An in silico Microarray Data Analysis to Map the Significant Genes & study the Biological Process of Breast Cancer

Gene expression profiling provides insight into the functions of genes at a molecular level. Microarray technology measures the relative activity of previously identified target genes. For understanding the disease network fundamentals of breast cancer, analysis of gene expression profiling data derived from micro-array technology was done. The dataset was downloaded by the publically available server GEO database, the raw data file was normalized, clustered using the R scripts, the output of the files that is the normalized files were loaded in MeV for the analysis of Gene expression. The normalized file has a set of 22,284 genes. Clustering enables the user to find the different types of clusters available within the dataset. The dataset were histological normal breast epithelium and reduction mammoplasty was clustered together. The t test shows the most probable significant & non-significant gene data. 1791 significant genes derived. The most interacting hub node was found out using Cytoscape 2.6.3. Understanding the hub nodes in the breast cancer disease network may well provide vital insights into curing or treating the disease. Later the Gene Ontology studies were carried out using Cytoscape 2.6.3. Using certain parameters like Hypergeometric test, FDR correction rate using Benjamini Hochberg method, with a significance level of 0.05. From the network, the best hub nodes for the gene ontology analysis was considered. The overall outcome of the research was to interpret the biological function of the given microarray data set along with the genes responsible.