Metabolic Pathway of Hereditary Cancer Disease from PPI-network of DEGS Detected using Mean-of-Mean Method

Abstract

Uncontrolled growth of cells often results Cancer disease in human body. When it is eventually transmitted throughout the generations in a family, it is referred to as hereditary diseases. Metabolic pathway explains several chemical reactions occurred for growth of a disease and KEGG pathway analysis identifies key genes involved with that disease. At first, differentially expressed genes (DEGs) are detected from cancer gene microarray time series datasets using a new and simple method, Mean of Mean (MoM). The MoM concept is developed from Gregor Johann Mendel's First Law of Heredity or Segregation rule of heredity. Highly expressed (HG) and lowly expressed (LG) genes in two different groups of microarray dataset are identified first using MoM. Then DEGs are found by implementing intersection operation between HG and LG genes. Performance of MoM method is analyzed by Support Vector Machine classifiers (SVMs) on some binary class cancer microarray data samples. Then all results are compared with the performance of other statistical parametric and nonparametric hypothetic tests. It is noticed that performance ofMoMis better than other statistical methods in almost all data sets. Finally, Protein-Protein Interaction Networks (PPINs) are constructed within identified DEGs using web based tool. Lastly, KEGG pathway analysis is performed for all proteins involved in PPINs to obtain list of key genes for growth of cancer disease.