A network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma

In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC) investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC.