Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2018  |  Volume : 14  |  Issue : 8  |  Page : 243-247

Identification of key genes related to high-risk gastrointestinal stromal tumors using bioinformatics analysis


1 Department of Anesthesia, Jinan Central Hospital Affiliated to Shandong University, Jinan, 250013, Shandong Province, China
2 Department of Gastroenterology, Chinese PLA General Hospital, Beijing, 100853, China

Date of Web Publication26-Mar-2018

Correspondence Address:
Shuan Jin
Department of Anesthesia, Jinan Central Hospital Affiliated to Shandong University, 105# Jiefang Road, Jinan, 250013, Shandong Province
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.207068

Rights and Permissions
 > Abstract 


Aim: The purpose of this study was to identify predictive biomarkers used for clinical therapy and prognostic evaluation of high-risk gastrointestinal stromal tumors (GISTs).
Materials and Methods: In this study, microarray data GSE31802 were used to identify differentially expressed genes (DEGs) between high-risk GISTs and low-risk GISTs. Then, enrichment analysis of DEGs was conducted based on the gene ontology and kyoto encyclopedia of genes and genomes pathway database. In addition, the transcription factors and cancer-related genes in DEGs were screened according to the TRANSFAC, TSGene, and TAG database. Finally, protein–protein interaction (PPI) network was constructed and analyzed to look for critical genes involved in high-risk GISTs.
Results: A total of forty DEGs were obtained and these genes were mainly involved in four pathways, including melanogenesis, neuroactive ligand-receptor interaction, malaria, and hematopoietic cell lineage. The enriched biological processes were related to the regulation of insulin secretion, integrin activation, and neuropeptide signaling pathway. Transcription factor analysis of DEGs indicated that POU domain, class 2, associating factor 1 (POU2AF1) was significantly downregulated in high-risk GISTs. By constructing the PPI network of DEGs, ten genes with high degrees formed local networks, such as PNOC, P2RY14, and SELP.
Conclusions: Four genes as POU2AF1, PNOC, P2RY14, and SELP might be used as biomarkers for prognosis of high-risk GISTs.

Keywords: Enrichment analysis, gastrointestinal stromal tumors, genes, protein–protein interaction network


How to cite this article:
Jin S, Zhu W, Li J. Identification of key genes related to high-risk gastrointestinal stromal tumors using bioinformatics analysis. J Can Res Ther 2018;14, Suppl S1:243-7

How to cite this URL:
Jin S, Zhu W, Li J. Identification of key genes related to high-risk gastrointestinal stromal tumors using bioinformatics analysis. J Can Res Ther [serial online] 2018 [cited 2019 Aug 18];14:243-7. Available from: http://www.cancerjournal.net/text.asp?2018/14/8/243/207068




 > Introduction Top


Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms, which arise throughout the entire gastrointestinal tract [1],[2] and probably originate from interstitial cells of Cajal or their precursor cells.[3] They usually occur in the stomach (50%–60%) and the small intestine (30%–35%), but less frequently arise in the colon and rectum (5%) and the esophagus (<1%).[4] It has been reported that more than 80% patients with GISTs are older than 50 years (median 63 years) despite GISTs can arise at any age.[4],[5]

Complete surgical resection is the primary treatment for GISTs. However, it is not curative for high-risk GISTs due to the high recurrence rate.[6],[7] The risk of relapse is usually estimated based on mitotic rate, tumor size, and tumor site. According to the risk definition system proposed by Fletcher et al.,[8] mitotic rate and tumor size are leading indicators in the risk stratification of tumors, and GISTs are divided into four risk grades: very low risk, low risk, intermediate risk, and high risk. This classification is extended by adding tumor site into the estimation criteria in 2006.[9] Thus, mitotic rate, tumor size, and localization are the key factors used for distinguishing benign from malignant GISTs or at least estimating malignant potential. However, molecular markers are more objective and accurate for identifying different risk grades of GISTs. Most (75%–80%) GISTs have mutations of KIT proto-oncogene, and one-third of GISTs without KIT mutation carry exclusive mutations in the PDGFRA gene.[10],[11],[12] Although the two genes can serve as crucial diagnostic of GISTs, they do not fully explain the progression of malignant GISTs. Therefore, other potential markers are required for the diagnosis and therapy of high-risk GISTs.

