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Identification of Precise Therapeutic Targets and Characteristic Prognostic Genes Based on Immune Gene Characteristics in Uveal Melanoma.

ABSTRACT: The tumor microenvironment is an important factor for the immunotherapy of tumor patients. The sequenced transcriptome data can be used to describe the tumor microenvironment and various immune subtypes. We exploited published data on patients with uveal melanoma (UVM) to identify immune subtypes. Based on the immune-related gene sets of 80 patients with UVM in the TCGA database, we used consensus clustering to identify two immune subgroups. In the two immune subtypes, we analyzed clinical characteristics and immune infiltration. Class1 has low immune infiltration, contains memory B cells, Th2 cells, Th17 cells, eosinophils, natural killer cells, and has a better prognosis. Class2 has higher immune infiltration. CD8+ T cells, Th1 cells, MDSCs, and Dendritic cells are enriched in class2, which has strong cytolytic activity, high expression of immune checkpoint genes, and poor outcome. Moreover, we have developed and verified an immune characteristic model that can predict the prognosis of patients well. Through this model, we screened prostaglandin-endoperoxide synthase 2 (PTGS2) as the therapeutic target of UVM. Treatment of choroidal melanoma cell line (OCM1) cells with celecoxib (an inhibitor of PTGS2) effectively inhibits cell growth, proliferation, and promotes apoptosis. Our results show the immunological heterogeneity of UVM patients and also provide an ideal therapeutic target for the future treatment design of patients.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC8187912 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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Identification of Precise Therapeutic Targets and Characteristic Prognostic Genes Based on Immune Gene Characteristics in Uveal Melanoma.

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