EDC-Protein Network Formation in Uvea Melanoma; An Analysis of Melanoma Metastasis-Associated Genes

Abstract: Background: Melanoma is a kind of pigment cell cancer that affects the iris, ciliary body, or choroid of the eye (collectively referred to as the uvea). Tumors arise from pigment cells located inside the uvea that stain the eye. Metastasis of melanoma in the eye can damage a number of melanoma, such as the liver. Early diagnosis and treatment of melanoma can prevent possible problems, including decreased vision or complete loss of the eye. The most common manifestations of the disease are blurred vision, diplopia,  photopsia and proptosis. Material and Methods: First, the accession number GSE22138 was used to access the Gene Expression Omnibus at the National Center for Biotechnology Information (GEO). Then, 2000 metastatic and non-metastatic melanoma genes were extracted from the NCBI database together with their P-value. Then, by constructing the PPI network, we established ten modules for the genes with the highest expression levels. The comptox database was used to identify possible Endocrine Disrupting Chemicals (EDCs) for 17 high-expression genes. Cytoscape software was used to visualize the EDC-Protein network for these genes. Finally, we analyzed GO (Gene-Ontology) and molecular pathways using the DAVID database.  Result: In melanoma, 120 potential EDCs were identified to have regulatory effects on gene expression. We present oryzalin as a very effective EDC based on a comprehensive evaluation of various EDCs for metastatic Melanoma. Conclusion: Oryzalin is the EDC with the highest degree in our network. However, these results need to be experimentally confirmed to suggest improved prevention

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers

Cervical cancer (CC) is one of the world’s most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module’s genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module’s genes including MCM2, MCM10, POLA1, and TONSL and introduced as potential candidate biomarkers for SCC. In addition, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC