Inferring propensity amongst lung and breast carcinomas via overlapped gene expression profiles

Abstract

Reconstruction of biological networks for topological analyses helps in correlation identification between various types of biomarkers. These networks have been vital components of System Biology in present era. Genes are the basic physical and structural unit of heredity. Genes act as instructions to make molecules called proteins. Alterations in the normal sequence of these genes are the root cause of various diseases and cancer is the prominent example disease caused by gene alteration or mutation. These slight alterations can be detected by microarray analysis. The high throughput data obtained by microarray experiments aid scientists in reconstructing cancer specific gene regulatory networks. The purpose of experiment performed is to find out the overlapping of the gene expression profiles of breast and lung cancer data, so that the common hub genes can be sifted and utilized as drug targets which could be used for the treatment of diseased conditions. In this study, first the differentially expressed genes have been identified (lung cancer and breast cancer), followed by a filtration approach and most significant genes are chosen using paired t-test and gene regulatory network construction. The obtained result has been checked and validated with the available databases and literature.