In this study, the gene expression profiles GSE31802 were downloaded to screen differentially expressed genes (DEGs) between high-risk GISTs and low-risk GISTs. Then, the gene ontology (GO) enrichment analysis and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were performed to study the biological process (BP) Function and pathways related with DEGs. Moreover, analysis of transcription factors and cancer-related genes in DEGs were conducted. Finally, protein–protein interaction (PPI) network was constructed by cytoscape to find key genes involved in high-risk GISTs.


 > Materials and Methods Top


Affymetrix microarray data

The raw microarray data GSE31802 were downloaded from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), which was deposited by Niinuma et al.[13] A total of 11 chips were available for analysis in our study, including 8 chips of high-risk GIST specimens and 3 chips of low-risk GIST specimens from surgical resections. The GPL4133 Agilent-014850 Whole Human Genome Microarray 4 × 44K G4112F was used as microarray platform.

Data preprocessing and differentially expressed genes screening

The raw data were preprocessed utilizing limma package in bioconductor [14] and affy annotation files from BrainArray Laboratory.[15] Background correction, quantile normalization, and probe summarization of the microarray data were performed by robust multiarray average algorithm [16] to obtain the gene expression matrix.

The Bayesian method was used to identify genes that were significantly, differentially expressed between high-risk GISTs and low-risk GISTs based on limma package. The genes with the cutoff criteria of |log2 FC| >1 and false discovery rate (FDR) <0.05 were selected as DEGs.

Enrichment analysis of differentially expressed genes

The Database for Annotation, Visualization and Integrated Discovery (DAVID)[17] is a comprehensive set of functional annotation tools used for GO enrichment analysis.[18] Based on DAVID online program, GO-BP functional analysis of DEGs was performed for DEGs in this study. KEGG pathway database,[19] which contains graphical representations of cellular processes, was applied to identify the main functional and metabolic pathways of DEGs. P < 0.05 was chosen as the cutoff criterion for both GO and KEGG enrichment analysis.

Functional annotation of differentially expressed genes

To study the transcriptional regulation function of DEGs, the screened genes were analyzed according to the TRANSFAC transcription factor database.[20] Furthermore, we extracted all the proto-oncogenes and cancer suppressor genes in DEGs based on TSGene [21] and TAG database.[22]

Construction of protein–protein interaction network

DEGs were submitted to STRING version 9.1 (http://string-db.org)[23] to search interaction relationships of the proteins, and the confidence score >0.9 was used as the cutoff criterion. Then, the cytoscape [24] was applied to construct the PPI network based on the protein interactions. Finally, the hub nodes of PPI were obtained by connectivity degree analysis in our study.


 > Results Top


Differentially expressed genes analysis

The Bayesian method was used to screen DEGs between high-risk GISTs and low-risk GISTs. A total of 40 DEGs with |log2 FC| >1 and FDR < 0.05 were identified, including 4 upregulated DEGs and 36 downregulated DEGs.

Enrichment and functional analysis of differentially expressed genes

GO and KEGG pathway enrichment analysis were performed for both the upregulated and downregulated DEGs. The GO analysis results showed that upregulated DEGs were not enriched in any BP. The GO terms in BP category of downregulated DEGs included regulation of insulin secretion, integrin activation, and neuropeptide signaling pathway as shown in [Table 1]. The results of KEGG pathway enrichment analysis indicated that upregulated DEGs were enriched in melanogenesis while downregulated DEGs were mainly enriched in three pathways: neuroactive ligand-receptor interaction, malaria, and hematopoietic cell lineage [Table 2].
Table 1: The enriched gene ontology terms of downregulated differentially expressed genes

Click here to view
Table 2: The enriched kyoto encyclopedia of genes and genomes pathway of differentially expressed genes

Click here to view


By analyzing the transcription factors and tumor-associated genes in DEGs, we found that only transcription factor POU domain, class 2, associating factor 1 (POU2AF1) was significantly downregulated in high-risk GISTs.

