BIOINFORMATICS-BASED ANALYSIS OF DIFFERENTIALLY EXPRESSED GENES AND MOLECULAR PATHWAYS ASSOCIATED WITH BREAST CANCER PROGRESSION

Abstract

metastatic potential, and patient survival. The objective of this study was to find differentially expressed genes and molecular signatures for breast cancer progression using an integrated approach to bioinformatics. Comparisons were made between Grade 1 and Grade 3 tumors and node-negative and node-positive breast cancer to analyze gene-expression and clinicopathological data. Differentially expressed genes were determined following false discovery rate correction, and overlapping genes between the two progression-related comparisons were regarded as candidate progression-associated genes. The analytical framework was expanded to include functional interpretation, protein interaction analysis, hub-gene prioritization, clinicopathological association and survival analysis. Twenty-eight, eighty-eight (288) genes were found to be significantly differently expressed between Grade 1 and Grade 3 tumors and 82 genes were differently expressed between node-negative and node-positive tumors. There were 67 genes common to both analyses, and 66 of these showed a similar expression pattern. Survival analysis also yielded several genes with prognostic significance related to progression, such as RHEB. The results suggest that cell cycle, proliferation and regulatory pathways are strongly linked together and are crucial for the progression of breast cancer. The genes identified are potential candidates for additional investigation in the prognosis and treatment of patients.