renal Disease: Techniques and Protocols. 2003; 86: 257-74. [13] Lauriola M, Ugolini G, Rosati G, Zanotti S, Montroni I, Manaresi A, et al. Identification by a Digital Gene Expression Displayer (DGED) and test by RT-PCR analysis of new mRNA candidate markers for colorectal cancer in peripheral blood. International Journal of Oncology. 2010; 37: 519-25. [14] Ossandon F, Villarroel C, Aguayo F, Santibanez E, Oue N, Yasui W, et al. In Silico analysis of Gastric carcinoma Serial Analysis of Gene Expression libraries reveals different profiles associated with ethnicity. Molecular Cancer. 2008; 7: 22. [15] Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Church DM, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Research. 2005; 33: D39-D45. [16] Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, et al. NCBI GEO: mining tens of millions of expression profiles – database and tools update. Nucleic Acids Research. 2007; 35: D760-D5. [17] Aung PP, Oue N, Mitani Y, Nakayama H, Yoshida K, Noguchi T, et al. Systematic search for gastric cancer-specific genes based on SAGE data: melanoma inhibitory activity and matrix metalloproteinase-10 are novel prognostic factors in patients with gastric cancer. Oncogene. 2006; 25: 2546-57. [18] Hustinx SR, Cao DF, Maitra A, Sato N, Martin ST, Sudhir D, et al. Differentially expressed genes in pancreatic ductal ade-
Y. Su et al. / Identification of novel human glioblastoma-specific transcripts by gene expression data mining
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
nocarcinomas identified through serial analysis of gene expression. Cancer Biology & Therapy. 2004; 3: 1254-61. Edwards S, Campbell C, Flohr P, Shipley J, Giddings I, tePoele R, et al. Expression analysis onto microarrays of randomly selected cDNA clones highlights HOXB13 as a marker of human prostate cancer. British Journal of Cancer. 2005; 92: 376-81. Kavsan VA, Dmitrenko VV, Shostak KO, Bukreieva TV, Vitak NY, Simirenko OE, et al. Comparison of microarray and SAGE techniques in gene expression analysis of human glioblastoma. Cytology and Genetics. 2007; 41: 30-48. de Bont JM, Kros JM, Passier MMCJ, Reddingius RE, Smitt PAES, Luider TM, et al. Differential expression and prognostic significance of SOX genes in pediatric medulloblastoma and ependymoma identified by microarray analysis. NeuroOncology. 2008; 10: 648-60. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J. TM4: A free, open-source system for microarray data management and analysis. Biotechniques. 2003; 34: 374-8. Virginia Goss Tusher, Robert Tibshirani, Gilbert Chu. Significance analysis of microarrays applied to the ionizing radiation response. P Natl Acad Sci USA. 2001; 98: 5116-21. Boon K, Osorio EC, Greenhut SF, Schaefer CF, Shoemaker J, Polyak K, et al. An anatomy of normal and malignant gene expression. P Natl Acad Sci USA. 2002; 99: 11287-92. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protocols. 2008; 4: 44-57. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research. 2009; 37: 1-13.
[27]
[28]
[29]
[30]
[31]
[32] [33]
[34]
[35]
375
Giachino C, Lantelme E, Lanzetti L, Saccone S, DellaValle G, Migone N. A novel SH3-containing human gene family preferentially expressed in the central nervous system. Genomics. 1997; 41: 427-34. Qu S, Yao Y, Shang C, Xue Y, Ma J, Li Z, et al. MicroRNA330 Is an Oncogenic Factor in Glioblastoma Cells by Regulating SH3GL2 Gene. Plos One. 2012; 7: e46010. Ringstad N, Nemoto Y, De Camilli P. The SH3p4/Sh3p8/ SH3p13 protein family: Binding partners for synaptojanin and dynamin via a Grb2-like Src homology 3 domain. Proceedings of the National Academy of Sciences. 1997; 94: 8569-74. Adachi H, Tsujimoto M. FEEL-1, a Novel Scavenger Receptor with in Vitro Bacteria-binding and Angiogenesismodulating Activities. Journal of Biological Chemistry. 2002; 277: 34264-70. Kzhyshkowska J, Mamidi S, Gratchev A, Kremmer E, Schmuttermaier C, Krusell L, et al. Novel stabilin-1 interacting chitinase-like protein (SI-CLP) is up-regulated in alternatively activated macrophages and secreted via lysosomal pathway. Blood. 2006; 107: 3221-8. Hanahan D, Weinberg Robert A. Hallmarks of Cancer: The Next Generation. Cell. 2011; 144: 646-74. Bogler O, Mikkelsen T. Angiogenesis in glioma: Molecular mechanisms and roadblocks to translation. Cancer J. 2003; 9: 205-13. Bögler O, Mikkelsen T. Angiogenesis and apoptosis in glioma: Two arenas for promising new therapies. Journal of Cellular Biochemistry. 2005; 96: 16-24. Weller Michael MD; Stupp Roger MD; Hegi Monika PhD; Wick Wolfgang MD. Individualized Targeted Therapy for Glioblastoma: Fact or Fiction? Journal of Cellular Biochemistry. Cancer J. 2012; 18: 40-44.