Construction of protein–protein interaction network

To systemically analyze the functions of DEGs in GISTs samples, the PPI network of DEGs was constructed using cytoscape in accordance with protein interactions. From [Figure 1], we discovered that the DEGs of PNOC (degree = 51), P2RY14 (degree = 48), KCNA5 (degree = 43), GPC5 (degree = 26), BLK (degree = 26), PTGIR (degree = 26), FZD8 (degree = 20), SELP (degree = 17), PDE1B (degree = 13), and NDST4 (degree = 10) formed local networks with high degrees. However, the degrees of other proteins were <10.
Figure 1: Protein–protein interaction network of differentially expressed genes. Red nodes represent upregulated differentially expressed genes; green nodes represent downregulated differentially expressed genes; yellow nodes represent normal genes

Click here to view



 > Discussion Top


In this study, we used bioinformatics methods to investigate the molecular mechanism of high-risk GISTs and find potential biomarkers for clinical therapy and prognostic evaluation of malignant GISTs. The results showed that the expression of forty genes was altered in high-risk GISTs compared with low-risk GISTs. These genes were mainly enriched in insulin secretion, integrin activation, and neuropeptide signaling pathway by GO analysis. Pathways of melanogenesis, neuroactive ligand-receptor interaction, malaria, and hematopoietic cell lineage were involved in high-risk GISTs. By transcription factor analysis of DEGs, only POU2AF1 was significantly downregulated in high-risk GISTs. In addition, genes with high degrees were obtained according to the PPI network, such as PNOC, P2RY14 and SELP.

The previous studies indicate that metastasis often arises in malignant tumors, such as colorectal cancer,[25] breast cancer,[26] and GISTs.[27] It has been reported that about 40% of localized GISTs will give rise to metastases.[4] Metastases of GISTs have a predilection to the liver, peritoneum, and other intra-abdominal sites.[28] Our study showed that integrin activation was one of the main enriched BPs involved in high-risk GISTs. The research of Felding-Habermann et al.[29] suggested that integrin activation controlled breast cancer metastasis. Integrins can activate focal adhesion kinase (FAK) which is an important molecule signal for tumor progression and metastasis.[30],[31],[32] Reports showed that FAK was activated by integrins in GISTs with imatinib treatment and inhibition of FAK was used as a potential therapeutic strategy for imatinib-resistant GISTs.[33],[34] Therefore, integrin activation might be closely associated with GISTs. These evidence also demonstrated that the analysis of our study was reliable.

POU2AF1 that constitutively expressed in B lymphocytes is a B-cell-specific transcriptional coactivator.[35] The functions of POU2AF1 in B cell proliferation and differentiation have been extensively studied.[36],[37],[38] Moreover, POU2AF1 plays an important role in malignancy, such as lymphocytic leukemia,[39] T-cell neoplasms,[40] and multiple myeloma.[41] Experimental results showed that the activation of POU2AF1 contributed to the progression of multiple myeloma by directly regulating expression of TNFRSF17 (B-cell maturation factor).[42] Consequently, we speculated that POU2AF1 might be a crucial transcription factor involved in the progression of GISTs.

