Towards the early detection of ductal carcinoma (a common type of breast cancer) using biomarkers linked to the PPAR(γ) signaling pathway

Ghazala Sultan

doi:10.6026/97320630015799

Abstract

Breast cancer is a leading cause of morbidity and mortality among women comprising about 12% females worldwide. The underlying alteration in the gene expression, molecular mechanism and metabolic pathways responsible for incidence and progression of breast tumorigenesis are yet not completely understood. In the present study, potential biomarker genes involved in the early progression for early diagnosis of breast cancer has been detailed. Regulation and Gene profiling of Ductal Carcinoma In-situ (DCIS), Invasive Ductal Carcinoma (IDC) and healthy samples have been analyzed to follow their expression pattern employing normalization, statistical calculation, DEGs annotation and Protein-Protein Interaction (PPI) network. We have performed a comparative study on differentially expressed genes among Healthy vs DCIS, Healthy vsIDC and DCIS vs IDC. We found MCM102 and SLC12A8as consistently over-expressed and LEP, SORBS1, SFRP1, PLIN1, FABP4, RBP4, CD300LG, ID4, CRYAB, ECRG4, G0S2, FMO2, ADAMTS5, CAV1, CAV2, ABCA8, MAMDC2, IGFBP6, CLDN11, TGFBR3as under-expressed genes in all the 3 conditions categorized for pre-invasive and invasive ductal breast carcinoma. These genes were further studied for the active pathways where PPAR(γ) signaling pathway was found to be significantly involved. The gene expression profile database can be a potential tool in the early diagnosis of breast cancer.

Highlights

Breast cancer is one of the second leading cause of mortality in females at the global level [1]
Ductal Carcinoma In-situ (DCIS) being the primary or early stage of breast cancer for tumorigenesis, in current analysis we selected three stages, i.e. healthy, DCIS and Invasive Ductal Carcinoma (IDC) patient gene expression with the aim to identify a set of genes which play a pivotal role in the initiation, progression of cancerous and gene involved in further development of the DCIS to IDC stages
The distribution of data post normalization has uniform intensity within the same intervals and the same density center. This helps us to understand the nonuniformity of the gene expression in the raw data which made it necessary to normalize the data before processing for minimizing the error probability in the results