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Cancer Biomarkers 13 (2013) 367–375 DOI 10.3233/CBM-130367 IOS Press
Identification of novel human glioblastoma-specific transcripts by serial analysis of gene expression data mining Yanlin Sua , Jie Xionga , Zhitong Bingb , Xiaomin Zengc, Yong Zhanga, Xiaohua Fua and Xiaoning Penga,∗ a
Department of Internal Medicine, College of Medicine, Hunan Normal University, Changsha, Hunan, China Institute of Modern Physics of Chinese Academy of Sciences, Lanzhou, Gansu, China c Department of Statistics and Epidemiology, Public Health School, Central South University, Changsha, Hunan, China b
Abstract. BACKGROUND: Glioblastoma multiforme (GBM) remains the most common and aggressive primary brain tumor in adults with a poor median survival, and molecular biomarkers for GBM pathogenesis are in need. PURPOSE: The objective of this study is to identify potential novel genes for GBM pathogenesis by gene expression data mining. MATERIALS AND METHODS: Available SAGE libraries of GBM, astrocytoma, and normal brain tissues were collected from the Cancer Genome Anatomy Project (CGAP). Significance analysis for microarray (SAM) and CGAP-SAGE-Genie-DGED were used to identify differentially expressed tags, and specific tags that were differentially expressed only in GBM were further selected. Tags to genes association was performed by CGAP-SAGE-Genie-SAV. Immunohistochemistry was used to investigate distribution and validate expression of the interested gene. RESULTS: Three genes were significantly differentially expressed just in brain. up-regulated expression of STAB1 and downregulated expression of SH3GL2 and DNM3. Immunohistochemistry assay indicated that STAB1 mainly expressed in vascular endothelial cells and over-expressed in GBM samples compared to normal samples. CONCLUSIONS: Our study shows that data mining of public sources of gene expression is an effective way to identify novel tumor-associated genes, and this work may contribute to the identification of candidate genes for GBM angiogenesis. Keywords: GBM, astrocytoma, SAGE, specific gene, STAB1, angiogenesis
1. Introduction A glioma is a type of tumor that arises from neuroepithelial tissue. The most common site of gliomas is the brain [1]. Gliomas are named according to the specific cell type they share histological features with, but this is not necessarily associated with the originating ∗ Corresponding author: Xiaoning Peng, Mailing address: No.371, Tongzipo Road, Changsha, Hunan, China. Tel.: +86 731 88912598; Fax: +86 731 88912417; E-mail: [email protected].
cell. The main types of gliomas are: astrocytic tumors, ependymal tumors, oligodendroglial tumors, oligoastrocytic tumors according to the World Health Organization (WHO classification of central nervous system tumors) [2]. A glioma is defined as a primary brain tumor, accounting for 70% of cancers that begin as brain cells. The most frequent and malignant histological type in the adult population is glioblastoma multiforme (GBM), which accounts for 12–15% of all brain tumors and 50–60% of astrocytomas [3–5]. Prognosis for GBM patients remains very poor because it is difficult to treat. Complete removal
c 2013 – IOS Press and the authors. All rights reserved ISSN 1574-0153/13/$27.50
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by surgery is complicated by poor differentiation, rapid proliferation and strong invasion phenotype; chemotherapy and radiation therapy are not highly specific, killing both tumor and healthy cells, and may cause serious adverse reactions. Available information indicates that the median survival time for GBM patients is 12–18 months with comprehensive treatment combined with surgery, radiation, and the chemotherapy drug temozolomide [2,6,7]. A meta-analysis of 12 randomized clinical trials indicates that the overall survival rate of patients with GBM was 40% for one year, and only slightly higher (46%) after the addition of adjuvant therapies [8]. Although the incidence rate of GBM is relatively low (5–8/100,000) and only accounts for 2% of all cancers, GBM is the second leading cause of death for people less than 34 years of age and third for 35–54 years of age [2]. GBM remains a treatment-refractory disease in the field of neurosurgery, making it a considerable public health issue. Effective means of reducing GBM mortality are early detection and novel therapies to specific molecular targets. Currently, the molecular pathology of GBM and its correlation to behavior are not well known; many molecular aberrations detected in GBM may have diagnostic and/or prognostic implications. Amplification of oncogenes such as the epidermal growth factor receptor (EGFR) [5,9–11], as well as loss of tumor suppressors like PTEN, p53, or p16/INK4A are some of the most common genetic alterations in GBM [12–14]. It was recently reported that isocitrate dehydrogenase (IDH1) mutations and MGMT hypermethylation are associated with the prognosis of GBM [15–17]. However, this does not justify designation as GBM bio-marker for most of molecular changes because biomarkers should provide unique diagnostic, prognostic, or predictive information exceeding that provided by histological classification. Many oncogenes and tumor suppressor genes are involved in GBM development, so it is important to understand the differential gene expression between tumor and normal tissue. These data will help clarify the molecular mechanisms involved in GBM development, and could lead to the identification of new markers for GBM diagnosis and new treatment targets. Serial analysis of gene expression (SAGE) is a powerful tool that allows the analysis of overall gene expression patterns with digital analysis. Because SAGE does not require a preexisting clone, it can be used to identify and quantitate new and known genes [8–10]. This technique has been widely used in human studies, and various SAGE tags/SAGE libraries have been generated from differ-
ent cells and tissues [11]. The NCI’s Cancer Genome Anatomy Project (CGAP) currently provides SAGE library data sets from a variety of sources for tumor cells/tissues and corresponding normal controls online. In silico comparison of these SAGE libraries from diverse tissues can be used to discover differentially expressed transcripts, which has led to the identification of several novel and known cancer-related genes, such as MIA and MMP in gastric cancer, MIC-1, DMBTI, Neugrin74, CD74, CXCL, CEBI, Kallikrein6 in pancreatic cancer and HOXB13 in prostate cancer [12– 19]. These studies show that analysis of SAGE data is an effective method for identifying tumor-related genes and markers. Here we compare thirty-nine SAGE library datasets derived from normal brain and glioma tissues to screen for differentially expressed genes between GBM and normal brain tissue. We conclude that combined, multiple high-throughput analyses is an effective data mining strategy for cancer gene identification. This approach may improve the use of publicly available genomic data through strategic data mining of highthroughput analysis.
2. Methods 2.1. SAGE data collection Thirty-nine libraries (9 normal libraries, 30 tumor libraries) were found by SAGE Genie retrieval. Only short tag libraries with 10 bp tags and the same restriction enzymes (BsmF1 and NIaIII) were included in this study. GBM samples from children were omitted because GBM mainly occurs in adults and the tag number of children libraries may lead to outliers. GSM383776 library originated from non-glial cells, and was also omitted in this study [20]. The final normal sample set consisted of 5 libraries (GSM383772-GSM383775 and GSM383779), and the tumor sample set included 9 GBM libraries(GSM383720-GSM383729) and twenty astrocytoma (grade I–III) libraries (GSM383687-383 706). All libraries were produced by the lab of Gregory Riggins, Duke University Medical Center, USA. A database containing 1,066,151 different tags was generated from libraries that contain between 10,115 and 42,250 unique tags.
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2.2. Analysis of differentially expressed tags Selection was performed to reduce noise from the large number of collected tags. The criteria for selection were: i) “tags found in all normal libraries” vs. “tags found in all GBM libraries”; ii) “tags found in all normal libraries” vs. “tags found in all astrocytoma libraries”; iii) “tags found in all GBM libraries” vs. “tags found in all astrocytoma libraries”. The online tool CGAP-SAGE-Genie-DGED was used to compare the average number of tags in these three pools under different combinations. 2.3. Selection for GBM-specific tags GBM-specific tags were selected by the criterion that differentially expressed tags should be only found in GBM libraries [21]. Multi-Experiment Viewer [22] (MeV) was used to perform the Significance Analysis for Microarray [23] (SAM) to confirm the differential expression of specific tags. The association of tags to genes was performed by SAGE Genie SAV (http:// www.ncbi.nlm.nih.gov/SAGE/AnatomicViewer) [24] and a network spider complied by the Java programming language was applied. DAVID (http://david.abcc. ncifcrf.gov/) was applied to predict functional classes of annotated genes [25,26].
Fig. 1. Venn diagram indicating the number of differentially expressed tags from the comparisons of GBM, Astrocytoma and Normal libraries. All tags that were differentially expressed with a fold value > 3.0 and Q-value < 0.05 were used to create a Venn diagram: 248 tags were most discriminative for GBM, being differentially expressed both between GBM libraries and normal libraries and between GBM libraries and Astrocytoma libraries; 329 tags were differentially expressed in both GBM libraries and Astrocytoma libraries when compared with normal libraries, but they were not found to discriminate between GBM libraries and Astrocytoma libraries; 40 tags were differentially expressed among three pools.
