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

Abstract

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.

Prognostic Biomarkers in Uveal Melanoma: The Status Quo, Recent Advances and Future Directions

“…Finally, in a recently published study, Jun Liu et al proposed a novel six-gene based signature ( ARPC1B, BTBD6, GUSB, KRTCAP2, RHBDD3 and SLC39A4 ) for survival prediction and risk stratification in UM [ 155 ]. In brief, using the TCGA database, they initially found that glycolysis and immune response were the most relevant hallmarks for UM related survival [ 155 ].…”

“…Finally, in a recently published study, Jun Liu et al proposed a novel six-gene based signature ( ARPC1B, BTBD6, GUSB, KRTCAP2, RHBDD3 and SLC39A4 ) for survival prediction and risk stratification in UM [ 155 ]. In brief, using the TCGA database, they initially found that glycolysis and immune response were the most relevant hallmarks for UM related survival [ 155 ]. Subsequently, they employed WGCNA, Cox regression analyses and a LASSO algorithm to identify significant hub genes related to glycolysis and immune response, which were used to build the risk model to predict OS of UM patients [ 155 ].…”

“…In brief, using the TCGA database, they initially found that glycolysis and immune response were the most relevant hallmarks for UM related survival [ 155 ]. Subsequently, they employed WGCNA, Cox regression analyses and a LASSO algorithm to identify significant hub genes related to glycolysis and immune response, which were used to build the risk model to predict OS of UM patients [ 155 ]. The TCGA database constituted the training dataset, while the GEO databases GSE22138 and GSE84976 were used to validate the newly developed prognostic model [ 155 ].…”

“…Subsequently, they employed WGCNA, Cox regression analyses and a LASSO algorithm to identify significant hub genes related to glycolysis and immune response, which were used to build the risk model to predict OS of UM patients [ 155 ]. The TCGA database constituted the training dataset, while the GEO databases GSE22138 and GSE84976 were used to validate the newly developed prognostic model [ 155 ]. Survival analysis demonstrated that the OS of the group with high glycolysis and high immune response Z-scores was lower comparatively to the group with low glycolysis and low immune response Z-scores, respectively [ 155 ].…”

“…The TCGA database constituted the training dataset, while the GEO databases GSE22138 and GSE84976 were used to validate the newly developed prognostic model [ 155 ]. Survival analysis demonstrated that the OS of the group with high glycolysis and high immune response Z-scores was lower comparatively to the group with low glycolysis and low immune response Z-scores, respectively [ 155 ]. Regarding the immune profile, a higher infiltration of B cells, CD4+ T cells and monocytes was evident in the low-risk group, while the high-risk group had high infiltration by M2-macrophages and myeloid dendritic cells [ 155 ].…”