Informatics in Medicine Unlocked (Jan 2022)
Agonist/antagonist compounds' mechanism of action on estrogen receptor-positive breast cancer: A system-level investigation assisted by meta-analysis
Abstract
The largest group of breast cancer patients are estrogen receptor-positive (ER+). There are a vast amount of studies focused on breast cancer. That vastness provides the requisites for the integration and meta-analysis of the related studies. Meta-analysis could lead to more reliable results than single investigations, which in turn are individually influenced by different known and unknown batch effects. In the present study, a specific layout for meta-analysis of several RNA-seq datasets was proposed to obtain a methodology with maximum precision and least error-prone. Meta-analysis was separately performed on two estrogen-treated MCF7 and T47D versus untreated cell lines to obtain meta-differentially expressed genes. Further, shared significant genes between MCF7 and T47D cell lines were enriched to get more stringent results. The ER+ cell lines respond to treatment with both ER agonist (E2) and ER antagonists (Tamoxifen, Fulvestrant, and Brilanestrant). Hence, the meta-analysis results were compared with genes affected by ER antagonists to understand the function of ER and its target genes. Genes involved in human mitochondria, including MT-CO1-3, COX4I1, MT-ND1-6, MT-ATP6, MT-ATP8, and MT-CYB, and several keratin family members including KRT8, KRT10, KRT18, and KRT19 genes were up-regulated in the meta-analysis. Still, they showed no alteration neither in individual datasets treated with E2 and ER antagonists. LINC01016 was up-regulated in the meta-analysis, individual datasets, and cell lines treated with Tamoxifen and down-regulated with Fulvestrant and Brilanestrant. Our findings indicated that Tamoxifen does not block some genes directly affected by ER and has no effect on their expression. Moreover, to the best of the authors' knowledge, pathways were identified that were not previously reported in breast cancer. Overall, meta-analysis of RNA-seq data with a more exact methodology could identify new genes and pathways involved in breast cancer progression. If there are suitable datasets, utilizing the present methodology is recommended for other diseases to obtain more exact results.