2.4. Virtual northern Virtual northern blot analysis enables researchers to view the expression of a specific gene in all EST and SAGE libraries. In the CGAP database. The Gene Finder tool (http://cgap.nci.nih.gov/Genes/GeneFind er) was applied to check gene expression across all available libraries classified according to their tissue origins. 2.5. Cross-validation The leave-one-out cross-validation method was used to validate the newly identified specific genes in GBM library GSM383726. In this study, tumor samples and normal samples are mixed to construct a tumor pool and normal pool, respectively. GSM383726 consisted of five pooled Duke GBM primary tumors and was suitable for leave-one-out cross-validation. 2.6. Immunohistochemical assay of STAB1 A total of 16 unifocal, primary GBM surgically resected from patients at Second people’s hospital of hu-
nan province from 2003 to 2013 were used in this study. We excluded patients received neoadjuvant or adjuvant chemotherapy and/or radiotherapy, and patients with a history of concurrent malignant disease or other previous primary cancers. There were no other minor participants in our study. The anonymity of all patients was maintained, and all specimens were analyzed in a blinded manner. Tissue specimens were fixed promptly with 100 g/ L formaldehyde solution, embedded in paraffin and cut into 3 µm sections. Two-Step method was used to detect the expression of STAB1. The rabbit polyclonal antibody against human STAB1 was purchased from Santa Cruz Biotechnology Company, USA (SC98788). NovoLinkTM Polymer Detection System (RE 7290-K) Novocastra Laboratories, Newcastle, UK. These systems can be used for the visualization of mouse IgG, mouse IgM and rabbit IgG primary antibodies. These detection systems contain Peroxidase Block, Protein Block, Post Primary Block, Novolink Polymer, DAB Chromogen, Novolink DAB Substrate Buffer (Polymer) and Hematoxylin.
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Fig. 2. Significance Analysis for Microarray of 162 specific tags between GBM and normal SAGE libraries. Left down tail which shown in green color represent the significant tags with higher expression in the normal libraries and the other tail with red color represent the significant tags with higher expression in the GBM libraries. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/CBM-130367)
All sections were deparaffinnized and dehydrated with graded alcohol, and washed in phosphate buffered saline (PBS, pH 7.4) Appropriate endogenous peroxidase blocking was conducted before histochemical processing using the Novocastra Peroxidase Block RE7101. Antigen retrieval was performed by treating the slides in citrate buffer in a microwave for 15 min. The sections were cooled for 20 min at room temperature, and then been washed with distilledwater for 3 × 5 min. The slides were treated with Novocastra Protein Block( RE7102) to minimize non-specific staining (background) .The slides were incubated in a moist chamber with STAB1 rabbit polyclonal antibody (1:300) at 37◦ C for 2 hours. Liver, placenta samples were used as positive controls, PBS was used to substitute the primary antibody as a negative controls. After a complete wash in PBS,the slides were treated with the Novolink Polymer (RE7200-K) for 25 min at 37◦ C. After washed in PBS completely, the slides were developed in 0.05% freshly prepared diaminobenzedine solution (DAB) for 10 min, and then counterstained with hematoxylin. Finally, slides were dehydrated in ascending concentrations of ethanol, airdried, and mounted. Positive staining with STAB1 was
defined as brown staining of endothelial cell cytoplasm. 3. Results 3.1. Differentially expressed SAGE tags identified by DGED Gene expression profiles were collected from 8 GBM libraries, 20 astrocytoma (grade I–III) libraries, and 5 control normal brain libraries. CGAP-SAGEGenie-DGED was used to compare the average number of tags between pairwise pools. A difference threshold of greater than 3-fold change and a Q-value < 0.05 is considered significantly differentially expressed. There were 1961 differentially expressed tags between GBM and normal libraries, 1452 differentially expressed tags between astrocytoma libraries and normal libraries, and 728 tags differentially expressed between GBM and astrocytoma libraries. 3.2. GBM-specific tags Two hundred and forty-eight specific tags that were most discriminative for GBM were filtered out by
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Table 1 Specific tags that associated to genes Tag TGGAGTGAAA TACCTATTGT TCACCAAAAA GAAAGTCTCT AAATGGTTCT ATGACCACTG CCCTTCCTTT GATTAAGTGA GCCCTGTATT GGAGGGGACA TGTTTGGGGG CGGGGAGATG TGCCTCTGCG TGTCCACACA ACCAAAAACC AGTGGTGGCT ATCAAGAATC ATGTCTTTTC CAGGCCCCAC CCCCCACCTA CCGTGACTCT CGCAAGCTGG CTCCCCTGCC GACCGCAGGA GCCCCCAATA GCCCTTTCTC GTTTATGGAT TAAGAGTCTG TGGAAATGAC
