Immune classification and identification of prognostic genes for uveal melanoma based on six immune cell signatures
Ontology highlight
ABSTRACT:
SUBMITTER: Gao G
PROVIDER: S-EPMC8593069 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundTo identify an immune-related prognostic signature and find potential therapeutic targets for uveal melanoma.MethodsThe RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The prognostic six-immune-gene signature was constructed through least absolute shrinkage and selection operator and multi-variate Cox regression analyses. Functional enrichment analysis and single sample GSEA were carried out. In addition, a nomogram model established by integrating clinical variables and this signature risk score was also constructed and evaluated.ResultsWe obtained 130 prognostic immune genes, and six of them were selected to construct a prognostic signature in the TCGA uveal melanoma dataset. Patients were classified into high-risk and low-risk groups according to a median risk score of this signature. High-risk group patients had poorer overall survival in comparison to the patients in the low-risk group (p < 0.001). These findings were further validated in two external GEO datasets. A nomogram model proved to be a good classifier for uveal melanoma by combining this signature. Both functional enrichment analysis and single sample GSEA analysis verified that this signature was truly correlated with immune system. In addition, in vitro cell experiments results demonstrated the consistent trend of our computational findings.ConclusionOur newly identified six-immune-gene signature and a nomogram model could be used as meaningful prognostic biomarkers, which might provide uveal melanoma patients with individualized clinical prognosis prediction and potential novel treatment targets.
| S-EPMC9809105 | biostudies-literature
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.
| S-EPMC7668584 | biostudies-literature
Project description:Currently, no autophagy-related long noncoding RNA (lncRNA) has been reported to predict the prognosis of uveal melanoma patients. Our study screened for autophagy-related lncRNAs in 80 samples downloaded from The Cancer Genome Atlas (TCGA) database through lncRNA-mRNA coexpression. We used univariate Cox to further filter the lncRNAs. Multivariate Cox regression and LASSO regression were applied to construct an autophagy-associated lncRNA predictive model and calculate the risk score. Clinical risk factors were validated using Cox regression to determine whether they were independent prognostic indicators. Functional enrichment was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The model was built with six predictive autophagy-associated lncRNAs and clustered uveal melanoma patients into high- and low-risk groups. The risk score of our model was a significant independent prognostic factor (hazard ratio = 1.0; p < 0.001). Moreover, these six lncRNAs were significantly concentrated in the biological pathways of cytoplasmic component recycling, energy metabolism, and apoptosis. Thus, the six autophagy-associated lncRNAs are potential molecular biomarkers and treatment targets for uveal melanoma patients.
| S-EPMC8379639 | biostudies-literature
Project description:ObjectiveCurrent advances in immunotherapy requires accurate tumor sub-classification due to the heterogeneity of lung adenocarcinoma (LUAD). This study aimed to develop a LUAD sub-classification system based on immune cell signatures and identified prognostic gene markers.MethodsSignatures related to the prognosis of TCGA-LUAD and 4 GSE cohorts were screened and intersected from 184 previously published immune cell signatures. The LUAD samples in the TCGA were clustered by ConsensusClusterPlus. Molecular characteristics, immune characteristics and sensitivity to immunotherapies/chemotherapies were compared. LDA score was established through Linear Discriminant Analysis (LDA). Co-expression module was constructed by Weighted Gene Co-Expression Network Analysis (WGCNA).ResultsFour LUAD subtypes with different molecular and immune characteristics were identified. Significant differences in prognosis among the four subtypes were observed. The IS1 subtype with the worst prognosis showed the highest number of TMB, mutant genes, IFN γ score, angiogenesis score and immune score. Twenty co-expression modules were generated by WGCNA. Blue module, sky blue module and light yellow module were significantly correlated with LUAD prognosis. The hub genes (CCDC90B, ARNTL2, RIPK2, SMCO2 and ADA and NBN) showing great prognostic significance were identified from the blue module. A total of 8 hub genes (NLRC3, CLEC2D, GIMAP5, CXorf65, PARP15, AKNA, ZC3H12D, and ARRDC5) were found in the light yellow module. Except for CXorf65, the expression of the other seven genes were significantly correlated with LUAD prognosis.ConclusionThis study determined four LUAD subtypes with different molecular and immune characteristics and 13 genes closely related to the prognosis of LUAD. The current findings could help understand the heterogeneity of LUAD immune classes.
| S-EPMC9005848 | biostudies-literature
Project description: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.
| S-EPMC8187912 | biostudies-literature
Project description: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.
