In Silico Evaluation of Biomarker Genes for Melanoma Carcinoma

ABSTRACTSMelanoma skin cancer is a primary cutaneous malignancy. Melanoma cancer of the skin cell is one of the major skin cancer worldwide, ranking first in frequency. Melanoma skin cancer has been strongly associated with psoriasis. Here, we use computational methods in an effort to identify possible biomarkers for psoriasis related Melanoma skin cancer. To do this, we downloaded gene expression microarray data from the GEO (Gene Expression Omnibus) database in the GSE series: GSE14905 and pre-processed it in the Bioconductor repository for R. The data was screened for DEGs using a rigorous methodology, which included the use of statistical testing methodologies and tools (Differentially Expressed Genes). Psoriasis has 6749 up-regulated genes and 7142 down-regulated genes. Psoriasis DEGs were combined with the NCG dataset resulting 874 up-regulated genes and 74 down-regulated genes for an in depth analysis of how differential expression can lead to malignancy. we used stringDB diseases dataset in Cytoscape to generate network of melanoma proteins, which were further mapped to DEGs and igraph to construct a GRN. In addition, module level analysis was carried out because of the benefits it provides in terms of stability and comprehension of intricate GRNs, resulting 17 biomarkers (17 up-regulated). There is an emphasis on the networks topology as well. The findings suggest that the network has a hierarchical structure. Additionally, survival analysis of 8 biomarkers results obtained from intersection mapping of skin cancer stringDB and biomarkers, was carried out. Significant enrichment of KEGG pathways was found. They also illuminate the interplay between biomarkers whose upregulation may lead to melanoma skin cancer. These findings may inform future investigations and the identification of potential therapeutic targets for this condition as well as shows the potential link between psoriasis and melanoma.