Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
권호 표지
DOI QR코드

DOI QR Code

Pathway and Network Analysis in Glioma with the Partial Least Squares Method

  • Gu, Wen-Tao (Department of Neurosurgery, Huashan Hospital, Fudan University) ;
  • Gu, Shi-Xin (Department of Neurosurgery, Huashan Hospital, Fudan University) ;
  • Shou, Jia-Jun (Department of Neurosurgery, Huashan Hospital, Fudan University)
  • Published : 2014.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Gene expression profiling facilitates the understanding of biological characteristics of gliomas. Previous studies mainly used regression/variance analysis without considering various background biological and environmental factors. The aim of this study was to investigate gene expression differences between grade III and IV gliomas through partial least squares (PLS) based analysis. The expression data set was from the Gene Expression Omnibus database. PLS based analysis was performed with the R statistical software. A total of 1,378 differentially expressed genes were identified. Survival analysis identified four pathways, including Prion diseases, colorectal cancer, CAMs, and PI3K-Akt signaling, which may be related with the prognosis of the patients. Network analysis identified two hub genes, ELAVL1 and FN1, which have been reported to be related with glioma previously. Our results provide new understanding of glioma pathogenesis and prognosis with the hope to offer theoretical support for future therapeutic studies.

Keywords

References

  1. Ashburner M, Ball CA, Blake JA, et al (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25, 25-9. https://doi.org/10.1038/75556
  2. Bolognani F, Gallani AI, Sokol L, et al (2012). mRNA stability alterations mediated by HuR are necessary to sustain the fast growth of glioma cells. J Neurooncol, 106, 531-42. https://doi.org/10.1007/s11060-011-0707-1
  3. Chakraborty S, Datta S, Datta S (2012). Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies. Bioinformatics, 28, 799-806. https://doi.org/10.1093/bioinformatics/bts022
  4. Fowler A, Thomson D, Giles K, et al (2011). miR-124a is frequently down-regulated in glioblastoma and is involved in migration and invasion. Eur J Cancer, 47, 953-63. https://doi.org/10.1016/j.ejca.2010.11.026
  5. Freije WA, Castro-Vargas FE, Fang Z, et al (2004). Gene expression profiling of gliomas strongly predicts survival. Cancer Res, 64, 6503-10. https://doi.org/10.1158/0008-5472.CAN-04-0452
  6. Gao QG, Li ZM, Wu KQ (2013). Partial least squares based analysis of pathways in recurrent breast cancer. Eur Rev Med Pharmacol Sci, 17, 2159-65.
  7. Goldbrunner RH, Bernstein JJ, Tonn JC (1998). ECM-mediated glioma cell invasion. Microsc Res Tech, 43, 250-7. https://doi.org/10.1002/(SICI)1097-0029(19981101)43:3<250::AID-JEMT7>3.0.CO;2-C
  8. Goodenberger ML, Jenkins RB (2012). Genetics of adult glioma. Cancer Genet, 205, 613-21. https://doi.org/10.1016/j.cancergen.2012.10.009
  9. Gosselin R, Rodrigue D, Duchesne C (2010). A Bootstrap-VIP approach for selecting wavelength intervals in spectral imaging applications. Chem Intel Lab Sys, 100, 12-21. https://doi.org/10.1016/j.chemolab.2009.09.005
  10. Harmalkar MN, Shirsat NV (2006). Staurosporine-induced growth inhibition of glioma cells is accompanied by altered expression of cyclins, CDKs and CDK inhibitors. Neurochem Res, 31, 685-92. https://doi.org/10.1007/s11064-006-9068-0
  11. Irizarry RA, Hobbs B, Collin F, et al (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249-64. https://doi.org/10.1093/biostatistics/4.2.249
  12. Ji G, Yang Z, You W (2011). PLS-Based Gene Selection and Identification of Tumor-Specific Genes. Ieee Transactions On Systems, Man, And Cybernetics-Part C: Appl Rev, 41, 830-41. https://doi.org/10.1109/TSMCC.2010.2078503
  13. Kanehisa M, Goto S (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 28, 27-30. https://doi.org/10.1093/nar/28.1.27
  14. Kawaguchi A, Yajima N, Komohara Y, et al (2012). Identification and validation of a gene expression signature that predicts outcome in malignant glioma patients. Int J Oncol, 40, 721-30.
  15. Kim S, Dougherty ER, Shmulevich I, et al (2002). Identification of combination gene sets for glioma classification. Mol Cancer Ther, 1, 1229-36.
  16. Ljubimova JY, Lakhter AJ, Loksh A, et al (2001). Overexpression of alpha4 chain-containing laminins in human glial tumors identified by gene microarray analysis. Cancer Res, 61, 5601-10.
  17. Malla R, Gopinath S, Alapati K, et al (2010). Downregulation of uPAR and cathepsin B induces apoptosis via regulation of Bcl-2 and Bax and inhibition of the PI3K/Akt pathway in gliomas. PLoS One, 5, 13731. https://doi.org/10.1371/journal.pone.0013731
  18. Martins JPA, Teofilo RF, Ferreira MMC (2010). Computational performance and cross-validation error precision of five PLS algorithms using designed and real data sets. J Chemomet, 24, 320-32.
  19. Nabors LB, Gillespie GY, Harkins L, King PH (2001). HuR, a RNA stability factor, is expressed in malignant brain tumors and binds to adenine- and uridine-rich elements within the 3' untranslated regions of cytokine and angiogenic factor mRNAs. Cancer Res, 61, 2154-61.
  20. Nielsen M, Christensen L, Albrechtsen R (1983). The basement membrane component laminin in breast carcinomas and axillary lymph node metastases. Acta Pathol Microbiol Immunol Scand A, 91, 257-64.
  21. Sami A, Karsy M (2013). Targeting the PI3K/AKT/mTOR signaling pathway in glioblastoma: novel therapeutic agents and advances in understanding. Tumour Biol, 34, 1991-2002. https://doi.org/10.1007/s13277-013-0800-5
  22. Shannon P, Markiel A, Ozier O, et al (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13, 2498-504. https://doi.org/10.1101/gr.1239303
  23. Shaochun YXWKL (1997). A Study of Prion Protein Expression in Gliomas. Acta universitatis medictnae tangji, 3.
  24. Smyth GK, Michaud J, Scott HS (2005). Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics, 21, 2067-75. https://doi.org/10.1093/bioinformatics/bti270
  25. Stelzl U, Worm U, Lalowski M, et al (2005). A human protein-protein interaction network: a resource for annotating the proteome. Cell, 122, 957-68. https://doi.org/10.1016/j.cell.2005.08.029
  26. Wei KC, Huang CY, Chen PY, et al (2010). Evaluation of the prognostic value of CD44 in glioblastoma multiforme. Anticancer Res, 30, 253-9.
  27. Wu S, Zhang X, Li ZM, et al (2013). Partial least squares based gene expression analysis in EBV- positive and EBV-negative posttransplant lymphoproliferative disorders. Asian Pac J Cancer Prev, 14, 6347-50. https://doi.org/10.7314/APJCP.2013.14.11.6347

Cited by

  1. Fibulin-5 is a Prognostic Marker that Contributes to Proliferation and Invasion of Human Glioma Cells vol.16, pp.2, 2015, https://doi.org/10.7314/APJCP.2015.16.2.769