Assessment of the Response of Human Umbilical Vein Endothelial Cells to Photodynamic Therapy: Highlighting the Role of Il-17 Signaling Pathway.
Mona Zamanian-AzodiBabak ArjmandMaryam Hamzeloo-MoghadamMostafa Rezaei TaviraniZahra RazzaghiAlireza AhmadzadehReza M RobatiMajid Rezaei TaviraniPublished in: Journal of lasers in medical sciences (2023)
Introduction: Photodynamic therapy (PDT) is a method based on the application of a photosensitive agent and the administration of light irradiation on the treated samples. PDT is applied as an effective tool with minimal side effects against tumor tissues. This study aimed to assess the targets of critical genes by PDT at the cellular level of cancer to provide a new perspective on its molecular mechanism. Methods: To assess the effect of PDT, we extracted the differentially expressed genes (DEGs) from the gene expression profiles of human umbilical vein endothelial cells (HUVECs) treated with PDT from Gene Expression Omnibus (GEO) databases. The queried DEGs were evaluated via a regulatory network and gene ontology enrichment to find the critical targets. Results: Among 76 queried significant DEGs, 27 individuals were interacted by activation, inhibition, and co-expression actions. Thirty DEGs were related to the five classes of biological terms. The IL-17 signaling pathway and PTGS2, CXCL8, FOS, JUN, CXCL1, ZFP36, and FOSB were identified as the crucial targets of PDT. Conclusion: PDT as a stimulator of gene expression and an activator of gene activity overexpressed and hyper-activated many genes. It seems that PDT introduces a number of genes and pathways that can be regulated by anticancer drugs to fight against cancers.
Keyphrases
- photodynamic therapy
- genome wide
- gene expression
- genome wide identification
- fluorescence imaging
- endothelial cells
- signaling pathway
- dna methylation
- bioinformatics analysis
- genome wide analysis
- transcription factor
- copy number
- poor prognosis
- pi k akt
- squamous cell carcinoma
- epithelial mesenchymal transition
- radiation therapy
- machine learning
- papillary thyroid
- deep learning
- induced apoptosis
- artificial intelligence
- childhood cancer