The results of PPI network indicated that PNOC, P2RY14, and SELP were the key nodes with high degrees. PNOC (prepronociceptin) is the precursor protein of nociceptin and has been proved to be associated with neuroblastoma.[43] Zaveri et al.[44] revealed that the mRNA expression of PNOC was upregulated in the NS20Y neuroblastoma cells, and PNOC was closely related to cellular differentiation. However, there was no report about the function of PNOC in GISTs. Further study is needed to demonstrate the relationship between PNOC and GISTs. P2RY14 belongs to the family of G-protein coupled receptors for UDP-glucose and related sugar nucleotides.[45],[46] It has been showed that P2RY14 can trigger innate mucosal immune responses in human and mouse female reproductive tract.[47] In 2012, Wu et al.[48] discovered that P2RY14 might be one of the lung adenocarcinoma marker genes by bioinformatics analysis, but lack of experimental evidence. P2RY14 was one of the important genes that might be related to GISTs in our study; further experiments were needed as well. SELP (P-selectin, CD62, GMP 140) is a member of the selectin family of adhesion receptors that may be involved in tumor metastasis.[49] Early researches have shown that SELP has an essential role in different tumors, such as breast cancer, neuroblastoma, and lung cancer.[50],[51] Recent studies indicated that SELP could be used to explore the effective treatment of lung cancer.[52],[53] However, SELP has not been reported to be involved in GISTs. In our study, SELP was found to interact with CD34 based on PPI network. It has been proved that CD34 is expressed in roughly 70% of GISTs [54] and can be used as a biomarker for the diagnosis of GISTs.[55],[56] Therefore, SELP might play an important role in GISTs.

In summary, the genes identified in this study were potential biomarkers of high-risk GISTs, which might contribute to the therapy and prognostic evaluation of this disease. However, these genes were only analyzed by bioinformatics methods. Experiments are still needed to validate the relationship between the genes and GISTs.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
 > References Top

1.
Joensuu H, Hohenberger P, Corless CL. Gastrointestinal stromal tumour. Lancet 2013;382:973-83.  Back to cited text no. 1
[PUBMED]    
2.
Beham AW, Schaefer IM, Schüler P, Cameron S, Ghadimi BM. Gastrointestinal stromal tumors. Int J Colorectal Dis 2012;27:689-700.  Back to cited text no. 2
    
3.
Corless CL, Barnett CM, Heinrich MC. Gastrointestinal stromal tumours: Origin and molecular oncology. Nat Rev Cancer 2011;11:865-78.  Back to cited text no. 3
[PUBMED]    
4.
Joensuu H, Vehtari A, Riihimäki J, Nishida T, Steigen SE, Brabec P, et al. Risk of recurrence of gastrointestinal stromal tumour after surgery: An analysis of pooled population-based cohorts. Lancet Oncol 2012;13:265-74.  Back to cited text no. 4
    
5.
Ducimetière F, Lurkin A, Ranchère-Vince D, Decouvelaere AV, Péoc'h M, Istier L, et al. Incidence of sarcoma histotypes and molecular subtypes in a prospective epidemiological study with central pathology review and molecular testing. PLoS One 2011;6:e20294.  Back to cited text no. 5
    
6.
Casali PG, Blay JY; ESMO/CONTICANET/EUROBONET Consensus Panel of Experts. Gastrointestinal stromal tumours: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 2010;21 Suppl 5:v98-102.  Back to cited text no. 6
[PUBMED]    
7.
Eisenberg BL, Judson I. Surgery and imatinib in the management of GIST: Emerging approaches to adjuvant and neoadjuvant therapy. Ann Surg Oncol 2004;11:465-75.  Back to cited text no. 7
[PUBMED]    
8.
Fletcher CD, Berman JJ, Corless C, Gorstein F, Lasota J, Longley BJ, et al. Diagnosis of gastrointestinal stromal tumors: A consensus approach. Int J Surg Pathol 2002;10:81-9.  Back to cited text no. 8
[PUBMED]    
9.
Miettinen M, Lasota J. Gastrointestinal stromal tumors: Pathology and prognosis at different sites. Semin Diagn Pathol 2006;23:70-83.  Back to cited text no. 9
[PUBMED]    
10.
Hirota S, Isozaki K, Moriyama Y, Hashimoto K, Nishida T, Ishiguro S, et al. Gain-of-function mutations of c-kit in human gastrointestinal stromal tumors. Science 1998;279:577-80.  Back to cited text no. 10
[PUBMED]    
11.
Heinrich MC, Corless CL, Duensing A, McGreevey L, Chen CJ, Joseph N, et al. PDGFRA activating mutations in gastrointestinal stromal tumors. Science 2003;299:708-10.  Back to cited text no. 11
[PUBMED]    
12.
Hirota S, Ohashi A, Nishida T, Isozaki K, Kinoshita K, Shinomura Y, et al. Gain-of-function mutations of platelet-derived growth factor receptor alpha gene in gastrointestinal stromal tumors. Gastroenterology 2003;125:660-7.  Back to cited text no. 12
[PUBMED]    
13.
Niinuma T, Suzuki H, Nojima M, Nosho K, Yamamoto H, Takamaru H, et al. Upregulation of miR-196a and HOTAIR drive malignant character in gastrointestinal stromal tumors. Cancer Res 2012;72:1126-36.  Back to cited text no. 13
[PUBMED]    
14.
Diboun I, Wernisch L, Orengo CA, Koltzenburg M. Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics 2006;7:252.  Back to cited text no. 14
[PUBMED]    
15.
Gautier L, Cope L, Bolstad BM, Irizarry RA. Affy – Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004;20:307-15.  Back to cited text no. 15
[PUBMED]    
16.
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003;4:249-64.  Back to cited text no. 16
[PUBMED]    
17.
Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57.  Back to cited text no. 17
[PUBMED]    
18.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unification of biology. Nat Genet 2000;25:25-9.  Back to cited text no. 18
[PUBMED]    
19.
Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30.  Back to cited text no. 19
[PUBMED]    
20.
Matys V, Fricke E, Geffers R, Gössling E, Haubrock M, Hehl R, et al. TRANSFAC ®: Transcriptional regulation, from patterns to profiles. Nucleic Acids Res 2003;31:374-8.  Back to cited text no. 20
    
21.
Zhao M, Sun J, Zhao Z. TSGene: A web resource for tumor suppressor genes. Nucleic Acids Res 2013;41:D970-6.  Back to cited text no. 21
[PUBMED]    
22.
Chen JS, Hung WS, Chan HH, Tsai SJ, Sun HS. In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma. Bioinformatics 2013;29:420-7.  Back to cited text no. 22
[PUBMED]    
23.
Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013;41:D808-15.  Back to cited text no. 23
[PUBMED]    
24.
Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, et al. A travel guide to cytoscape plugins. Nat Methods 2012;9:1069-76.  Back to cited text no. 24
[PUBMED]    
25.
Zhang YY, Chen B, Ding YQ. Metastasis-associated factors facilitating the progression of colorectal cancer. Asian Pac J Cancer Prev 2012;13:2437-44.  Back to cited text no. 25
[PUBMED]    
26.
Gjerdrum C, Tiron C, Høiby T, Stefansson I, Haugen H, Sandal T, et al. Axl is an essential epithelial-to-mesenchymal transition-induced regulator of breast cancer metastasis and patient survival. Proc Natl Acad Sci U S A 2010;107:1124-9.  Back to cited text no. 26
    
27.
Emile JF, Brahimi S, Coindre JM, Bringuier PP, Monges G, Samb P, et al. Frequencies of KIT and PDGFRA mutations in the MolecGIST prospective population-based study differ from those of advanced GISTs. Med Oncol 2012;29:1765-72.  Back to cited text no. 27
[PUBMED]    
28.
DeMatteo RP, Lewis JJ, Leung D, Mudan SS, Woodruff JM, Brennan MF. Two hundred gastrointestinal stromal tumors: Recurrence patterns and prognostic factors for survival. Ann Surg 2000;231:51-8.  Back to cited text no. 28
[PUBMED]    
29.
Felding-Habermann B, O'Toole TE, Smith JW, Fransvea E, Ruggeri ZM, Ginsberg MH, et al. Integrin activation controls metastasis in human breast cancer. Proc Natl Acad Sci U S A 2001;98:1853-8.  Back to cited text no. 29
    
