A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma

Abstract

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.

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

“…On the other hand, biological networks, constructed on the basis of the known functions and interactions of individual molecules, offer alternate approaches that are superior to blind learning approaches. Networks have the added advantage of combining condition specific transcriptome data and allow understanding of the functional role of the individual genes capable of discriminating disease from healthy or between different disease stages 22 – 24 . Machine learning methods on the other hand are capable of providing a quantitative picture of the classification efficiencies of the individual genes 25 , 26 .…”

Decreased expression and clinical significance of miR-148a in hepatocellular carcinoma tissues

“…In the meantime, in Japan, United States, and Europe, hepatitis C virus (HCV) infection is predominant, and consequently, it has become the key risk factor for contracting HCC in these regions [ 3 – 7 ]. Although the improvement of molecular biology has led to the identification of new tumor markers that play vital roles in the treatment and prognosis of HCC, more tumor markers are still required for effective early diagnosis and monitoring of the curative effect of HCC [ 8 – 15 ]. MicroRNAs, a major class of small non-coding RNAs, are well-conserved very small RNA molecules (20 to 22 nucleotides) that can negatively modulate gene expression post-transcriptionally.…”

Identification of proteins in promastigote and amastigote-like stages of Leishmania tropica by a quantitative proteomic approach

“…In the protein-protein network, each node (protein) with corresponding gene expression value is regarded as "seed node." For a seed node i, this node and its neighbors j within the shortest distance k, form a connected subnetwork with n nodes [45]. These seed nodes can serve as candidate drug and vaccine for cutaneous leishmaniasis caused by L. tropica.…”