Dataset Information

Identification of differentially expressed metastatic genes and their signatures to predict the overall survival of uveal melanoma patients by bioinformatics analysis.

ABSTRACT: AIM:To identify metastatic genes and miRNAs and to investigate the metastatic mechanism of uveal melanoma (UVM). METHODS:GSE27831, GSE39717, and GSE73652 gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database, and the limma R package was used to identify differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool. A comprehensive list of interacting DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. The Cytoscape MCODE plug-in was used to identify clustered sub-networks and modules of hub genes from the protein-protein interaction network. GEPIA online software was used for survival analysis of UVM patients (n=80) from the The Cancer Genome Atlas (TCGA) cohort. OncomiR online software was used to find that the miRNAs were associated with UVM prognosis from the TCGA cohort. TargetScan Human 7.2 software was then used to identify the miRNAs targeting the genes. RESULTS:There were 1600 up-regulated genes and 1399 down-regulated genes. The up-regulated genes were mainly involved in protein translation in the cytosol, whereas the down-regulated genes were correlated with extracellular matrix organization and cell adhesion in the extracellular space. Among the 2999 DEGs, five genes, Znf391, Mrps11, Htra3, Sulf2, and Smarcd3 were potential predictors of UVM prognosis. Otherwise, three miRNAs, hsa-miR-509-3-5p, hsa-miR-513a-5p, and hsa-miR-1269a were associated with UVM prognosis. CONCLUSION:After analyzing the metastasis-related enriched terms and signaling pathways, the up-regulated DEGs are mainly involved in protein synthesis and cell proliferation by ribosome and mitogen-activated protein kinase (MAPK) pathways. However, the down-regulated DEGs are mainly involved in processes that reduced cell-cell adhesion and promoted cell migration in the extracellular matrix through PI3K-Akt signaling pathway, focal adhesion, and extracellular matrix-receptor interactions. Bioinformatics and interaction analysis may provide new insights on the events leading up to the development and progression of UVM.

SUBMITTER: Zhao DD

PROVIDER: S-EPMC7321956 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of differentially expressed metastatic genes and their signatures to predict the overall survival of uveal melanoma patients by bioinformatics analysis.

Zhao Dan-Dan DD Zhao Xin X Li Wen-Tao WT

International journal of ophthalmology 20200718 7

<h4>Aim</h4>To identify metastatic genes and miRNAs and to investigate the metastatic mechanism of uveal melanoma (UVM).<h4>Methods</h4>GSE27831, GSE39717, and GSE73652 gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database, and the limma R package was used to identify differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool. A ...[more]

PMID: 32685390

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Project description:BackgroundUveal melanoma (UM) represents the most common primary intra-ocular malignancy in adults. Up to 50% of the patients develop distant metastases within 10 years from diagnosis, with the liver as the most common site. Upon metastatization, life expectancy strongly reduces and immune checkpoint inhibitors that prove effective in cutaneous melanoma do not modify clinical outcome. To date, few studies have focused on deciphering the immunomodulatory features of metastatic UM microenvironment, and there are no prognostic models for clinical use. This highlights the urgent need to understand the delicate interplay between tumor and immune cells acting at the site of metastasis.MethodsWe collected a patient cohort comprising 21 metastatic UM patients. Hepatic and extra-hepatic UM metastasis samples were studied by multiplex immunofluorescence to assess the tumor immune cell composition. Quantitative analyses were performed to correlate immune cell densities with treatment response, metastasis site and patient survival.ResultsCompared to patients with progressive disease, those with controlled disease had a higher intra-tumoral/peritumoral ratio of CD8 + Granzyme B+ cells, higher density of intra-tumoral CD8+ cytotoxic T lymphocytes (CTL) and an increased percentage of UM cells in close proximity to T lymphocytes, reflecting a role of tumor-killing T cells in the disease. In liver metastases (LM), the intra-tumoral densities of CD163+ tumor-associated macrophages (TAM) and of total CD8+ T cells were higher than in extra-hepatic UM metastases, but the percentage of Granzyme B+ CTL was lower. Moreover, LM displayed more UM cells adjacent to both CTL and TAM, and also more T cells in proximity to TAM, all signs of an impaired immune response. The percentage of activated CTL within the tumor represented a prognostic indicator, as patients with a higher intra-tumoral percentage of CD8 + Granzyme B+ cells had the better outcome. A temptative Immunoscore was generated and proved capable to stratify patients with improved survival. Finally, CD4 + FoxP3+ T cells appeared a crucial population for response to immunotherapy.ConclusionThe results of this study underly the clinical relevance and functional importance of composition and localization of antitumor effector cells for the progression of UM metastasis.

Project description:ObjectivesUveal melanoma (UM) is the most common primary intraocular malignancy in adults, and immune infiltration plays a crucial role in the prognosis of UM. This study aimed to generate an immunological marker-based predictive signature for the overall survival (OS) of UM patients.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) was used to profile immune cell infiltration in 79 patients with UM from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate least absolute shrinkage and selection operator (LASSO) Cox regressions were used to determine the prognostic factors for UM and construct the predictive immunosignature. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were performed to evaluate the clinical ability and accuracy of the model. In addition, the predictive accuracy was compared between the immunosignature and the Tumor, Node, Metastasis (TNM) staging system of American Joint Committee on Cancer (AJCC). We further analyzed the differences in clinical characteristics, immune infiltrates, immune checkpoints, and therapy sensitivity between high- and low-risk groups characterized by the prognostic model.ResultsHigher levels of immune cell infiltration in UM were related to a lower survival rate. Matrix metallopeptidase 12 (MMP12), TCDD inducible poly (ADP-ribose) polymerase (TIPARP), and leucine rich repeat neuronal 3 (LRRN3) were identified as prognostic signatures, and an immunological marker-based prognostic signature was constructed with good clinical ability and accuracy. The immunosignature was developed with a concordance index (C-index) of 0.881, which is significantly better than that of the TNM staging system (p < 0.001). We further identified 1,762 genes with upregulated expression and 798 genes with downregulated expression in the high-risk group, and the differences between the high- and low-risk groups were mainly in immune-related processes. In addition, the expression of most of the immune checkpoint-relevant and immune activity-relevant genes was significantly higher in the high-risk group, which was more sensitive to therapy.ConclusionWe developed a novel immunosignature constructed by MMP12, TIPARP, and LRRN3 that could effectively predict the OS of UM.

Dataset Information

Identification of differentially expressed metastatic genes and their signatures to predict the overall survival of uveal melanoma patients by bioinformatics analysis.

Publications

Identification of differentially expressed metastatic genes and their signatures to predict the overall survival of uveal melanoma patients by bioinformatics analysis.

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