Gene symbol SH3GL2 DNM3 STAB1 NTRK2 CCDC93 B3GAT1 ATP6V1G2 OCIAD2 ZDHHC22 SCG3 GLT25D1 NDRG2 CD151 ANTXR2 COL1A1 FMOD IFI30 IGFBP4 S100A11 PLP2 FSTL1 LMNA CAPG COL4A1 LGALS1 MRC2 MGP ATP9A COL1A1
Protein Expression Virtual northern name state in GBM in brain SH3-domain GRB2-like 2 down 1 dynamin 3 down 1 stabilin 1 up 1 neurotrophic tyrosine kinase, receptor, type 2 down 1 coiled-coil domain containing 93 down 0 beta-1,3-glucuronyltransferase 1 down 0 ATPase, H+ transporting, lysosomal 13kDa, V1 subunit G2 down 0 OCIA domain containing 2 up 0 zinc finger, DHHC-type containing 22 down 0 secretogranin III down 0 glycosyltransferase 25 domain containing 1 up 0 NDRG family member 2 down 0 CD151 molecule (Raph blood group) up 0 anthrax toxin receptor 2 up 0 collagen, type I, alpha 1 up 1 fibromodulin up 1 interferon, gamma-inducible protein 30 up 1 insulin-like growth factor binding protein 4 up 1 S100 calcium binding protein A11 up 1 proteolipid protein 2 (colonic epithelium-enriched) up 1 follistatin-like 1 up 1 lamin A/C up 1 capping protein (actin filament), gelsolin-like up 1 collagen, type IV, alpha 1 up 1 lectin, galactoside-binding, soluble, 1 up 1 mannose receptor, C type 2 up 1 matrix Gla protein up 1 ATPase, class II, type 9A down 1 collagen, type I, alpha 1 up 1
Virtual northern in other organs
kidney, ect kidney, ect prostate eye, ect skin, thyroid lung, ect eye, ect liver, ect mammary gland eye, ect eye, ect kidney, ect eye, ect lung, ect prostate kidney, ect skin lung, ect
In the “Virtual Northern in brain” column “1” indicates tags were differentially expressed in tissue brain while “0” indicates tags were not differentially expressed in brain, in the “Virtual Northern in other organs” column other organs were listed if the tags were differentially expressed.
the criterion for tags that were only differentially expressed in GBM. These tags were found to be differentially expressed by comparing of GBM with normal and with astrocytoma, but not differentially expressed by comparing astrocytoma with normal (Fig. 1). We found 985 tags that were aberrantly expressed in both GBM and Astrocytoma libraries when compared with normal libraries, but the expression of these tags was not significantly different between GBM and astrocytoma libraries (Fig. 1). Thus, these tags are likely related to the presence of a tumor but do not specifically discriminate either GBM or astocytoma. Overall, 40 tags had statistically significant differential expression among three pools (Fig. 1). The vast majority of tags were only present in few libraries; to avoid differentially expressed tags merely caused by seldom, highexpression libraries, 162 GBM specific tags expressed in the majority ( 80%) of libraries were filtered out for the next analysis. Subsequent confirmation of GBM-specific tags was performed by SAM analysis. An expression matrix of 162 specific tags was constructed, and the frequency
of each specific tag was normalized by dividing it by the total number of tags in the corresponding library and multiplying by 200,000 (CGAP normalization format); a log2 transformation was implemented. SAM analysis with suitable delta values (maintaining false discovery rate near 0) was adopted to confirm both the differences when comparing GBM vs. normal and GBM vs. Astrocytoma and the absence of any significant differences when comparing astrocytoma and normal (based on a threshold fold change > 3.0). 75 tags were confirmed to be differentially expressed in GBM vs. normal (Fig. 2); 91 tags were confirmed to be differentially expressed in GBM vs. astrocytoma; the absence of differences between astrocytoma and normal was almost completely confirmed, except for one differentially expressed tag. As expected, clustering heatmaps of differentially expressed tags show a clear separation between GBM and normal (Fig. 3) and GBM and astrocytoma, but no obvious separation between astrocytoma and normal. Finally, an overlapping tags set including 54 tags was generated from 75 confirmed differentially expressed tags between GBM and
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rection was used. The significance level was set at a p-value = 0.05. DAVID analysis showed that 54 specific genes that had significantly increased expression in GBM were related to “negative regulation of cellsubstrate adhesion” (GO:10812), “carbohydrate binding” (GO:30246) and “negative regulation of biological process” (GO:48519), while GO analysis of the down-regulated GBM-specific genes revealed no significant overrepresentation of GO annotations. 3.4. Virtual northern and cross-validation