30.
Mitra SK, Schlaepfer DD. Integrin-regulated FAK-Src signaling in normal and cancer cells. Curr Opin Cell Biol 2006;18:516-23.  Back to cited text no. 30
[PUBMED]    
31.
Gabarra-Niecko V, Schaller MD, Dunty JM. FAK regulates biological processes important for the pathogenesis of cancer. Cancer Metastasis Rev 2003;22:359-74.  Back to cited text no. 31
[PUBMED]    
32.
Shattil SJ. Integrins and Src: Dynamic duo of adhesion signaling. Trends Cell Biol 2005;15:399-403.  Back to cited text no. 32
[PUBMED]    
33.
Rossi F, Yozgat Y, de Stanchina E, Veach D, Clarkson B, Manova K, et al. Imatinib upregulates compensatory integrin signaling in a mouse model of gastrointestinal stromal tumor and is more effective when combined with dasatinib. Mol Cancer Res 2010;8:1271-83.  Back to cited text no. 33
[PUBMED]    
34.
Sakurama K, Noma K, Takaoka M, Tomono Y, Watanabe N, Hatakeyama S, et al. Inhibition of focal adhesion kinase as a potential therapeutic strategy for imatinib-resistant gastrointestinal stromal tumor. Mol Cancer Ther 2009;8:127-34.  Back to cited text no. 34
[PUBMED]    
35.
Luo Y, Roeder RG. Cloning, functional characterization, and mechanism of action of the B-cell-specific transcriptional coactivator OCA-B. Mol Cell Biol 1995;15:4115-24.  Back to cited text no. 35
[PUBMED]    
36.
Schubart K, Massa S, Schubart D, Corcoran LM, Rolink AG, Matthias P. B cell development and immunoglobulin gene transcription in the absence of Oct-2 and OBF-1. Nat Immunol 2001;2:69-74.  Back to cited text no. 36
[PUBMED]    
37.
Schubart DB, Rolink A, Kosco-Vilbois MH, Botteri F, Matthias P. B-cell-specific coactivator OBF-1/OCA-B/Bob1 required for immune response and germinal centre formation. Nature 1996;383:538-42.  Back to cited text no. 37
[PUBMED]    
38.
Kim U, Siegel R, Ren X, Gunther CS, Gaasterland T, Roeder RG. Identification of transcription coactivator OCA-B-dependent genes involved in antigen-dependent B cell differentiation by cDNA array analyses. Proc Natl Acad Sci U S A 2003;100:8868-73.  Back to cited text no. 38
[PUBMED]    
39.
Auer RL, Starczynski J, McElwaine S, Bertoni F, Newland AC, Fegan CD, et al. Identification of a potential role for POU2AF1 and BTG4 in the deletion of 11q23 in chronic lymphocytic leukemia. Genes Chromosomes Cancer 2005;43:1-10.  Back to cited text no. 39
[PUBMED]    
40.
Marafioti T, Ascani S, Pulford K, Sabattini E, Piccioli M, Jones M, et al. Expression of B-lymphocyte-associated transcription factors in human T-cell neoplasms. Am J Pathol 2003;162:861-71.  Back to cited text no. 40
[PUBMED]    
41.
Toman I, Loree J, Klimowicz AC, Bahlis N, Lai R, Belch A, et al. Expression and prognostic significance of Oct2 and Bob1 in multiple myeloma: Implications for targeted therapeutics. Leuk Lymphoma 2011;52:659-67.  Back to cited text no. 41
[PUBMED]    
42.
Zhao C, Inoue J, Imoto I, Otsuki T, Iida S, Ueda R, et al. POU2AF1, an amplification target at 11q23, promotes growth of multiple myeloma cells by directly regulating expression of a B-cell maturation factor, TNFRSF17. Oncogene 2008;27:63-75.  Back to cited text no. 42
[PUBMED]    
43.
Kim MS, Cheong YP, So HS, Lee KM, Son Y, Lee CS, et al. Regulation of cyclic AMP-dependent response element-binding protein (CREB) by the nociceptin/orphanin FQ in human dopaminergic SH-SY5Y cells. Biochem Biophys Res Commun 2002;291:663-8.  Back to cited text no. 43
[PUBMED]    
44.
Zaveri NT, Waleh N, Toll L. Regulation of the prepronociceptin gene and its effect on neuronal differentiation. Gene 2006;384:27-36.  Back to cited text no. 44
[PUBMED]    
45.
Abbracchio MP, Boeynaems JM, Barnard EA, Boyer JL, Kennedy C, Miras-Portugal MT, et al. Characterization of the UDP-glucose receptor (re-named here the P2Y14 receptor) adds diversity to the P2Y receptor family. Trends Pharmacol Sci 2003;24:52-5.  Back to cited text no. 45
[PUBMED]    
46.
Abbracchio MP, Burnstock G, Boeynaems JM, Barnard EA, Boyer JL, Kennedy C, et al. International Union of Pharmacology LVIII: Update on the P2Y G protein-coupled nucleotide receptors: From molecular mechanisms and pathophysiology to therapy. Pharmacol Rev 2006;58:281-341.  Back to cited text no. 46
[PUBMED]    
47.
Arase T, Uchida H, Kajitani T, Ono M, Tamaki K, Oda H, et al. The UDP-glucose receptor P2RY14 triggers innate mucosal immunity in the female reproductive tract by inducing IL-8. J Immunol 2009;182:7074-84.  Back to cited text no. 47
[PUBMED]    
48.
Wu X, Zang W, Cui S, Wang M. Bioinformatics analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. Eur Rev Med Pharmacol Sci 2012;16:1582-7.  Back to cited text no. 48
[PUBMED]    
49.
Springer TA. Adhesion receptors of the immune system. Nature 1990;346:425-34.  Back to cited text no. 49
[PUBMED]    
50.
Aruffo A, Dietsch MT, Wan H, Hellström KE, Hellström I. Granule membrane protein 140 (GMP140) binds to carcinomas and carcinoma-derived cell lines. Proc Natl Acad Sci U S A 1992;89:2292-6.  Back to cited text no. 50
    
