Chaharlashkar Zeinab, Saeedi Honar Yousof, Abdollahpour-Alitappeh Meghdad, Parvizpour Sepideh, Barzegar Abolfazl, Alizadeh Effat
Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
Department of Plant Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran.
PLoS One. 2025 Jan 16;20(1):e0312754. doi: 10.1371/journal.pone.0312754. eCollection 2025.
Metastatic melanoma causes a high rate of mortality. We conducted an integrated analysis to identify critical regulators associated with the prognosis, pathogenesis, and targeted therapies of metastatic-melanoma. A microarray dataset, GSE15605, including 12 metastatic-melanoma and sixteen normal skin (NS) samples, were obtained from the GEO database. After exploration of DEGs of NS and metastatic-melanoma, identification of relevant transcription factors (TFs) and kinases, the Gene Ontology (GO), and pathways analyses of DEGs were performed. Protein-protein interaction (PPI) networks were evaluated by the STRING and Cytoscape. Subsequently, the hub genes were selected using GEPIA. Survival analysis was performed using the TCGA. To identify microRNA and lncRNA DEGs of the melanoma-associated genes miRwalk and FANTOM6 were employed. In metastatic-melanoma samples 285 and 1173 genes were up and down-regulated, respectively. The upregulated genes were mostly involved in granulocyte chemotaxis, positive regulation of calcium ion transmembrane transport, and melanin biosynthetic process. Five hub genes including CXCL11, ICAM1, LEF1, MITF, and STAT1 were identified, SUZ12, SOX2, TCF3, NANOG, and SMAD4 were determined as the most significant TFs in metastatic-melanoma. Furthermore, CDK2, GSK3B, CSNK2A1, and CDK1 target the highest amounts of genes associated with disease. The DGIdb analysis results show the match drugs for five hub genes. MiRNAs analysis revealed hsa-miR-181c-5p, hsa-miR-30b-3p, hsa-miR-3680-3P, hsa-miR-4659a-3p, hsa-miR-4687-3P, and hsa-miR-6808-3P could regulate the hub genes, whereas RP11-553K8.5 and SRP14-AS1 were identified as the top significant lncRNA. The items recognized in the current study can be used as potential biomarkers for diagnostic, predictive, and might helpful to develop targeted combined therapies.
转移性黑色素瘤导致高死亡率。我们进行了一项综合分析，以确定与转移性黑色素瘤的预后、发病机制和靶向治疗相关的关键调节因子。从基因表达综合数据库（GEO数据库）中获取了一个微阵列数据集GSE15605，其中包括12个转移性黑色素瘤样本和16个正常皮肤（NS）样本。在探索NS和转移性黑色素瘤的差异表达基因（DEGs）、鉴定相关转录因子（TFs）和激酶之后，进行了基因本体（GO）分析以及DEGs的通路分析。通过STRING和Cytoscape评估蛋白质-蛋白质相互作用（PPI）网络。随后，使用GEPIA选择枢纽基因。使用癌症基因组图谱（TCGA）进行生存分析。为了鉴定黑色素瘤相关基因的微小RNA（miRNA）和长链非编码RNA（lncRNA）差异表达基因，采用了miRwalk和FANTOM6。在转移性黑色素瘤样本中，分别有285个基因上调和1173个基因下调。上调的基因大多参与粒细胞趋化、钙离子跨膜转运的正调控以及黑色素生物合成过程。鉴定出包括CXCL11、ICAM1、LEF1、MITF和STAT1在内的5个枢纽基因，确定SUZ12、SOX2、TCF3、NANOG和SMAD4为转移性黑色素瘤中最显著的转录因子。此外，细胞周期蛋白依赖性激酶2（CDK2）、糖原合成酶激酶3β（GSK3B）、酪蛋白激酶2α1（CSNK2A1）和细胞周期蛋白依赖性激酶1（CDK1）靶向与该疾病相关的基因数量最多。药物基因相互作用数据库（DGIdb）分析结果显示了5个枢纽基因的匹配药物。miRNA分析表明，hsa-miR-181c-5p、hsa-miR-30b-3p、hsa-miR-3680-3P、hsa-miR-4659a-3p、hsa-miR-4687-3P和hsa-miR-6808-3P可以调节枢纽基因，而RP11-553K8.5和SRP14-AS1被确定为最显著的lncRNA。本研究中识别出的项目可用作潜在的生物标志物，用于诊断、预测，可能还有助于开发靶向联合疗法。
