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Identification of Prognostic Signatures for Predicting the Overall Survival of Uveal Melanoma Patients.

ABSTRACT: Uveal melanoma (UM) is an aggressive cancer which has a high percentage of metastasis and with a poor prognosis. Identifying the potential prognostic markers of uveal melanoma may provide information for early detection of metastasis and treatment. In this work, we analyzed 80 uveal melanoma samples from The Cancer Genome Atlas (TCGA). We developed an 18-gene signature which can significantly predict the prognosis of UM patients. Firstly, we performed a univariate Cox regression analysis to identify significantly prognostic genes in uveal melanoma (P<0.01). Then the glmnet Cox analysis was used to generate a powerful prognostic gene model. Further, we established a risk score formula for every patient based on the 18-gene prognostic model with multivariate Cox regression. We stratified patients into high- and low-risk subtypes with median risk score and found that patients in high-risk group had worse prognosis than patients in low-risk group. Multivariate Cox regression analysis demonstrated that 18-gene model risk score was independent of clinical prognostic factors. We identified four genes whose mutations were closely to UM patients' prognosis or risk score. We also explored the relationship between copy number variation and risk score and found that high risk group showed more chromosome aberrations than low risk group. Gene Set Enrichment Analysis (GSEA) analysis showed that the different biological pathways and functions between low and high risk group. In summary, our findings constructed an 18-gene signature for estimating overall survival (OS) of UM. Patients were categorized into two subtypes based on the risk score and we found that high risk group showed more chromosome aberrations than low risk group.

SUBMITTER: Xue M

PROVIDER: S-EPMC6775505 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of Prognostic Signatures for Predicting the Overall Survival of Uveal Melanoma Patients.

Xue Meijuan M Shang Jun J Chen Binglin B Yang Zuyi Z Song Qian Q Sun Xiaoyan X Chen Jianing J Yang Ji J

Journal of Cancer 20190827 20

Uveal melanoma (UM) is an aggressive cancer which has a high percentage of metastasis and with a poor prognosis. Identifying the potential prognostic markers of uveal melanoma may provide information for early detection of metastasis and treatment. In this work, we analyzed 80 uveal melanoma samples from The Cancer Genome Atlas (TCGA). We developed an 18-gene signature which can significantly predict the prognosis of UM patients. Firstly, we performed a univariate Cox regression analysis to iden ...[more]

PMID: 31598164

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Project description: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.

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: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 Prognostic Signatures for Predicting the Overall Survival of Uveal Melanoma Patients.

Publications

Identification of Prognostic Signatures for Predicting the Overall Survival of Uveal Melanoma Patients.

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