Fig. 3. Clustering for GBM and Normal SAGE libraries. “N” represents normal brain and “G” represents GBM. The analysis shows a clear separation between GBM and normal with 75 tags which were most discriminative for GBM. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/CBM-130367)
normal, 91 confirmed differentially expressed tags between GBM and astrocytoma and 161 absent differences tags between astrocytoma and normal. 3.3. Tags to genes association The CGAP-SAGE Genie-SAGE Anatomic Viewer (SAV) was used to map specific differentially expressed SAGE tags to genes and a net spider program complied by the Java programming language was applied. Tags represent the 3’ end of known genes and ranks greater than 90% were considered to be successful associations. Ambiguous tags that represent different genes or EST clusters where at least two genes met the above conditions were removed. Among 54 specific differential tags for GBM, 29 tags were successfully assigned to 28 specific genes; 18 genes were up-regulated and 10 genes were downregulated in GBM (Table 1). DAVID was applied to predict the functional classes of annotated genes, and the Benjamini and Hochberg false discovery rate cor-
By analysis of SAGE libraries we found 28 genes specific for GBM. We then adopted Virtual Northern to detect the expression pattern of these genes across tissues. Genes with expression levels in EST and SAGE libraries of GBM that were 3-fold higher compared to normal brain and with a P-value < 0.05 were designated as positive. Fifteen genes were confirmed to be up-regulated in GBM and 3 genes were confirmed to be down-regulated in GBM (Table 1). Most importantly, STAB1, SH3GL2, DNM3 and NTRK2 were only differentially expressed in the brain; STAB1 was up-regulated in GBM, while SH3GL2 and DNM3 were down-regulated. The differential expression pattern of NTRK2 was inconsistent between SAGE DEGE analysis (down-regulated) and Virtual Northern (upregulated, Table 1), thus, NTRK2 was excluded from the list of GBM-specific genes (Table 2). To further validate Virtual Northern results, all other cancer SAGE data in CGAP were compared by SAGEGenie-DGED and the results showed that STAB1, SH3GL2, DNM3 were only differentially expressed in the brain. These genes have potential diagnostic and therapeutic implications in GBM. A comparison between GSM383726 and normal pool was also conducted for cross-validation and STAB1 was confirmed differentially expressed. 3.5. Immunohistochemistry GBM samples were diagnosed by two experienced pathologists. To validate the differentially expressed pattern of STAB1 at the protein level, immunohistochemistry was performed under standard protocol which indicated STAB1 mainly expressed in vascular endothelial cells (Fig. 4A) and differentially expressed between GBM and normal brain (P < 0.0001; Fig. 4B).
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Table 2 Virtual northern of GBM candidate genes
Four genes were only differentially expressed in brain and the differential expression patterns of were consistent with SAGE DEGE analysis except NTRK2.
Fig. 4. Immunohistochemical DAB-hematoxylin staining of STAB1 in GBM (A) and normal brain tissue (B). (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/CBM-130367)
4. Discussion Combining multiple analytical approaches would reduce the possibility of artifacts in identification of differentially expressed genes. In this study, we use SAGE-Genie-DGED, SAM and Virtual Northern approaches to compare gene expression profiles between GBM, normal brain and astrocytoma libraries to identify novel GBM-specific genes. We compared the gene expression profiles of 9 GBM, 20 astrocytomas and 5 normal brain tissues in different combinations. Fifty-four tags that successfully matched to 30 genes most discriminated GBM from other tissues. Among these genes, many transcription factors like OTX2 and EGFR were found to have higher expression in GBM when compared to normal cerebellum. Other important, GBM-associated genes like S100A11 were up-regulated in GBM and also differentially expressed in bone marrow, eye, kidney, liver, lymph node and skin. CAPG was upregulated in GBM, Kidney and Liver and downregulated in Prostate; NDRG2 was up-regulated in
