Identification and validation of a CCL18-related signature for prediction of overall survival in patients with uveal melanoma.

Uveal melanoma (UM), the most frequent primary intraocular tumor in adults, has poor prognosis. High C-C motif chemokine ligand 18 (CCL18) has been detected in various tumors and is closely correlated with patients' clinicopathological characteristics. However, the essential role of CCL18 in UM remains unclear. Therefore, this study aimed to explore the prognostic value of CCL18 in UM. Uveal melanoma cells (M17) were transfected with pcDNA3.1-CCL18 si-RNA using Lipofectamine™ 2000. Cell growth and invasion abilities were measured through Cell Counting Kit-8 assay and invasion assay. RNA expression data and clinical and histopathological details were downloaded from the UM in The Cancer Genome Atlas (TCGA-UM) and GSE22138 datasets, which were defined as the training and validation cohorts, respectively. Univariate and multivariate Cox regression analyses were performed to identify significant prognostic biomarkers. The coefficients of these significant biomarkers generated by multivariate Cox proportional hazard regression analysis were used to establish a risk score formula. Functional enrichment analyses were also carried out. We found that downregulated CCL18 inhibits M17 cell growth and invasion in vitro. CCL18 may affect UM progression by altering C-C motif receptor 8 related pathways. Higher CCL18 expression was associated with worse clinical outcomes and tumor-specific death in the TCGA-UM dataset. Based on the coefficients obtained from the Cox proportional hazard regression analysis, a CCL18-related prognostic signature formula was constructed as follows: risk score = 0.05590 × age +2.43437 × chromosome 3 status +0.39496 × ExpressionCCL18. Notably, in this formula, the normal chromosome 3 was coded as 0, whereas the chromosome 3 loss was coded as 1. Each patient was assigned to either low-risk or high-risk groups using the median cut-off in the training cohort. High-risk patients survived for a shorter time than low-risk patients. The time-dependent and multivariate receiver operating characteristic curves showed promising diagnostic efficacy. Multivariate Cox regression analysis demonstrated the potential of this CCL18-related signature as an independent prognostic indicator. These results were validated using the GSE22138 dataset. In addition, in both TCGA-UM and GSE22138 datasets, stratification of clinical correlations and survival analyses based on this signature indicated the involvement of clinical progression and survival outcome in UM. In the high-risk group, Gene Ontology analyses mainly indicated the enrichment of immune response pathways, such as the T cell activation, response to interferon-gamma, antigen processing and presentation, interferon-gamma-mediated signaling pathway, MHC protein complex, MHC class II protein complex, antigen binding, and cytokine binding. Meanwhile, Kyoto Encyclopedia of Genes and Genomes analyses showed enrichments of pathways in cancer, cell adhesion, cytokine-cytokine receptor interaction, chemokine signaling pathway, Th1 and Th2 cell differentiation, and chemokine signaling pathway. Moreover, single-sample gene set enrichment analysis demonstrated the enrichment of almost all immune cells and immune functions in the high-risk group. In summary, a new prognostic CCL18-related signature was successfully established using the TCGA-UM dataset and validated using the GSE22138 dataset with meaningful predictive and diagnostic efficacies. This signature could serve as an independent and promising prognostic biomarker for patients with UM.

Yang B ，Fan Y ，Chen M ，Tang L ，Tang X ，Li H ，Gu A ，Liang R ，Wu Y ... -  《-》

High EIF4E2 expression is an independent prognostic risk factor for poor overall survival and recurrence-free survival in uveal melanoma.

Uveal melanoma (UM), as the most common primary intraocular carcinoma, is a relatively rare but lethal tumor. Upregulated eukaryotic translation initiation factor 4E family member 2 (EIF4E2) promotes the progression of multiple human carcinomas. However, its role remains unclear in UM. To identify the prognostic value of EIF4E2 in UM, we downloaded RNA-sequencing data along with clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. EIF4E2 mRNA was significantly increased in three different subgroups in the TCGA-UM dataset. High mRNA expression was correlated with shorter overall survival (OS) and shorter recurrence-free survival (RFS). Moreover, we constructed a prognostic signature using Cox regression analyses in our training cohort TCGA-UM dataset as follows: risk score = 0.04335 × Age +0.49639 × expression of EIF4E2. Based on the risk score, each patient was classified as high-risk or low-risk. Additional survival analyses suggested that patients in the high-risk score group had an unfavorable OS compared with patients in the low-risk score group, which was validated in two external GEO datasets, including GSE84976 and GSE22138. Functional enrichment analysis demonstrated that UM was correlated with hypoxia-related functions. Gene set enrichment analysis (GSEA) indicated significant enrichments of the p53 and Notch pathways. In addition, EIF4E2 was genetically altered in 12.5% (10/80) of UM patients. Epigenetically, higher expression of cg03852847 was correlated with longer OS and longer RFS. In conclusion, our findings demonstrated that high EIF4E2 expression is an independent prognostic risk factor for UM patients. EIF4E2 might play an important role in hypoxia-related signaling pathways during UM progression. Both genetic and epigenetic alterations may contribute to UM pathogenesis. These findings could offer individualized clinical prognostication and potential novel treatment targets for UM patients.

