The Journal of international medical research · 2020
Identification of key genes involved in the pathogenesis of cutaneous melanoma using bioinformatics analysis.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chen Jianqin, Sun Wen, Mo Nian, Chen Xiangjun, Yang Lihong, Tu Shaozhong, Zhang Siwen, Liu Jing
Аннотация
OBJECTIVE: Malignant melanoma is a highly invasive cancer whose pathogenesis remains unclear. We analyzed the microarray dataset GDS1375 in the Gene Expression Omnibus database to search for key genes involved in the occurrence and development of melanoma.
METHODS: The dataset included 52 samples (7 normal skin and 45 melanoma samples). We identified differentially expressed genes (DEGs) between the two groups and used integrated discovery databases for Gene Ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) pathway analyses. In addition, we used the STRING and MCODE plugins of Cytoscape to visualize the protein-protein interactions (PPI) for these DEGs.
RESULTS: A total of 509 upregulated and 618 downregulated DEGs were identified, which were enriched in GO terms including integrin binding, protein binding, and structural constituent of cytoskeleton, and in KEGG pathways such as melanogenesis, prostate cancer, focal adhesion, and renin secretion. Three major modules from the PPI networks and 10 hub genes were identified, including CDC20, GNB2, PPP2R1A, AURKB, POLR2E, and AGTR1. Overall survival was low when these six hub genes were highly expressed.
CONCLUSION: This bioinformatics analysis identified hub genes that may promote the development of melanoma and represent potential new biomarkers for diagnosis and treatment of melanoma.
METHODS: The dataset included 52 samples (7 normal skin and 45 melanoma samples). We identified differentially expressed genes (DEGs) between the two groups and used integrated discovery databases for Gene Ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) pathway analyses. In addition, we used the STRING and MCODE plugins of Cytoscape to visualize the protein-protein interactions (PPI) for these DEGs.
RESULTS: A total of 509 upregulated and 618 downregulated DEGs were identified, which were enriched in GO terms including integrin binding, protein binding, and structural constituent of cytoskeleton, and in KEGG pathways such as melanogenesis, prostate cancer, focal adhesion, and renin secretion. Three major modules from the PPI networks and 10 hub genes were identified, including CDC20, GNB2, PPP2R1A, AURKB, POLR2E, and AGTR1. Overall survival was low when these six hub genes were highly expressed.
CONCLUSION: This bioinformatics analysis identified hub genes that may promote the development of melanoma and represent potential new biomarkers for diagnosis and treatment of melanoma.