Title: Identification of glioma cancer-alerted gene markers based on a diagnostic outcome correlation analysis preferential approach

Authors: Bin Han; Haifeng Lai; Ruifei Xie; Lihua Li; Lei Zhu

Addresses: College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, 310018 Zhejiang, P.R. China ' College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, 310018 Zhejiang, P.R. China ' Hangzhou Cancer Institute, Hangzhou Cancer Hospital, 310002 Zhejiang, P.R. China ' College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, 310018 Zhejiang, P.R. China ' College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, 310018 Zhejiang, P.R. China

Abstract: Identifying glioma cancer-alerted genetic markers through analysis of microarray data allows us to detect tumours at the genome-wide level. To this end, we propose to identify glioma gene markers based primarily on their correlation with the glioma diagnostic outcomes, rather than merely on the classification quality or differential expression levels, as it is not the classification or expression level per se that is crucial, but the selection of biologically relevant biomarkers is the most important issue. With the help of singular value decomposition, microarray data are decomposed and the eigenvectors corresponding to the biological effect of diagnostic outcomes are identified. Genes that play important roles in determining this biological effect are thus detected. Therefore, genes are essentially identified in terms of their strength of association with diagnostic outcomes. Monte Carlo simulations are then used to fine tune the selected gene set in terms of classification accuracy. Experiments show that the proposed method achieves better classification accuracies and is data sets independent. Graph-based statistical analysis showed that the selected genes have close relationships with glioma diagnostic outcomes. Further biological database and literature study confirms that the identified genes are biologically relevant.

Keywords: gene selection; glioma gene markers; biomarkers; SVD; singular value decomposition; Monte Carlo simulation; tumour detection; diagnostic outcomes; cancer diagnosis; microarray data; intracranial tumour; brain tumour; bioinformatics.

DOI: 10.1504/IJDMB.2014.057778

International Journal of Data Mining and Bioinformatics, 2014 Vol.9 No.1, pp.67 - 88

Received: 16 Jun 2011
Accepted: 12 Feb 2012
Published online: 21 Oct 2014 *

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