1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Bioinformatics-Based Prediction of Recurrence in Tamoxifen-Treated Patients with Estrogen Receptor-Positive Breast Cancer
Paper ID : 1385-ICB10
Authors:
Fatemeh Nabizadeh1, Farshad Qalekhani *2, Shabnaz Koochakkhani3
1Pharmaceutical Sciences Research Center, Health Technologies Institute, Kermanshah University of Medical Sciences(KUMS), Kermanshah 6734667149, Iran
2Pharmaceutical Sciences Research Center, Health Technologies Institute, Kermanshah University of Medical Sciences (KUMS), Kermanshah 6734667149, Iran
3Department of Human Genetics, Faculty of Medicine, Hormozgan University of Medical Sciences(HUMS), Bandar Abbas 7919693116, Iran
Abstract:
Estrogen receptor (ER)-positive breast cancer is the most common subtype of all invasive breast cancer types. After binding with the ligand, the activated ER can promote cell proliferation while inhibiting cell apoptosis. Tamoxifen (TAM) acts as a selective ER modulator in adjuvant therapy for ER-positive breast cancer, inhibiting the proliferation of cancer cells and activating apoptosis. Although TAM treatment can drastically decrease mortality rates among breast cancer patients, about half of the patients still suffer from the recurrence of therapy resistance tumors. Therefore, identification of a molecular signature that predicts the relapse of TAM-treated patients could help the therapeutical management of ER-positive breast cancer.
Here, we used network analysis to compare the responses of ER-positive breast cancer patients to TAM, as some patients developed metastasis while others showed partial or complete remission after treatment. To do this, two microarray-based gene expression profiling datasets, GSE9893 and GSE82172 were downloaded from GEO. After merging the datasets and batch effect removal, differentially expressed genes (DEGs) were obtained by comparing the expression values between recurrent and nonrecurrent breast cancer samples. Limma package was used to calculate fold changes of the DEGs. Next, we selected the genes with adjusted p-value <0.05 and Log2 fold change <-1 and >1. Functional and pathway enrichment analyses of DEGs were performed using the Enrichr web server. Following network analysis within Cytoscape software, the hub genes were identified by selecting the top 10% of nodes harboring the highest degree of connectivity, using the cytoHubba plugin. In conclusion, we found that FN1, ACTB, COL1A2, COL3A1, VIM, CTGF, YWHAZ, ACTA2, LUM, and COL4A1 act as hub genes in the TAM-responsive regulatory network. The results can be helpful in predicting the response of ER-positive breast cancer patients to TAM and identifying patients who do not benefit from Tamoxifen treatment.
Keywords:
Breast cancer; ER-positive, Microarray; Recurrence; Tamoxifen.
Status : Paper Accepted (Poster Presentation)