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Repression of genes involved in melanocyte differentiation in uveal melanoma.

ABSTRACT: Uveal melanoma (UM) has been the subject of intense interest due to its distinctive metastatic pattern, which involves hematogenous dissemination of cancerous cells toward the liver in 50% of patients. To search for new UM prognostic markers, the Suppressive Subtractive Hybridization (SSH) technique was used to isolate genes that are differentially expressed between UM primary tumors and normal uveal melanocytes (UVM).A subtracted cDNA library was prepared using cDNA from uncultured UM primary tumors and UVM. The expression level of selected genes was further validated by cDNA microarray, semi-quantitative reverse transcription polymerase chain reaction (RT-PCR), and immunofluorescence analyses.One hundred-fifteen genes were identified using the SSH technique. Microarray analyses comparing the gene expression profiles of UM primary tumors to UVM validated a significant differential expression for 48% of these genes. The expression pattern of selected genes was then analyzed by semi-quantitative RT-PCR and was found to be consistent with the SSH and cDNA microarray findings. A down-regulation of genes associated with melanocyte differentiation was confirmed in UM primary tumors. Presence of undifferentiated cells in the UM was demonstrated by the expression of stem cell markers ATP-binding cassette sub-family G member 2 (ABCG2) and octamer-binding protein 4 (OCT4).We demonstrated that the SSH technique is efficient to detect differentially expressed genes between UM and UVM. The genes identified in this study represent valuable candidates for further functional analysis in UM and should be informative in studying the biology of this tumor. In addition, deregulation of the melanocyte differentiation pathway revealed the presence of UM cells exhibiting a stem cell-like phenotype.

SUBMITTER: Bergeron MA

PROVIDER: S-EPMC3398504 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Repression of genes involved in melanocyte differentiation in uveal melanoma.

Bergeron Marjorie-Allison MA Champagne Sophie S Gaudreault Manon M Deschambeault Alexandre A Landreville Solange S

Molecular vision 20120704

<h4>Purpose</h4>Uveal melanoma (UM) has been the subject of intense interest due to its distinctive metastatic pattern, which involves hematogenous dissemination of cancerous cells toward the liver in 50% of patients. To search for new UM prognostic markers, the Suppressive Subtractive Hybridization (SSH) technique was used to isolate genes that are differentially expressed between UM primary tumors and normal uveal melanocytes (UVM).<h4>Methods</h4>A subtracted cDNA library was prepared using c ...[more]

PMID: 22815634

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Project description:BackgroundUveal melanoma (UM) is the most common primary intraocular malignancy in adults. Many previous studies have demonstrated that the infiltrating of immune and stromal cells in the tumor microenvironment contributes significantly to prognosis.MethodsDataset TCGA-UVM, download from TCGA portal, was taken as the training cohort, and GSE22138, obtained from GEO database, was set as the validation cohort. ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to initial screen for potential prognostic genes. Then these genes entered into the validation cohort for validation using the same methods as that in the training cohort. Moreover, we conducted correlation analyses between the genes obtained in the validation cohort and the status of chromosome 3, chromosome 8q, and tumor metastasis to get prognosis genes. At last, the immune infiltration analysis was performed between the prognostic genes and 6 main kinds of tumor-infiltrating immune cells (TICs) for understanding the role of the genes in the tumor microenvironment.Results959 intersection DEGs were found in the UM microenvironment. Kaplan-Meier and Cox analysis was then performed in the training and validation cohorts on these DEGs, and 52 genes were identified with potential prognostic value. After comparing the 52 genes to chromosome 3, chromosome 8q, and metastasis, we obtained 21 genes as the prognostic genes. The immune infiltration analysis showed that Neutrophil had the potential prognostic ability, and almost every prognostic gene we had identified was correlated with abundances of Neutrophil and CD8+ T Cell.ConclusionsIdentifying 21 prognosis genes (SERPINB9, EDNRB, RAPGEF3, HFE, RNF43, ZNF415, IL12RB2, MTUS1, NEDD9, ZNF667, AZGP1, WARS, GEM, RAB31, CALHM2, CA12, MYEOV, CELF2, SLCO5A1, ISM1, and PAPSS2) could accurately identify patients' prognosis and had close interactions with Neutrophil in the tumor environment, which may provide UM patients with personalized prognosis prediction and new treatment insights.

Project description:BackgroundMelanoma is the deadliest type of skin cancer. Until now, its pathological mechanisms, particularly the mechanism of metastasis, remain largely unknown. Our study on the identification of genes in association with metastasis for melanoma provides a novel understanding of melanoma.MethodsFrom the Gene Expression Omnibus (GEO) database, the gene expression microarray datasets GSE46517, GSE7553, and GSE8401 were downloaded. We made use of R aiming at analyzing the differentially expressed genes (DEGs) between metastatic and nonmetastatic melanoma. R was also used in differentially expressed miRNA (DEM) data mining from GSE18509, GSE19387, GSE24996, GSE34460, GSE35579, GSE36236, and GSE54492 datasets referring to Li's study. Based on the DEG and DEM data, we performed functional enrichment analysis through the application of the DAVID database. Furthermore, we constructed the protein-protein interaction (PPI) network and established functional modules by making use of the STRING database. Through making use of Cytoscape, the PPI results were visualized. We predicted the targets of the DEMs through applying TargetScan, miRanda, and PITA databases and identified the overlapping genes between DEGs and predicted targets, followed by the construction of DEM-DEG pair network. The expressions of these keratinocyte differentiation-involved genes in Module 1 were identified based on the data from TCGA.Results239 DEGs were screened out in all 3 datasets, which were inclusive of 21 positively regulated genes and 218 negatively regulated genes. Based on these 239 DEGs, we finished constructing the PPI network which was formed from 225 nodes and 846 edges. We finished establishing 3 functional modules. And we analyzed 92 overlapping genes and 26 miRNA, including 11 upregulated genes targeted by 11 negatively regulated DEMs and 81 downregulated genes targeted by 15 positively regulated DEMs. As proof of the differential expression of metastasis-associated genes, eleven keratinocyte differentiation-involved genes, including LOR, EVPL, SPRR1A, FLG, SPRR1B, SPRR2B, TGM1, DSP, CSTA, CDSN, and IVL in Module 1, were obviously downregulated in metastatic melanoma tissue in comparison with primary melanoma tissue based on the data from TCGA.Conclusion239 melanoma metastasis-associated genes and 26 differentially expressed miRNA were identified in our study. The keratinocyte differentiation-involved genes may take part in melanoma metastasis, providing a latent molecular mechanism for this disease.

Dataset Information

Repression of genes involved in melanocyte differentiation in uveal melanoma.

Publications

Repression of genes involved in melanocyte differentiation in uveal melanoma.

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