Computational Evaluation of Gene Expression and Protein Protein Interaction Data to Characterize Significant Network Markers in Breast Lung and Prostate Cancer

Abstract

Abstract newlineCancer belongs to a class of heterogeneous, highly aggressive diseases and a leading newlinecause of death worldwide. Identifying the genes causing divergence of molecular and newlinecellular processes in human cancer would help in drug design for the disease. With newlinemore than 100 types of cancers, breast, lung, and prostate cancer remain to be the most newlinecommon types. Hence, cancer gene discovery is an important challenge clinically and newlinecomputationally in a comprehensive genetic context, where a wide variety of omics newlinedata are available. To obtain transcriptomic data, cancer samples are investigated at a newlinegenome-wide scale using high-throughput measurement techniques such as DNA (Deoxyribonucleic newlineacid) sequencing and microarrays. A plethora of cancer microarray and newlineRNA (Ribonucleic acid) sequencing (RNA-Seq) experiments are performed and the obtained newlinedata are publicly available in databases, including the Gene Expression Omnibus newline(GEO), Array Express and The Cancer Genome Atlas (TCGA). These rapidly evolving newlinetechnologies provide experimental data that have two challenging characteristics: the newlinevolume of data is large and data are complex to analyze. When these data are analyzed newlinein an accurate and scalable manner, noteworthy conclusions can be drawn relevant to newlinebiomedical research. Researchers perform rigorous investigations and analysis of these newlinedata which are categorized into different clinical or in-silico data types such as gene expression newlinedata, pathway and regulatory data, Protein-Protein-Interaction (PPI) data and newlineothers. Numerous researchers have published interesting results in the identification newlineof biomarkers as a result of bioinformatics analysis, which can yield deep insight in newlineunderstanding the biological processes of cancer oncology [1 3]. The identified gene newlinebiomarkers are typically called as Diffrentially Expressed Genes (DEGs). Hence, detection newlineof DEGs is essential to understand the complex functional changes that occur in the newlinedisease. This thesis aims at characterizing a set of such DEGs that are common to di