Mi-RNA-888-5p Is Involved in S-Adenosylmethionine Antitumor Effects in Laryngeal Squamous Cancer Cells.
Martina PaganoLaura MoscaFrancesca VitielloConcetta Paola IlissoAlessandra CoppolaLuigi BorzacchielloLuigi MeleFrancesca Pia CarusoMichele CeccarelliMichele CaragliaGiovanna CacciapuotiMarina PorcelliPublished in: Cancers (2020)
(1) Purpose: The methyl donor S-Adenosylmethionine (AdoMet) has been widely explored as a therapeutic compound, and its application-alone or in combination with other molecules-is emerging as a potential effective strategy for the treatment and chemoprevention of tumours. In this study, we investigated the antitumor activity of AdoMet in Laryngeal Squamous Cell Carcinoma (LSCC), exploring the underlying mechanisms. (2) Results: We demonstrated that AdoMet induced ROS generation and triggered autophagy with a consistent increase in LC3B-II autophagy-marker in JHU-SCC-011 and HNO210 LSCC cells. AdoMet induced ER-stress and activated UPR signaling through the upregulation of the spliced form of XBP1 and CHOP. To gain new insights into the molecular mechanisms underlying the antitumor activity of AdoMet, we evaluated the regulation of miRNA expression profile and we found a downregulation of miR-888-5p. We transfected LSCC cells with miR-888-5p inhibitor and exposed the cells to AdoMet for 48 and 72 h. The combination of AdoMet with miR-888-5p inhibitor synergistically induced both apoptosis and inhibited cell migration paralleled by the up-regulation of MYCBP and CDH1 genes and of their targets. (3) Conclusion: Overall, these data highlighted that epigenetic reprogramming of miRNAs by AdoMet play an important role in inhibiting apoptosis and migration in LSCC cell lines.
Keyphrases
- cell cycle arrest
- cell death
- induced apoptosis
- endoplasmic reticulum stress
- signaling pathway
- oxidative stress
- squamous cell carcinoma
- diabetic rats
- pi k akt
- high glucose
- cell migration
- cell proliferation
- dna methylation
- gene expression
- drug induced
- reactive oxygen species
- high grade
- machine learning
- mass spectrometry
- risk assessment
- climate change
- radiation therapy
- big data
- electronic health record
- lymph node metastasis
- transcription factor
- data analysis