Anatomy Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, 71491, Tabuk, Saudi Arabia.
J Appl Genet. 2024 Feb;65(1):83-93. doi: 10.1007/s13353-023-00794-4. Epub 2023 Oct 24.
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.
黑色素瘤是一种高度侵袭性的皮肤癌，可穿透整个真皮层，与死亡率增加有关。皮肤过度暴露于阳光，特别是紫外线辐射，是导致这种恶性疾病的根本原因。独特的皮肤痣的出现代表了一个可见的线索，称为“丑小鸭”标志，表明黑色素瘤的存在及其与细胞 DNA 损伤的关联。本研究旨在利用生物信息学技术和方法，从微阵列数据中探索潜在的生物标志物，对黑色素瘤皮肤癌进行全面研究。黑色素瘤皮肤癌的微阵列数据集从 GEO 数据库获得，并进行了彻底的数据分析和质量控制措施，以识别差异表达基因 (DEGs)。确定了前 14 个高表达的 DEGs，并从 NCBI 基因和蛋白质数据库中检索了它们的基因信息和蛋白质序列。这些蛋白质进一步进行了结构域识别和网络分析。进行基因表达分析以可视化上调和下调的基因。此外，还进行了基因代谢物网络分析，以了解高度相互连接的基因和调节转录物之间的相互作用。采用分子对接技术研究配体结合位点，并可视化蛋白质的三维结构。我们的研究揭示了一组具有不同表达水平的基因，有些上调，有些下调，这些基因可能作为与黑色素瘤皮肤癌发展和进展密切相关的有前途的生物标志物。通过对 GINS2 蛋白的分子对接分析，我们确定了两种具有抑制黑色素瘤潜力的天然化合物（PubChem-156021169 和 PubChem-60700）。这项研究对黑色素瘤的早期检测、治疗和理解分子基础具有重要意义。