Yang B ，Gu A ，Wu Y 《-》

A Comprehensive Prognostic and Immunological Analysis of a Six-Gene Signature Associated With Glycolysis and Immune Response in Uveal Melanoma.

Uveal melanoma (UM) is a subtype of melanoma with poor prognosis. This study aimed to construct a new prognostic gene signature that can be used for survival prediction and risk stratification of UM patients. In this work, transcriptome data from the Molecular Signatures Database were used to identify the cancer hallmarks most relevant to the prognosis of UM patients. Weighted gene co-expression network, univariate least absolute contraction and selection operator (LASSO), and multivariate Cox regression analyses were used to construct the prognostic gene characteristics. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to evaluate the survival predictive ability of the gene signature. The results showed that glycolysis and immune response were the main risk factors for overall survival (OS) in UM patients. Using univariate Cox regression analysis, 238 candidates related to the prognosis of UM patients were identified (p < 0.05). Using LASSO and multivariate Cox regression analyses, a six-gene signature including ARPC1B, BTBD6, GUSB, KRTCAP2, RHBDD3, and SLC39A4 was constructed. Kaplan-Meier analysis of the UM cohort in the training set showed that patients with higher risk scores had worse OS (HR = 2.61, p < 0.001). The time-dependent ROC (t-ROC) curve showed that the risk score had good predictive efficiency for UM patients in the training set (AUC > 0.9). Besides, t-ROC analysis showed that the predictive ability of risk scores was significantly higher than that of other clinicopathological characteristics. Univariate and multivariate Cox regression analyses showed that risk score was an independent risk factor for OS in UM patients. The prognostic value of risk scores was further verified in two external UM cohorts (GSE22138 and GSE84976). Two-factor survival analysis showed that UM patients with high hypoxia or immune response scores and high risk scores had the worst prognosis. Moreover, a nomogram based on the six-gene signature was established for clinical practice. In addition, risk scores were related to the immune infiltration profiles. Taken together, this study identified a new prognostic six-gene signature related to glycolysis and immune response. This six-gene signature can not only be used for survival prediction and risk stratification but also may be a potential therapeutic target for UM patients.

Liu J ，Lu J ，Li W 《Frontiers in Immunology》

Immunogenic profiling of metastatic uveal melanoma discerns a potential signature related to prognosis.

Wang J ，Liu M ，Sun J ，Zhang Z ... -  《-》

Identification of Survival-Related Metabolic Genes and a Novel Gene Signature Predicting the Overall Survival for Patients with Uveal Melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignancy among adults. Altered metabolism has been shown to contribute to the development of cancer closely, but the prognostic role of metabolism in UM remains to be explored. This study aimed to construct a metabolic-related signature for UM. We collected the mRNA sequencing data and corresponding clinical information from The Cancer Genome Atlas and Gene Expression Omnibus databases. A univariate Cox regression analysis, the Lasso-penalized Cox regression analysis, and multivariate Cox regression analyses were used to construct a metabolic signature based on TCGA. The time-dependent ROC and Kaplan-Meier survival curves were calculated to validate the prognostic ability of the signature. The immune-related features and mutation profile were characterized by CIBERSORT and maftools between high- and low-risk groups. A novel metabolic-related signature (risk score = -0.246*SLC25A38 - 0.50186*ABCA12 + 0.032*CA12 + 0.086*SYNJ2) was constructed to predict the prognosis of UM patients. In TCGA and GSE22138, the signature had high sensitivity and specificity in predicting the prognosis of UM patients (survival probability; p < 0.0001, p = 0.012) . Gene Ontology pathway enrichment analysis and GSEA were used to discriminate several significantly enriched metabolism-related pathways, including channel activity and passive transmembrane transporter activity, which may reveal the underlying mechanisms. The high-risk group had more immune cell infiltration and greater distribution of BAP1 mutations. Our study developed a robust metabolic-gene signature based on TCGA to predict the prognosis of UM patients. The signature indicates a dysregulated metabolic microenvironment and provides new metabolic biomarkers and therapeutic targets for UM patients.

Guo X ，Yu X ，Li F ，Xia Q ，Ren H ，Chen Z ，Xing Y ... -  《-》