Skin, Kidney and Liver and down-regulated in GBM, Colon and Lung (Table 1). All of these validate earlier findings. To the best of our knowledge, STAB1 and DNM3 have never been reported in GBM, while SH3GL2 was newly reported as a GBM suppressor, consistent with our analysis. SH3 domain-containing GRB2-like protein 2 (SH3 GL2) is implicated in synaptic vesicle endocytosis. SH3GL2 is mainly distributed in the central nervous system, particularly enriched in the presynaptic ganglion [27]. A recent study showed that SH3GL2 was a target of miR-330 that plays an oncogenic role in human GBM [28]. SH3GL2 is a candidate tumor suppressor gene and particularly highly expressed in the central nervous system [29]. From previous studies and our findings, we inferred that SH3GL2 may be a marker of GBM. Dynamin-3 (DNM3) is involved in producing bundles and is able to bind and hydrolyze GTP; its role in cancer is currently unclear. STAB1 is the only up-regulated GBM-specific gene found by this study, and its differential expression pattern in GBM was confirmed by the live-one-out val-
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idation and immunohistochemistry. STAB1 acts as a scavenger receptor for acetylated low density lipoprotein, and is also involved in angiogenesis and cell-cell interactions [30,31]. Previous studies have proposed that angiogenesis, invasion and metastasis were hallmarks of cancer [32]. It is possible that STAB1 can modulate the tube formation by interacting with various growth factors [30]. To the best of our knowledge, GBM has one of the highest rates of new blood vessel formation and require angiogenesis for transformation, proliferation, migration and survival [33]. Overexpression of STAB1 in vascular endothelial cells is observed in GBM by immunohistochemistry, which indicates that STAB1 may have a pivotal role in malignant GBM. At present, the use anti-angiogenic therapy for inhibiting the growth of GBM is widely accepted [34] and inhibitors of angiogenesis are currently the focus of drug development in GBM [35]. Although some known important genes like VEGF and IL-6 were associated with angiogenesis, these genes were found to be significantly changed in GBM vs. normal and astrocytoma vs normal but absent differences in GBM vs. astrocytoma, which implies VEGF and IL-6 may be common angiogenic factors in glioma and STAB1 maybe angiogenic accelerator in the most malignant glioma. After comparison of these data and validation by immunohistochemistry, our results indicated that STAB1 may be a vital factor for GBM through promoting angiogenesis, and this mechanism warrants further study. In summary, this work confirms that public SAGE databases can be used to effectively identify genes that are differentially expressed between tumors or tissues. Three potential markers of GBM were identified, and STAB1 may be a potential novel factor involved in rapid angiogenesis in GBM.
Acknowledgments We thank Gregory Riggins’s laboratory of Duke University Medical Center for generating the data and made data public. We also thank rongrong zhang of Second people’s hospital of hunan province for assisting us to achieve immunohistochemistry. This work was supported by grants from the National Natural Foundation of China (No. 81071628), Natural Science foundation of Hunan grants (No. 11JJ2013) and grants from the Foundation for Young Talents of Gansu (1208RJYA013).
References [1]
Mamelak AN, Jacoby DB. Targeted delivery of antitumoral therapy to glioma and other malignancies with synthetic chlorotoxin (TM-601). Expert Opinion on Drug Delivery. 2007; 4: 175-86. [2] Louis D, Ohgaki H, Wiestler O, Cavenee W, Burger P, Jouvet A, et al. The 2007 WHO Classification of Tumours of the Central Nervous System. Acta Neuropathol. 2007; 114: 97109. [3] Jeuken JM, Deimling A, Wesseling P. Molecular pathogenesis of oligodendroglial tumors. J Neurooncol. 2004; 70: 161-81. [4] Hegi ME, Diserens A-C, Gorlia T, Hamou M-F, de Tribolet N, Weller M, et al. MGMT Gene Silencing and Benefit from Temozolomide in Glioblastoma. New Engl J Med. 2005; 352: 997-1003. [5] Parsons DW, Jones S, Zhang X, Lin JC-H, Leary RJ, Angenendt P, et al. An Integrated Genomic Analysis of Human Glioblastoma Multiforme. Science. 2008; 321: 1807-12. [6] Pfister S, Janzarik WG, Remke M, Ernst A, Werft W, Becker N, et al. BRAF gene duplication constitutes a mechanism of MAPK pathway activation in low-grade astrocytomas. The Journal of Clinical Investigation. 2008; 118: 1739-49. [7] Riemenschneider M, Jeuken JM, Wesseling P, Reifenberger G. Molecular diagnostics of gliomas: state of the art. Acta Neuropathol. 2010; 120: 567-84. [8] Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial Analysis of Gene Expression. Science. 1995; 270: 484-7. [9] Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett Jr DE, et al. Characterization of the Yeast Transcriptome. Cell. 1997; 88: 243-51. [10] Murray D, Doran P, MacMathuna P, Moss A. In silico gene expression analysis – an overview. Molecular Cancer. 2007; 6: 50. [11] Tuteja R, Tuteja N. Serial Analysis of Gene Expression: Applications in Human Studies. Journal of Biomedicine and Biotechnology. 2004; 2004: 113-20. [12] El-Meanawy MA, Barathan S, Hayden PS, Iyengar SK, Schelling JR, Sedor JR. Serial analysis of gene expression. Renal Disease: Techniques and Protocols. 