Bioinformatics study of genes related to skin cancer
Code: G-1360
Authors: Majid Mazhar ℗, Mohammad Taha Tousi, Salar Sabouri, Soheil Sabouri *
Schedule: Not Scheduled!
Tag: Cancer Diagnosis & Treatment
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Abstract:
Abstract
Background:Skin cancer is one of the most active types of cancer in the current decade. It is generally classified into two main categories: melanoma and non-melanoma skin cancer. Melanoma is a dangerous, rare and deadly skin cancer. Melanoma develops in cells called melanocytes. Various types of biologically active molecules can be seen in exosomes, which can be combined with them and transferred to target cells. Aims: The aim of this study was to bioinformatics study of gene expression and signaling pathways in skin cancer. Method and Results: To identify the differentially expressed genes, the combination of two criteria (2 Fold Change -2) and (Adj P-Value 0.05) was used. The obtained results were used to create a gene network. In this regard, to understand the molecular mechanism of communication between genes, the WGCNA package based on the R environment was used to identify hubs and gene modules in this disease. WGCNA is a valuable tool for correlation research between genes, identification of modules with high correlation and identification of Hub genes in different modules. The skin is often described as a protective layer because it is the body's first line of defense. Melanoma is the most dangerous cancer and is the main cause of 74% of deaths, even though it accounts for only 4% of all skin cancer cases. Based on the analyzes performed in skin cancer and different signaling pathways, we examined genes and signaling pathways that are identical to previous research. Conclusion: According to the investigations, it was observed that skin cancer has a significant relationship with the studied genes, and by increasing and decreasing the expression of genes, and by affecting the pathways of STAT3, PI3K, IL-22 and other investigated pathways, it induces, increases or decreases apoptosis in cancer.
Keywords
Skin cancer, LncRNA, mRNA, meta-analysis
