No abstract

Background Breast cancer is a highly malignant tumor that affects a large number of women worldwide. Sesamol, a natural compound, has been shown to exhibit inhibitory effects on various tumors, including breast cancer. However, the underlying mechanism of its action has not been fully explored. In this study, we aimed to investigate the effect of sesamol on the transcriptome of MCF-7 breast cancer cells, in order to better understand its potential as an anti-cancer agent. Methods The transcriptome profiles of MCF-7 breast cancer cells treated with sesamol were analyzed using Illumina deep-sequencing. The differentially expressed genes (DEGs) between the control and sesamol-treated groups were identified, and GO and KEGG pathway analyses of these DEGs were conducted using ClueGO. Protein–protein interaction (PPI) network of DEGs was mapped on STRING database and visualized by Cytoscape software. Hub genes in the network were screened by Cytohubba plugin of Cytoscape. Prognostic values of hub genes were analyses by the online Kaplan–Meier plotter and validated by qRT-PCR in MCF-7 cells. Results The results of the study showed that sesamol treatment had a significant effect on the transcriptome of MCF-7 cells, with a total of 351 DEGs identified. Functional enrichment analyses of DEGs revealed their involvement in extracellular matrix (ECM) remodeling, fatty acid metabolism and monocyte chemotaxis. The protein–protein interaction (PPI) network analysis of DEGs resulted in the identification of 10 hub genes, namely IGF2, MMP1, MSLN, CXCL10, WT1, ITGAL, PLD1, MME, TWIST1 , and FOXA2 . Survival analysis showed that MMP1 and ITGAL were significantly associated with overall survival (OS) and recovery-free survival (RFS) in breast cancer patients. Conclusion Sesamol may play important roles in extracellular matrix (ECM) remodeling, fatty acid metabolism and cell cycle of MCF-7.

Background Breast cancer stem cells (BCSCs) are associated with tumor initiation, invasion, metastasis and drug resistance. It is known that many proteins and signaling pathways are involved in the regulation of BCSCs, however, much more efforts are needed to understand BCSCs comprehensively. Tumor-infiltrating immune cells are important in cancer treatment efficacy and patient prognosis. We tried to identify potential suppressor of BCSCs and analyze its correlation with various immune cells infiltration by bioinformatic and experimental methods. Methods Expression level and methylation state of OVOL2 were analyzed by tools from bc-GenExMiner v4.8 and UALCAN databases. The Kaplan–Meier plotter was applied to evaluate the prognostic values of OVOL2. Gene expression datasets (GSE7515, GSE15192) were selected to analyze differentially expressed genes (DEGs) related to BCSCs. GO and KEGG pathway analyses of DEGs were conducted. MCODE app plugin of Cytoscape was used to screen modules in PPI network of downregulated DEGs. Correlation of OVOL2 expression with infiltrating immune cells was evaluated by TIMER 2.0. Experiments were conducted to verify whether OVOL2 could inhibit stemness traits of breast cancer cell MDA-MB-231. Results The expression level of OVOL2 in basal/TNBC was significantly lower than that of other subtypes. Survival analyses indicated that high expression of OVOL2 was associated with favorable prognosis. GO and KEGG pathway analyses for upregulated and downregulated DEGs were conducted. The top three clusters of downregulated DEGs showed that tight junction and chemokines may play important roles in BCSCs. OVOL2 is one module of clusters. OVOL2 expression is correlated with various immune cells infiltration. Experiments showed that OVOL2 suppresses CD44 + /CD24 − ratio and mammospheres formation of MDA-MB-231. Conclusion OVOL2 may play an important role in the regulation of breast cancer stemness and immune cell infiltration, and is likely to be a target for the treatment of breast cancer.