Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31106
Gene Expression Signature for Classification of Metastasis Positive and Negative Oral Cancer in Homosapiens

Authors: A. Shukla, A. Tarsauliya, R. Tiwari, S. Sharma

Abstract:

Cancer classification to their corresponding cohorts has been key area of research in bioinformatics aiming better prognosis of the disease. High dimensionality of gene data has been makes it a complex task and requires significance data identification technique in order to reducing the dimensionality and identification of significant information. In this paper, we have proposed a novel approach for classification of oral cancer into metastasis positive and negative patients. We have used significance analysis of microarrays (SAM) for identifying significant genes which constitutes gene signature. 3 different gene signatures were identified using SAM from 3 different combination of training datasets and their classification accuracy was calculated on corresponding testing datasets using k-Nearest Neighbour (kNN), Fuzzy C-Means Clustering (FCM), Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN). A final gene signature of only 9 genes was obtained from above 3 individual gene signatures. 9 gene signature-s classification capability was compared using same classifiers on same testing datasets. Results obtained from experimentation shows that 9 gene signature classified all samples in testing dataset accurately while individual genes could not classify all accurately.

Keywords: Cancer, classification, SAM, Gene Signature

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328045

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1765

References:


[1] O-Donnell, K. R.et al. (2005), Gene expression signature predicts lymphatic metastasis in squamous cell carcinoma of the oral cavity, Oncogene, 24, 1244-1251.
[2] Ming-Jian Ge et al. (2009), Gene expression signature for lymphatic metastasis of human lung adenocarcinoma . Chinese Journal of Cancer 28:3, 220-224.
[3] Zhou X. et al. (2006), Global Expression-Based Classification of Lymph Node Metastasis and Extracapsular Spread of Oral Tongue Squamous Cell Carcinoma. Neoplasia. Vol. 8, No. 11, pp. 925 - 932.
[4] Van ÔÇÿt Veer LJ, Dai H, Van de Vijver MJ et al. (2002), Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530-536
[5] Lu Y. et al. (2006), A Gene Expression Signature Predicts Survival of Patients with Stage I Non-Small Cell Lung Cancer. PLoS Medicine, Vol. 3, Issue 12, e467.
[6] Roepman P. et al. (2006), Multiple Robust Signatures for Detecting Lymph Node Metastasis in Head and Neck Cancer. Cancer Research; 66:2361-2366.
[7] Tusher, Tibshirani and Chu (2001); "Significance analysis of microarrays applied to the ionizing radiation response", PNAS 2001 98: 5116-5121.
[8] Pramana J. et al. (2007), Gene Expression Profiling to Predict Outcome After Chemoradiation in Head and Neck Cancer," International Journal of Radiation Oncology * Biology * Physics, Vol. 69, Issue 5, Pages 1544-1552.
[9] Millenaar FF. et al. (2007), Identification of a predictive gene expression signature of cervical lymph node metastasis in oral squamous cell carcinoma, Cancer Science, Vol.98, Issue 5, Pages 740-746.
[10] Bertucci F., Finetti P., Cervera N et al (2006) Gene expression profiling and clinical outcome in breast cancer. Omics 10:429-443.
[11] Kondohet N. et al. (2007), Gene expression signatures that can discriminate oral leukoplakia subtypes and squamous cell carcinoma, Oral Oncology, Volume 43, Issue 5, Pages 455-462.
[12] Van de Vijver MJ, He YD, Van-t Veer LJ et al. (2002), A geneexpression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999-2009.
[13] Parry M. R. et al. (2010), k- Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction, The Pharmacogenomics Journal (2010) 10, 292-309.
[14] Dembélé D. and Kastner P. (2003) Fuzzy C-means method for clustering microarray data. Bioinformatics, 19(8): 973-980.
[15] Han, L. K., Zeng X. and Yan H. (2008) Fuzzy clustering analysis of microarray data, Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine October 1, 2008 222: 1143- 1148.
[16] Suykens, J (2001), "Support Vector Machines : a nonlinear modelling and control perspective" , European Journal of Control, Special Issue on fundamental issues in control, 7(2-3):311-327.
[17] Guyon, I, Weston, J, Barnhill, S, and Vapnik, V (2002)., "Gene selection for cancer classification using support vector machines", Machine Learning, 46:389-422.
[18] Chen L. and Boggess L. (2002), Neural Networks for genome signature analysis, Proceedings of the 2002 International Conference on Neural Information Processing , pp. 1554-1558.