Bioinformatics and Personalized Medicine

Variability is the law of life, with the use of novel genomics technologies large amount of data from various individuals can be derived which is named Big Data. Novel development of computer software and hardware for Biocomputing deciphers the structure and function of genes. Such Big Data can be used to describe the complexity underneath the obstacles of bringing information and hence knowledge from lab to bed. The systematic study of protein-protein interaction networks through systems biology-based analysis has been provided an appropriate strategy to discover candidate proteins and key biological pathways as a major bioinformatics approach in Biomarker discovery. For example, using a comprehensive list of gastric cancer-involved proteins we retrieved interaction information in this mortal disease. Dominant functional theme and centrality parameters including Betweenness, Closeness and Stress of each topological clusters and expressionally active subnetworks in the resulted network were investigated. The results of functional analysis on gene sets showed that neurotrophoin signaling pathway, cell cycle and nucleotide excision possesses the strongest enrichment signals. According to the computed centrality parameters, HNF4A, TAF1, TP53 and AKT1 were the most significant nodes in interaction networks of the engaged proteins in gastric cancer. Introduced pathways and proteins in this study can be applied as diagnostic markers and therapeutic targets in future studies on gastric cancer and other cancers or complex diseases. Bioinformatics has the mission to verify new knowledge in order to detect prevent and hence cure diseases in a specific or personalized manner.

Keywords:

Bioinformatics , Gastric Cancer , Biomarker