Microarray s gene expression analysis in breast cancer using system biology approaches

In recent years, cancer research has benefited from a large number of available high throughputs gene expression datasets. The results of such studies suggest new candidatebiomarkers for cancers. The NCBI gene expression omnibus (GEO) datasets are being used to specify the list of differentially expressed genes (DEGs) between cancer tissues and the adjacent non-tumor tissues. Breast cancer as the most common cancers among women is the main target of this study. GSE103512 (ductal breast cancer, 49 samples) was selected from GEO datasets and the preprocessing and normalization steps were performed on the downloaded CEL files (raw data) using an AffylmGUI package in R software version x64 3.2.2. After normalization, the probe IDs were mapped to gene symbols according to AffyMetrix annotation files provided for each platform. The Limma package in AffylmGUI was applied to identify the set of DEGs in our target cancer. DEGs with adjusted p-values ≤ 0.05 were considered as significant. Subsequently, DEGs with logFC (fold change) values ≥1 or 1≤ that respectively correspond to a two-fold increase and decrease in the expression levels were selected. Functional enrichment analysis was carried out for the identified DEGs using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. In total, 2524 DEGs with 395 down genes and 105 up genes were detected in breast cancer. Also, differential coexpression analysis was performed using the ARACNE algorithm. The results of the differential expression study introduced new genes in breast cancer and provided better insights into the molecular characteristics of this malignancy.

کلیدواژه ها:

GEO ، GSE103512 ، AffylmGUL ، Limma ، LogFC ، DAVID