51.
Stone JP, Wagner DD. P-selectin mediates adhesion of platelets to neuroblastoma and small cell lung cancer. J Clin Invest 1993;92:804-13.  Back to cited text no. 51
[PUBMED]    
52.
Gong L, Cai Y, Zhou X, Yang H. Activated platelets interact with lung cancer cells through P-selectin glycoprotein ligand-1. Pathol Oncol Res 2012;18:989-96.  Back to cited text no. 52
[PUBMED]    
53.
Hariri G, Zhang Y, Fu A, Han Z, Brechbiel M, Tantawy MN, et al. Radiation-guided P-selectin antibody targeted to lung cancer. Ann Biomed Eng 2008;36:821-30.  Back to cited text no. 53
[PUBMED]    
54.
Miettinen M, Virolainen M. Gastrointestinal stromal tumors – Value of CD34 antigen in their identification and separation from true leiomyomas and schwannomas. Am J Surg Pathol 1995;19:207-16.  Back to cited text no. 54
    
55.
Robinson TL, Sircar K, Hewlett BR, Chorneyko K, Riddell RH, Huizinga JD. Gastrointestinal stromal tumors may originate from a subset of CD34-positive interstitial cells of Cajal. Am J Pathol 2000;156:1157-63.  Back to cited text no. 55
[PUBMED]    
56.
Riddle ND, Gonzalez RJ, Bridge JA, Antonia S, Bui MM. A CD117 and CD34 immunoreactive sarcoma masquerading as a gastrointestinal stromal tumor: Diagnostic pitfalls of ancillary studies in sarcoma. Cancer Control 2011;18:152-9.  Back to cited text no. 56
[PUBMED]    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  >Abstract>Introduction>Materials and Me...>Results>Discussion>Article Figures>Article Tables
  In this article
>References

 Article Access Statistics
    Viewed1676    
    Printed34    
    Emailed0    
    PDF Downloaded65    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]