[Paper Review] Cancer systems biology: exploring cancer-associated genes on cellular networks

Edwin Wang, Anne E.G. Lenferink+1 more|ArXiv.org|Dec 21, 2007

Bioinformatics and Genomic Networks72 references21 citations

TL;DR

This paper proposes a systems biology approach to understand cancer-associated genes by integrating them into cellular protein and signaling networks. By analyzing network topology, the study reveals that cancer genes frequently act as hub proteins, connecting multiple pathways, and identifies novel candidates through gene-gene and gene regulatory network reconstruction using expression data, advancing the discovery of oncogenic regulators.

ABSTRACT

Genomic alterations lead to cancer complexity and form a major hurdle for a comprehensive understanding of the molecular mechanisms underlying oncogenesis. In this review, we describe the recent advances in studying cancer-associated genes from a systems biological point of view. The integration of known cancer genes onto protein and signaling networks reveals the characteristics of cancer genes within networks. This approach shows that cancer genes often function as network hub proteins which are involved in many cellular processes and form focal nodes in the information exchange between many signaling pathways. Literature mining allows constructing gene-gene networks, in which new cancer genes can be identified. The gene expression profiles of cancer cells are used for reconstructing gene regulatory networks. By doing so, the genes, which are involved in the regulation of cancer progression, can be picked up from these networks after which their functions can be further confirmed in the laboratory.

Motivation & Objective

• To understand the role of cancer-associated genes within the context of cellular networks.
• To identify patterns in the network topology of known cancer genes, particularly their centrality and connectivity.
• To develop a systems-level approach for identifying novel cancer-related genes using network-based integration of genomic and expression data.
• To reconstruct gene regulatory networks from cancer cell expression profiles to pinpoint key regulators of oncogenesis.

Proposed method

• Integration of known cancer genes into protein-protein and signaling interaction networks to analyze their network properties.
• Use of literature mining to construct gene-gene interaction networks for identifying potential cancer genes.
• Reconstruction of gene regulatory networks from cancer cell gene expression profiles using computational methods.
• Identification of hub and bottleneck proteins in networks to prioritize genes with potential functional significance in cancer.
• Application of network centrality measures to assess the importance of cancer genes within signaling pathways.
• Validation of predicted regulatory relationships through experimental follow-up in laboratory settings.

Experimental results

Research questions

• RQ1 How do cancer-associated genes distribute within cellular protein and signaling networks?
• RQ2 What network properties, such as centrality or connectivity, are characteristic of cancer genes?
• RQ3 Can gene-gene interaction networks derived from literature mining help identify novel cancer-related genes?
• RQ4 How can gene expression profiles be used to reconstruct regulatory networks that reveal key drivers of cancer progression?
• RQ5 To what extent do cancer genes act as hubs or bottlenecks in information flow across signaling pathways?

Key findings

• Cancer-associated genes are significantly enriched as hub proteins in cellular networks, indicating their central role in multiple cellular processes.
• These hub proteins often connect multiple signaling pathways, suggesting their importance in coordinating oncogenic signals.
• Network-based analysis of gene-gene interactions derived from literature mining successfully identifies potential novel cancer-related genes.
• Gene regulatory networks reconstructed from cancer cell expression profiles highlight specific genes involved in the regulation of cancer progression.
• The integration of genomic data with network topology enables the prioritization of candidate oncogenes for further experimental validation.
• Cancer genes frequently occupy bottleneck positions in networks, indicating their potential as critical control points in oncogenic signaling.