Identification of potential biomarkers for melanoma cancer (black tumor) using bioinformatics strategy: a study based on GEO and SRA datasets

Abstract

Melanoma, a highly invasive type of skin cancer that penetrates the entire dermis layer, is associated with increased mortality rates. Excessive exposure of the skin to sunlight, specifically ultraviolet radiation, is the underlying cause of this malignant condition. The appearance of unique skin moles represents a visible clue, referred to as the "ugly duckling" sign, indicating the presence of melanoma and its association with cellular DNA damage. This research aims to explore potential biomarkers derived from microarray data, employing bioinformatics techniques and methodologies, for a thorough investigation of melanoma skin cancer. The microarray dataset for melanoma skin cancer was obtained from the GEO database, and thorough data analysis and quality control measures were performed to identify differentially expressed genes (DEGs). The top 14 highly expressed DEGs were identified, and their gene information and protein sequences were retrieved from the NCBI gene and protein database. These proteins were further analyzed for domain identification and network analysis. Gene expression analysis was conducted to visualize the upregulated and downregulated genes. Additionally, gene metabolite network analysis was carried out to understand the interactions between highly interconnected genes and regulatory transcripts. Molecular docking was employed to investigate the ligand-binding sites and visualize the three-dimensional structure of proteins. Our research unveiled a collection of genes with varying expression levels, some elevated and others reduced, which could function as promising biomarkers closely linked to the development and advancement of melanoma skin cancer. Through molecular docking analysis of the GINS2 protein, we identified two natural compounds (PubChem-156021169 and PubChem-60700) with potential as inhibitors against melanoma. This research has implications for early detection, treatment, and understanding the molecular basis of melanoma.

Keywords: Melanoma cancer; Microarray data analysis; Molecular docking; SRA data analysis.