Example of Bioinformatic Analysis in Breast Cancer: Comparison of Invasive Ductal Carcinoma and Invasive Lobular Carcinoma for The Definition of New Biomarkers

Abstract

In our study, it was aimed to determine the gene/genes that can be evaluated as biomarkers in early diagnosis with bioinformatic analysis in Invasive ductal carcinoma (IDC) and Invasive lobular carcinoma (ILC), which are frequently encountered breast cancer types. IDC is the most common subtype, 80% of all breast cancers. Invasive Ductal means that cancer has invaded or spread to the surrounding breast tissues from the ductal wall. ILC is around 10% of all breast cancers. In this type, the cancer is surrounding the milk duct, but their shapes are not deformed. This is why it is very difficult to identify in mamammographyGenetic tests and their analysis is very important for the early detection of cancer. Physicians can estimate bad prognoses and can develop better treatment plans. 17 ILC and 83 IDC patients’ data were gathered from online databases. GEO, Array express, SRA. We developed software using R Bioconductor for differential expression analysis of the data. Our analysis picked TFF3, MMP9, DUSP1, SCGB2A2, CTHRC1, APOD, TGFB3, NMU, IGFBP1, CD34 genes as differentially expressed. The relations among genes are demonstrated using the online STRING tool. The threshold for the expression levels is selected in terms of Log-fold change (LogFC). The down limit is LogFC -0.9; the down limit is logFC 1.693. According to the STRING analysis, 4 genes were strongly linked; MM9, CD34, IGFBP1 and CTHRC1. CD34 is upregulated in ILC (logFC 1.693, p<0.05) and downregulated in IDC (logFC -0.9, p<0.05). As a result of our study, we suggest that CD34 gene expression should be primarily evaluated in distinguishing between ILC and IDC.