hsa-miR-1204 and hsa-miR-639 Prominent Role in Tamoxifen's Molecular Mechanisms on the EMT Phenomenon in Breast Cancer Patients

Authors: Mahsa Taghavi

Abstract:

In the treatment of breast cancer, tamoxifen is a regularly prescribed medication. The effect of tamoxifen on breast cancer patients' EMT pathways was studied. In this study to see if it had any effect on the cancer cells' resistance to tamoxifen and to look for specific miRNAs associated with EMT. In this work, we used continuous and integrated bioinformatics analysis to choose the optimal GEO datasets. Once we had sorted the gene expression profile, we looked at the mechanism of signaling, the ontology of genes, and the protein interaction of each gene. In the end, we used the GEPIA database to confirm the candidate genes. after that, I investigated critical miRNAs related to candidate genes. There were two gene expression profiles that were categorized into two distinct groups. Using the expression profile of genes that were lowered in the EMT pathway, the first group was examined. The second group represented the polar opposite of the first. A total of 253 genes from the first group and 302 genes from the second group were found to be common. Several genes in the first category were linked to cell death, focal adhesion, and cellular aging. Two genes in the second group were linked to cell death, focal adhesion, and cellular aging. distinct cell cycle stages were observed. Finally, proteins such as MYLK, SOCS3, and STAT5B from the first group and BIRC5, PLK1, and RAPGAP1 from the second group were selected as potential candidates linked to tamoxifen's influence on the EMT pathway. hsa-miR-1204 and hsa-miR-639 have a very close relationship with the candidates genes according to the node degrees and betweenness index. With this, the action of tamoxifen on the EMT pathway was better understood. It's important to learn more about how tamoxifen's target genes and proteins work so that we can better understand the drug.

Keywords: tamoxifen, breast cancer, bioinformatics analysis, EMT, miRNAs