2003; 86: 257-74. [13] Lauriola M, Ugolini G, Rosati G, Zanotti S, Montroni I, Manaresi A, et al. Identification by a Digital Gene Expression Displayer (DGED) and test by RT-PCR analysis of new mRNA candidate markers for colorectal cancer in peripheral blood. International Journal of Oncology. 2010; 37: 519-25. [14] Ossandon F, Villarroel C, Aguayo F, Santibanez E, Oue N, Yasui W, et al. In Silico analysis of Gastric carcinoma Serial Analysis of Gene Expression libraries reveals different profiles associated with ethnicity. Molecular Cancer. 2008; 7: 22. [15] Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Church DM, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Research. 2005; 33: D39-D45. [16] Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, et al. NCBI GEO: mining tens of millions of expression profiles – database and tools update. Nucleic Acids Research. 2007; 35: D760-D5. [17] Aung PP, Oue N, Mitani Y, Nakayama H, Yoshida K, Noguchi T, et al. Systematic search for gastric cancer-specific genes based on SAGE data: melanoma inhibitory activity and matrix metalloproteinase-10 are novel prognostic factors in patients with gastric cancer. Oncogene. 2006; 25: 2546-57. [18] Hustinx SR, Cao DF, Maitra A, Sato N, Martin ST, Sudhir D, et al. Differentially expressed genes in pancreatic ductal ade-
Y. Su et al. / Identification of novel human glioblastoma-specific transcripts by gene expression data mining
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
nocarcinomas identified through serial analysis of gene expression. Cancer Biology & Therapy. 2004; 3: 1254-61. Edwards S, Campbell C, Flohr P, Shipley J, Giddings I, tePoele R, et al. Expression analysis onto microarrays of randomly selected cDNA clones highlights HOXB13 as a marker of human prostate cancer. British Journal of Cancer. 2005; 92: 376-81. Kavsan VA, Dmitrenko VV, Shostak KO, Bukreieva TV, Vitak NY, Simirenko OE, et al. Comparison of microarray and SAGE techniques in gene expression analysis of human glioblastoma. Cytology and Genetics. 2007; 41: 30-48. de Bont JM, Kros JM, Passier MMCJ, Reddingius RE, Smitt PAES, Luider TM, et al. Differential expression and prognostic significance of SOX genes in pediatric medulloblastoma and ependymoma identified by microarray analysis. NeuroOncology. 2008; 10: 648-60. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J. TM4: A free, open-source system for microarray data management and analysis. Biotechniques. 2003; 34: 374-8. Virginia Goss Tusher, Robert Tibshirani, Gilbert Chu. Significance analysis of microarrays applied to the ionizing radiation response. P Natl Acad Sci USA. 2001; 98: 5116-21. Boon K, Osorio EC, Greenhut SF, Schaefer CF, Shoemaker J, Polyak K, et al. An anatomy of normal and malignant gene expression. P Natl Acad Sci USA. 2002; 99: 11287-92. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protocols. 2008; 4: 44-57. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research. 2009; 37: 1-13.
[27]
[28]
[29]
[30]
[31]
[32] [33]
[34]
[35]
375
Giachino C, Lantelme E, Lanzetti L, Saccone S, DellaValle G, Migone N. A novel SH3-containing human gene family preferentially expressed in the central nervous system. Genomics. 1997; 41: 427-34. Qu S, Yao Y, Shang C, Xue Y, Ma J, Li Z, et al. MicroRNA330 Is an Oncogenic Factor in Glioblastoma Cells by Regulating SH3GL2 Gene. Plos One. 2012; 7: e46010. Ringstad N, Nemoto Y, De Camilli P. The SH3p4/Sh3p8/ SH3p13 protein family: Binding partners for synaptojanin and dynamin via a Grb2-like Src homology 3 domain. Proceedings of the National Academy of Sciences. 1997; 94: 8569-74. Adachi H, Tsujimoto M. FEEL-1, a Novel Scavenger Receptor with in Vitro Bacteria-binding and Angiogenesismodulating Activities. Journal of Biological Chemistry. 2002; 277: 34264-70. Kzhyshkowska J, Mamidi S, Gratchev A, Kremmer E, Schmuttermaier C, Krusell L, et al. Novel stabilin-1 interacting chitinase-like protein (SI-CLP) is up-regulated in alternatively activated macrophages and secreted via lysosomal pathway. Blood. 2006; 107: 3221-8. Hanahan D, Weinberg Robert A. Hallmarks of Cancer: The Next Generation. Cell. 2011; 144: 646-74. Bogler O, Mikkelsen T. Angiogenesis in glioma: Molecular mechanisms and roadblocks to translation. Cancer J. 2003; 9: 205-13. Bögler O, Mikkelsen T. Angiogenesis and apoptosis in glioma: Two arenas for promising new therapies. Journal of Cellular Biochemistry. 2005; 96: 16-24. Weller Michael MD; Stupp Roger MD; Hegi Monika PhD; Wick Wolfgang MD. Individualized Targeted Therapy for Glioblastoma: Fact or Fiction? Journal of Cellular Biochemistry. Cancer J. 2012; 18: 40-44.
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