January 01, 2018. doi:10.12123/npcd201801003
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Dan Zhang, Jiaru Luo, Yunzhi Ling*
All authors are from Biowed (China) Co., Ltd.
Keywords: Uveal melanoma; GEO database; DEGs; Go analysis; KEGG analysis
Received: September 01,2017 Accept: November 01,2017 published:January 01,2018
Uveal melanoma (UVM) is the most common malignant ocular tumor among adults, with the characteristics of strong infiltration, prone to distant metastasis, and poor prognosis after metastasis. Up until now, many studies suggested the metastasis of uveal melanoma is associated with multiple factors, such as the abnormalities of chromosomes, gene expressions and signaling pathways. In this study, to further understand the underlying mechanism and molecular basis of the UVM metastasis, we selected the datasets of GSE27831 from GEO database, and made a comparative analysis between the 11 cases of UVM metastasis samples and 17 cases of non-metastasis samples using the bioinformatics methods. According to the results of differentially expressed gene screening: compared with the non-metastasis samples, which were considered as control group, 46 up-regulated genes and 323 down-regulated gene existed in the UVM metastasis group. The GO results showed system development (GO:0048731, 1.05E-10), cell periphery (GO:0071944, 9.26E-08), brain-derived neurotrophic factor binding (GO:0048403, 1.12E-09), etc. were significantly enriched, and the hierarchical tree of GO category suggested motor activity (GO: 0003774, 9.14e-4), calmodulin binding (GO: 0005516, 2.070e-4), and cis-trans isomerase activity (GO: 0016859, 8.921e-5), etc., may associated with the UVM metastasis process. Meanwhile, the Kegg analysis showed that 369 DEGs were involved in 19 signaling pathways, including protein digestion and absorption, focal adhesion, ECM-receptor interaction, PI3K-Akt signaling pathway, p53 signaling pathway, etc. In addition, the network of protein- protein interaction indicated 129 nodes existed and multiple hub proteins were identified, including POMC, HIST2H2BE, CABP7, CACNA1A, NGFR, etc. Throughout the study, we were able to preliminarily explore the changes of gene expression profile of metastatic UVM, and hopefully we can generate more accurate UVM transcriptome profiles based on large sample size, and investigates some potential biomarkers and therapy targets for metastatic UVM in the future.