Construction and validation of a pyroptosis-related gene signature associated with the tumor microenvironment in uveal melanoma.

The present study aimed to construct a pyroptosis-related gene signature in uveal melanoma (UM) patients. Patients from The Cancer Genome Atlas (TCGA) served as the training cohort, whereas patients (GSE22138) from Gene Expression Omnibus (GEO) served as the validation cohort. Using the Kaplan-Meier (KM) method, univariate analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression, A five pyroptosis-related gene signature was constructed in the training cohort. Patients were divided into high- and low-risk groups. Survival analysis showed that patients in the high-risk group had a shorter survival time. Risk and survival analysis, time-independent receiver operating characteristic (ROC) curve analysis and principal component analysis (PCA) validated that the prognostic signature had greater predictive value in both cohorts. Multivariate analysis proved that the risk score was an independent prognostic factor. Functional analysis showed that the expressed genes in the high-risk group were most abundant in immunological repose-related and tumor-related signaling pathways. Single-sample gene-set enrichment analysis (ssGSEA) revealed that the different risk groups were associated with the tumor microenvironment. Moreover, the predictive signature could help patients be better matched to immunotherapy and targeted treatments. In conclusion, the pyroptosis-related gene signature associated with the tumor microenvironment maybe a reliable tool for predicting the prognosis of UM patients.

Zhang F ，Deng Y ，Wang D ，Wang S ... -  《Scientific Reports》

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》

An autophagy-related prognostic signature associated with immune microenvironment features of uveal melanoma.

Autophagy is involved in cancer initiation and progression but its role in uveal melanoma (UM) was rarely investigated. Herein, we built an autophagy-related gene (ARG) risk model of UM patients by univariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression model and filtrated out nine prognostic ARGs in The Cancer Genome Atlas (TCGA) cohort. Survival and Receiver Operating Characteristic (ROC) Curve analysis in the TCGA and other four independent UM cohorts (GSE22138, GSE27831, GSE44295 and GSE84976) proved that the ARG-signature possessed robust and steady prognosis predictive ability. We calculated risk scores for patients included in our study and patients with higher risk scores showed worse clinical outcomes. We found the expressions of the nine ARGs were significantly associated with clinical and molecular features (including risk score) and overall survival (OS) of UM patients. Furthermore, we utilized univariate and multivariate Cox regression analyses to determine the independent prognostic ability of the ARG-signature. Functional enrichment analysis showed the ARG-signature was correlated with several immune-related processes and pathways like T-cell activation and T-cell receptor signaling pathway. Gene set enrichment analysis (GSEA) found tumor hallmarks including angiogenesis, IL6-JAK-STAT3-signaling, reactive oxygen species pathway and oxidative phosphorylation were enriched in high-risk UM patients. Finally, infiltrations of several immune cells and immune-related scores were found significantly associated with the ARG-signature. In conclusion, the ARG-signature might be a strong predictor for evaluating the prognosis and immune infiltration of UM patients.

Zheng Z ，Zhang L ，Tu Z ，Deng Y ，Yin X ... -  《-》

Development and validation of an immune and stromal prognostic signature in uveal melanoma to guide clinical therapy.

Gong Q ，Wan Q ，Li A ，Yu Y ，Ding X ，Lin L ，Qi X ，Hu L ... -  《Aging-US》

A Novel 8-Gene Prognostic Signature for Survival Prediction of Uveal Melanoma.

Uveal melanoma (UM) has favorable local tumor control, but once metastasis develops, the prognosis is rather poor. Thus, it is urgent to develop metastasis predicting markers. Our study investigated a novel gene expression-based signature in predicting metastasis for patients with UM. In the discovery phase, 63 patients with UM from GEO data set GSE22138 were analyzed using the Weighted Correlation Network Analysis (WGCNA) to identify metastasis-related hub genes. The Least Absolute Shrinkage and Selection Operator (Lasso) Cox regression was used to select candidate genes and build a gene expression signature. In the validation phase, the signature was validated in The Cancer Genome Atlas database. Forty-one genes were identified as hub genes of metastasis by WGCNA. After the Lasso Cox regression analysis, eight genes including RPL10A, EIF1B, TIPARP, RPL15, SLC25A38, PHLDA1, TFDP2, and MEGF10 were highlighted as candidate predictors. The gene expression signature for UM (UMPS) could independently predict MFS by univariate and multivariate Cox regression analysis. Incorporating UMPS increased the AUC of the traditional clinical model. In the validation cohort, UMPS performed well in predicting the MFS of UM patients. UMPS, an eight-gene-based signature, is useful in predicting prognosis for patients with UM.

Tang Z ，Cai K 《-》