In this paper, we proposed an approach systematically based on the use of gene co-expression network analyses to identify potential biomarkers for Hepatocellular Carcinoma (HCC). With the analysis of differential gene expression, we first selected candidate genes closely related to HCC from the whole genome on a large scale. By identifying the relationships between each two genes, we built up the gene co-expression network using Cytoscape software. Then the global network was clustered into several sub-modules by Markov Cluster Algorithm (MCL). And, GO-Analysis was carried out for these identified gene modules to further explore the genes obviously associated with the dysfunctions of HCC, and in result we find Hexokinase 2 (HK2) and Krüppel-like Factor 4 (KLF4) as potential candidate biomarkers to provide insights into the mechanism of the development of HCC. Finally, we evaluated the disease classification results via an SVM-based machine learning method to verify the accuracy of the classification
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