Investigation of mesenchymal stem cell secretome on breast cancer gene expression: A bioinformatic approach to identify differentially expressed genes, functional networks, and potential therapeutic targets
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Abstract
The mesenchymal stem cell (MSC) secretome plays a pivotal role in shaping the tumor microenvironment, influencing both cancer progression and potential therapeutic outcomes. In this research, by using publicly available dataset GSE196312, we investigated the role of MSC secretome on breast cancer cell gene expression. Our results raveled differentially expressed genes, including the upregulation of Phosphatidylinositol-3,4,5-Trisphosphate Dependent Rac Exchange Factor 1 (PREX1), C-C Motif Chemokine Ligand 28 (CCL28), and downregulation of Collagen Type I Alpha 1 Chain (COL1A1), Collagen Type I Alpha 3 Chain (COL1A3), Collagen Type III Alpha 1 Chain (COL3A1), which contributing to extra cellular matrix (ECM) weakening and promoting cell migration. Functional enrichment analyses also highlighted suppression of ECM remodeling pathways, and activation of calcium ion binding and Rap1 signaling pathway. We proposed that Ca2 + medicated activation of Ras-related protein 1 (Rap1) through its its downstream pathways such as Matrix Metalloprotease (MMP), PI3K/Akt, and MEK/ERK signaling pathway contribute to promotion of cell migration. However, the co-culture model by reducing Fibronectin 1 (FN1) and Secreted Protein Acidic and Cysteine Rich (SPARC) gene expression in cancer cells, emphasized on therapeutical aspects of MSC secretome. These findings emphasize on the dual edge sword nature of MSC secretome on cancer cell behaviors, while our major results emphasize on the cancer progression through ECM remodeling, the therapeutic aspects should not be underscored.
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Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered.
Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered.
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