PurposeUltraviolet radiation causes skin cancer, but the exact mechanism by which it occurs and the most effective methods of intervention to prevent it are yet unknown. For this purpose, our study will use bioinformatics and systems biology approaches to discover potential biomarkers of skin cancer for early diagnosis and prevention of disease with applicable clinical treatments.MethodsThis study compared gene expression and protein levels in ultraviolet-mediated cultured keratinocytes and adjacent normal skin tissue using RNA sequencing data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) database. Then, pathway analysis was employed with a selection of hub genes from the protein-protein interaction (PPI) network and the survival and expression profiles. Finally, potential clinical biomarkers were validated by receiver operating characteristic (ROC) curve analysis.ResultsWe identified 32 shared differentially expressed genes (DEGs) by analyzing three different subsets of the GSE85443 dataset. Skin cancer development is related to the control of several DEGs through cyclin-dependent protein serine/threonine kinase activity, cell cycle regulation, and activation of the NIMA kinase pathways. The cytoHubba plugin in Cytoscape identified 12 hub genes from PPI; among these 3 DEGs, namely, AURKA, CDK4, and PLK1 were significantly associated with survival ( P < 0.05) and highly expressed in skin cancer tissues. For validation purposes, ROC curve analysis indicated two biomarkers: AURKA (area under the curve (AUC) value = 0.8) and PLK1 (AUC value = 0.7), which were in an acceptable range.ConclusionsFurther translational research, including clinical experiments, teratogenicity tests, and in-vitro or in-vivo studies, will be performed to evaluate the expression of these identified biomarkers regarding the prognosis of skin cancer patients.

中文翻译：

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通过计算技术筛选紫外线介导的黑色素瘤的分子途径和预后生物标志物

目的紫外线辐射会导致皮肤癌，但其发生的确切机制以及预防其最有效的干预方法尚不清楚。为此，我们的研究将利用生物信息学和系统生物学方法来发现皮肤癌的潜在生物标志物，以便通过适用的临床治疗进行早期诊断和预防疾病。方法本研究比较了紫外线介导的培养角质形成细胞和邻近正常皮肤的基因表达和蛋白质水平使用来自国家生物技术信息中心基因表达综合 (NCBI-GEO) 数据库的 RNA 测序数据对组织进行分析。然后，对从蛋白质-蛋白质相互作用 (PPI) 网络中选择的中心基因以及存活和表达谱进行路径分析。最后，通过受试者工作特征 (ROC) 曲线分析来验证潜在的临床生物标志物。结果我们通过分析 GSE85443 数据集的三个不同子集，确定了 32 个共享的差异表达基因 (DEG)。皮肤癌的发展与通过细胞周期蛋白依赖性蛋白丝氨酸/苏氨酸激酶活性、细胞周期调节和 NIMA 激酶途径激活对多种 DEG 的控制有关。Cytoscape 中的 cytoHubba 插件从 PPI 中识别出 12 个 hub 基因；其中AURKA、CDK4和PLK1这3个DEG与生存显着相关（P<0.05），并且在皮肤癌组织中高表达。出于验证目的，ROC曲线分析表明两个生物标志物：AURKA（曲线下面积（AUC）值= 0.8）和PLK1（AUC值= 0.7），均在可接受的范围内。结论进一步的转化研究，包括临床实验、致畸性测试将进行体外或体内研究，以评估这些已识别的生物标志物在皮肤癌患者预后方面的表达。