| S-EPMC6775505 | biostudies-literature
Project description:Uveal melanoma (UM) is the most common primary intraocular cancer in adults. In the present study, we aimed to characterize the immunological features of primary UM cancer and to provide an association with prognostic markers and outcome. Also, we assessed the influence of the microenvironment on the expression of inhibitory immune checkpoints in UM. Genes of interest included MHC Class I and Class II molecules, as well as inhibitory immune-checkpoints, i.e. PDL1, PDL2, B7-H3, B7-H4, TBFRSF6B, CD47, CD155, GAL9, HVEM and CD200. We observed significant lower levels of MHC genes in UM cells as compared to normal uveal melanocytes. Unexpectedly however, the expression levels of most of the analyzed inhibitory immune-checkpoint genes were not different in cancer cells as compared to normal melanocytes, with the exception of CD200 and HVEM, that resulted significantly reduced. On the other hand, PDL1 inversely correlated with OS, PFS and thickness of the tumor. Also, PDL1, along with PDL2, expression significantly increased under inflammatory conditions. Finally, for the first time, we propose a possible role for CD47 in the immune evasive properties of UM. We show here that CD47 is significantly upregulated by UM cells following inflammatory stimuli and that it represents a good independent predictor of disease progression. The results from this study may propel advances in the development of immune-based therapies for UM patients.
| S-EPMC6336329 | biostudies-literature
Project description:The most common intraocular malignancy in adults remains uveal melanoma (UVM), and those with metastatic disease have a poor outlook. Proliferation, angiogenesis, and metastasis of tumor cells can be triggered by cuproptosis, affecting the survival of cancer patients. Nonetheless, cuproptosis-related genes (CRGs) have not been identified in UVM. In this study, we analyzed 10 CRGs in 80 patients with UVM in the Cancer Genome Atlas (TCGA) database regarding the alterations of the genes including copy number variation and methylation. We further constructed a prognostic gene model using these CRGs and built the risk score formula. Univariate and multivariate Cox regression was applied to validate the risk score as an independent prognostic factor. The prognostic model was validated using 63 UVM samples from the GSE22138 cohort, an independent validation data set. Based on the risk scores for 80 patients with UVM from TCGA, we categorized the patients into high- and low-risk groups. Differentially expressed genes (DEGs) between groups were enriched in allograft rejection, hypoxia, glycolysis, TNFα signaling via NF-κB, and interferon-γ responses via Gene set enrichment analysis (GSEA). CD8 T cells and exhausted T cells were notably enriched in the high-risk group. In conclusion, the alteration of CRGs is related to patients with UVM, and the constructed CRG-related model may be helpful to predict the prognosis of such patients.
| S-EPMC9468663 | biostudies-literature
Project description:Uveal melanoma (UVM) is the most common primary intraocular cancer in adults. Increasing evidence has demonstrated that immune cell infiltration (ICI) is crucial in predicting patient outcomes and therapeutic efficacy. Thus, describing the immune cell infiltrative landscape of UVM tumors may yield a novel prognostic marker and provide direction for immunotherapeutic selection. In this study, the gene expression data and clinical information of UVM patients were obtained from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. The ICI landscape of UVM was analyzed using the CIBERSORT and ESTIMATE algorithms. Two ICI phenotypes were defined, and the ICI scores were calculated by using principal component analysis algorithms. We found that a subtype with high ICI scores had poorer prognosis and increased expression levels of immune checkpoint-related genes. This study demonstrates that ICI scores are an independent prognostic biomarker and highlights their value in predicting immunotherapeutic outcomes.
| S-EPMC8427429 | biostudies-literature
Project description:Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. DNA repair genes play a vital role in cancer development. However, there has been very little research about DNA repair genes in UM. This study aimed to evaluate the importance of DNA repair genes and established a signature for predicting prognosis and immune features of UM. In this study, we mined TCGA database through bioinformatics analysis, and the intersect was taken between DNA repair genes and prognosis related genes and yielded 52 genes. We divided 80 UM patients into C1 and C2 subtypes. GSEA results indicated that abundant cancer-promoting functions and signaling pathways were activated in C2 subtype and the proportion of SNVs was higher in C2 than in C1 which suggested a worse prognosis. We built a six DNA repair genes model including ITPA, CETN2, CCNO, POLR2J, POLD1, and POLA1 by LASSO regression to predict prognosis of UM patients and utilized the median value of risk scores as the cutoff point to differentiate high risk and low risk group. The survival analyses and the receiver operating characteristic (ROC) curves in the validation group and entire data set confirmed the accuracy of this model. We also constructed a nomogram based on age and risk scores to evaluate the relationship between risk scores and clinical outcome. The calibration curve of the overall survival (OS) indicated that the performance of this model is steady and robust. Finally, the enrichment analysis showed that there were complex regulatory mechanisms in UM patients. The immune infiltration analysis indicated that the immune infiltration in C2 in the high risk group was different from that in the low risk group. Our findings indicated that the DNA repair genes may be related to UM prognosis and provide new insight into the underlying mechanisms.
| S-EPMC8791741 | biostudies-literature