Investigating the Inhibitory Aspects of Metformin/Curcumin Co-Treatment through Convergence of In-Silico and In-Vitro Approaches

Abstract

Nearly 16% of people with breast cancer (BC) have Diabetes Mellitus type 2 (DM2) and are at a higher risk of death worldwide. Their common regulatory factors and functional mechanisms can be targeted applying multi-target drugs including Metformin (MTFN) and Curcumin (CURC). In this study, we used in-silico approaches to study the potential underlying mechanisms of this co-treatment strategy on BC and DM2 in order to introduce novel therapeutic targets.The total number of 48 shared differentially expressed genes (17 up-regulated and 31 down-regulated) were identified through establishing diseases' protein-protein network and BC RNA-sequencing expression data. The integration of functional clustering and pathway analyses revealed that the most involved cellular pathways and processes are regard to cells' proliferation, death, migration, and response to external stimulus. Afterwards, the MTFN/CURC correlation and co-treatment optimization was probed through response surface methodology (RSM) based on MCF7 cell line and confirmed by MDA-MB-231. Combination index calculation by MTT viability assay proved supportive effects on both cell lines. The superior apoptotic potential of co-treatment compared to single treatments was shown on inhibition of MCF7 proliferation and induction of cell death demonstrated by cell body co-staining and flow cytometry as well as gene expression analysis via RT-PCR. Furthermore, wound-healing scratch assay showed that this co-treatment has a slightly higher effect on migration inhibition compared to single treatments.In conclusion, our study used in-silico and in-vitro approaches and introduced a potential regulatory panel between BC and DM2. We also provided a linear model and equation that show the positive relation of drugs' co-treatment. The proposed co-treatment strategy successfully controlled the biological processes under investigation. METHODS Identification and validation of overlapped DEGs between BC and DM2The STRING disease network importer of Cytoscape 3.6 was used to establish the PPI networks of BC and DM2 with ultimate number of proteins (2000 nodes) and 0.7 as the minimum interaction score. The networks were merged together to find the overlapped factors and subsequently, filtered by BC differentially expressed genes (DEGs) of The Cancer Genome Atlas (TCGA) database. TCGA database contains the RNA-sequencing (RNA-seq) expression data of 33 different types of human cancers. In this study, BC RNA-seq raw data of 224 samples (112 cancerous tissues and 112 adjacent normal ones) was downloaded and normalized using TCGAbiolinks package of R v3.5.2 software. To identify the (DEGs) the criteria of FDR < 0.05 and |logFC| > 1 were applied. Functional Annotation Clustering and Pathway AnalysisTo understand the biological meaning behind the obtained DEGs in groups, Functional Annotation Clustering tool of David 6.7 database, based on Kappa statistics and fuzzy heuristic clustering algorithms, was used to make the ontology report easier to follow [23]. Each cluster contains Gene Ont...

WZB117 Decorated Metformin-Carboxymethyl Chitosan Nanoparticles for Targeting Breast Cancer Metabolism

“…MET, OCMC-MET, WZB117, and WZB117-OCMC-MET were tested for their effect on apoptosis using two distinct stains, acridine orange (AO) and ethidium bromide (EB) [ 42 ]. Cells were grown in T25 flasks with a final concentration of 2 × 10 4 cells/mL in complete DMEM media.…”