Abstract
Abstract
Purpose
The receptor-interacting protein kinase (RIPK4) has an oncogenic function in melanoma, regulates NF-κB and Wnt/β-catenin pathways, and is sensitive to the BRAF inhibitors: vemurafenib and dabrafenib which lead to its decreased level. As its role in melanoma remains not fully understood, we examined the effects of its downregulation on the transcriptomic profile of melanoma.
Methods
Applying RNA-seq, we revealed global alterations in the transcriptome of WM266.4 cells with RIPK4 silencing. Functional partners of RIPK4 were evaluated using STRING and GeneMANIA databases. Cells with transient knockdown (via siRNA) and stable knockout (via CRISPR/Cas9) of RIPK4 were stimulated with TNF-α. The expression levels of selected proteins were assessed using Western blot, ELISA, and qPCR.
Results
Global analysis of gene expression changes indicates a complex role for RIPK4 in regulating adhesion, migration, proliferation, and inflammatory processes in melanoma cells. Our study highlights potential functional partners of RIPK4 such as BIRC3, TNF-α receptors, and MAP2K6. Data from RIPK4 knockout cells suggest a putative role for RIPK4 in modulating TNF-α-induced production of IL-8 and IL-6 through two distinct signaling pathways—BIRC3/NF-κB and p38/MAPK. Furthermore, increased serum TNF-α levels and the correlation of RIPK4 with NF-κB were revealed in melanoma patients.
Conclusion
These data reveal a complex role for RIPK4 in regulating the immune signaling network in melanoma cells and suggest that this kinase may represent an alternative target for melanoma-targeted adjuvant therapy.
Publisher
Springer Science and Business Media LLC
Reference74 articles.
1. Adams S, Pankow S, Werner S, Munz B (2007) Regulation of NF-κB activity and keratinocyte differentiation by the RIP4 protein: implications for cutaneous wound repair. J Investig Dermatol 127(3):538–544. https://doi.org/10.1038/sj.jid.5700588
2. Anders S, Pyl PT, Huber W (2015) HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics (Oxford, England) 31(2):166–169. https://doi.org/10.1093/BIOINFORMATICS/BTU638
3. Andrews S (2010) FASTQC. A quality control tool for high throughput sequence data, Available online at http://www.bioinformatics.babraham.ac.uk/projects/fastqc
4. Anghel A-E, Ene C-D, Nicolae I, Budu VA, Constantin C, Neagu M (2015) Interleukin 8-major player in cutaneous melanoma metastatic process. Romanian Biotechnol Lett 20(6):10911
5. Arasu UT, Deen AJ, Pasonen-Seppänen S, Heikkinen S, Lalowski M, Kärnä R, Härkönen K, Mäkinen P, Lázaro-Ibáñez E, Siljander PRM, Oikari S, Levonen AL, Rilla K (2020) HAS3-induced extracellular vesicles from melanoma cells stimulate IHH mediated c-Myc upregulation via the hedgehog signaling pathway in target cells. Cell Mol Life Sci 77(20):4093–4115. https://doi.org/10.1007/S00018-019-03399-5