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

Abstract

Purpose 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. Methods 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. Results 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. Conclusions 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.

Machine Learning Methods for Gene Selection in Uveal Melanoma

“…Other genes such as PLXNB1 and HLA-A were part of an immune gene signature used to define two risk classes, one of which had higher immune cell infiltration and lower survival in the TCGA UVM dataset [35]. CTF1 and RAPGEF3 were previously reported to be parts of gene signatures related to tumor microenvironment and immune system [26,30], with the first seen downregulated and methylated in BAP1 mutated samples [36]; PALMD was found to have low expression in metastatic UM tissues [37], and GSTA3 in low-survival patients [38].…”

1,4-dihydroxy quininib activates ferroptosis pathways in metastatic uveal melanoma and reveals a novel prognostic biomarker signature

“…The signature could predict UM outcome related to mast cell resting [ 87 ]. Another study exploited weighted gene co-expression network analysis (WGCNA) to identify a prognostic signature including 5 genes ( EEFSEC, EEF1A2, ALDH1A3, CTNNB1 and COMMD2 ) [ 88 ]. More recently, GPX4 and SLC7A11 levels negatively correlated with UM survival, and ferroptosis susceptibility correlated with loss of BAP1 [ 27 ].…”

Identification and validation of immunogenic cell death-related score in uveal melanoma to improve prediction of prognosis and response to immunotherapy

“…These signatures had a tight relevance with a number of biological processes, such as immune cell infiltration [ 39 , 40 ], various forms of cell death [ 41 , 43 , 44 ], DNA methylation [ 42 ], epithelial–mesenchymal transition (EMT) [ 47 ], and metabolism [ 48 ]. Univariate Cox regression showed that only the ICDscore and four other signatures exhibited prognostic significance across all studied cohorts [ 47 , 59 – 61 ], indicating most signatures had a weak association with prognosis or had not been thoroughly validated ( Figure 5A ). Likewise, the ICDscore had stable performance across multiple cohorts and its average C-index was the highest ( Figure 5B – 5F ), exhibiting an advantage in predicting prognosis of UVM patients.…”