The effects of adipose-derived stem cells on CD133-expressing bladder cancer cells.
Małgorzata MajAnna KokochaAnna BajekTomasz DrewaPublished in: Journal of cellular biochemistry (2019)
Mesenchymal stem cells (MSCs) hold great promise as therapeutic agents in regenerative medicine. They are also considered as a preferred cell source for urinary tract reconstruction. However, as MSCs exhibit affinity to tumor microenvironment, possible activation of tumor-initiating cells remains a major concern in the application of stem cell-based therapies for patients with a bladder cancer history. To analyze the influence of adipose-derived stem cells (ASCs) on bladder cancer cells with stem cell-like properties, we isolated CD133-positive bladder cancer cells and cultured them in conditioned medium from ASCs (ASC-CM). Our results showed that parental 5637 and HB-CLS-1 cells showed induced clonogenic potential when cultured in ASC-CM. Soluble mediators secreted by ASCs increased proliferation and viability of unsorted cells as well as CD133+ and CD133- subpopulations. Furthermore, incubation with ASC-CM modulated activation of intracellular signaling pathways. Soluble mediators secreted by ASCs increased phosphorylation of AKT1/2/3 (1.4-fold, P < 0.05), ERK1/2 (1.6-fold, P < 0.02), and p70 S6K (1.4-fold) in CD133+ cells isolated from 5637 cell line. In turn, decreased phosphorylation of those three proteins involved in PI3K/Akt and MAPK signaling was observed in CD133+ cells isolated from HB-CLS-1 cell line. Our results revealed that bladder cancer stem-like cells are responsive to signals from ASCs. Paracrine factors secreted by locally-delivered ASCs may, therefore, contribute to the modulation of signaling pathways involved in cancer progression, metastasis, and drug resistance.
Keyphrases
- induced apoptosis
- signaling pathway
- cell cycle arrest
- pi k akt
- stem cells
- mesenchymal stem cells
- cell death
- endoplasmic reticulum stress
- cell proliferation
- urinary tract
- epithelial mesenchymal transition
- machine learning
- endothelial cells
- oxidative stress
- spinal cord injury
- single cell
- risk assessment
- bone marrow
- mass spectrometry
- deep learning
- artificial intelligence
- human health
- big data
- protein kinase