Topology-based cancer classification and related pathway mining using microarray data.
Cancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.
Liu
Chun-Chi
CC
Department of Computer Science, National Chung-Hsing University, Taichung, Taiwan, ROC.
Chen
Wen-Shyen E
WS
Lin
Chin-Chung
CC
Liu
Hsiang-Chuan
HC
Chen
Hsuan-Yu
HY
Yang
Pan-Chyr
PC
Chang
Pei-Chun
PC
Chen
Jeremy J W
JJ
D020869:Gene Expression Profiling; D052138:Genes, Neoplasm; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000145:classification* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results