Integrative analysis identifies key genes related to metastasis and a robust gene-based prognostic signature in uveal melanoma.

Uveal melanoma (UM) is an aggressive intraocular malignancy, leading to systemic metastasis in half of the patients. However, the mechanism of the high metastatic rate remains unclear. This study aimed to identify key genes related to metastasis and construct a gene-based signature for better prognosis prediction of UM patients. Weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate, Lasso-penalized and multivariate Cox regression analyses were performed to establish a prognostic signature for UM patients. The tan and greenyellow modules were significantly associated with the metastasis of UM patients. Significant genes related to the overall survival (OS) in these two modules were then identified. Additionally, an OS-predicting signature was established. The UM patients were divided into a low- or high-risk group. The Kaplan-Meier curve indicated that high-risk patients had poorer OS than low-risk patients. The receiver operating curve (ROC) was used to validate the stability and accuracy of the final five-gene signature. Based on the signature and clinical traits of UM patients, a nomogram was established to serve in clinical practice. We identified key genes involved in the metastasis of UM. A robust five-gene-based prognostic signature was constructed and validated. In addition, the gene signature-based nomogram was created that can optimize the prognosis prediction and identify possible factors causing the poor prognosis of high-risk UM patients.

Lei S ，Zhang Y 《BMC Medical Genomics》

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

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

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》

Identification of survival-related genes and a novel gene-based prognostic signature involving the tumor microenvironment of uveal melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults and almost fifty percent of patients subsequently develop systemic metastases usually involving the liver. The tumor microenvironment (TME) is crucial to the initiation and progression of tumors. In the present study, we comprehensively evaluated the TME of primary UM samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database by using several bioinformatic algorithms. The prognostic value of immune score and infiltrating immune cells in the TME were evaluated. Differentially expressed genes between the low- and high-immune score groups were also identified. The majority of tumor-infiltrating lymphocytes in UM have been determined to be activated CD8 + T cells. Therefore, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules and genes significantly associated with the level of infiltrating CD8 + T cells in UM. Survival-related genes involved in the TME were identified by univariate Cox regression analysis. Furthermore, an eight-gene-based prognostic signature was established in the training dataset TCGA-UM via Lasso-penalized and multivariate Cox regression analyses. The predictive value of this signature was validated in two testing datasets. Besides, a nomogram was established to serve clinical practice. Moreover, hub genes involved in the infiltrating CD8 + T cells were identified and a potential targeted therapy for preventing metastasis of UM was proposed based on the results. In summary, our results provided a robust gene-based prognostic signature for predicting prognosis of UM patients and proposed a potential targeted therapy for preventing UM metastasis.

Lei S ，Zhang Y 《-》

Construction and validation of a novel prognostic signature for uveal melanoma based on five metabolism-related genes.

Zhao H ，Chen Y ，Shen P ，Gong L ... -  《-》