Analysis of Ferroptosis-Mediated Modification Patterns and Tumor Immune Microenvironment Characterization in Uveal Melanoma

Abstract

Uveal melanoma (UVM) is an intraocular malignancy in adults in which approximately 50% of patients develop metastatic disease and have a poor prognosis. The need for immunotherapies has rapidly emerged, and recent research has yielded impressive results. Emerging evidence has implicated ferroptosis as a novel type of cell death that may mediate tumor-infiltrating immune cells to influence anticancer immunity. In this study, we first selected 11 ferroptosis regulators in UVM samples from the training set (TCGA and GSE84976 databases) by Cox analysis. We then divided these molecules into modules A and B based on the STRING database and used consensus clustering analysis to classify genes in both modules. According to the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), the results revealed that the clusters in module A were remarkably related to immune-related pathways. Next, we applied the ESTIMATE and CIBERSORT algorithms and found that these ferroptosis-related patterns may affect a proportion of TME infiltrating cells, thereby mediating the tumor immune environment. Additionally, to further develop the prognostic signatures based on the immune landscape, we established a six-gene-regulator prognostic model in the training set and successfully verified it in the validation set (GSE44295 and GSE27831). Subsequently, we identified the key molecules, including ABCC1, CHAC1, and GSS, which were associated with poor overall survival, progression-free survival, disease-specific survival, and progression-free interval. We constructed a competing endogenous RNA network to further elucidate the mechanisms, which consisted of 29 lncRNAs, 12 miRNAs, and 25 ferroptosis-related mRNAs. Our findings indicate that the ferroptosis-related genes may be suitable potential biomarkers to provide novel insights into UVM prognosis and decipher the underlying mechanisms in tumor microenvironment characterization.

Keywords: Uveal melanoma; ferroptosis; prognostic model; tumor immune environment; tumor-infiltrating immune cells.

FIGURE 1

Selection of prognostic regulators. (A) Venn plot of ferroptosis-related regulators. (B) Study flow chart.

FIGURE 2

Clustering of ferroptosis-related genes. (A) MCL clustering of ferroptosis-related genes. (B) Pearson correlation analysis among ferroptosis-related regulators in module A. (C) Consensus clustering cumulative distribution function (CDF) and relative change in area under CDF curve for k = 2 in module A. (D) Kaplan–Meier curves of two clusters in UVM about OS in module A. (E) The different expression of the ferroptosis-related mediators in two clusters based on module A.

FIGURE 3

Interaction of ferroptosis-related genes. (A) GO and KEGG analyses of DEGs in module A. (B) GSEA analysis of DEGs in module A. (C) GO and KEGG analysis of DEGs in module B.

FIGURE 4

Tumor immune microenvironment in module A. (A) The heatmap of ferroptosis-related regulators in two clusters. (B) The different expression of ESTIMATE score in two clusters. (C) The different distribution of 22 TME infiltrating cells in two patterns (***p < 0.001; **p < 0.01; *p < 0.05).

FIGURE 5

Prognostic analysis of risk model and ferroptosis-related genes. (A) The distributions of risk scores, alive/dead status, and expression of ferroptosis-related genes in module A. (B) Kaplan–Meier curves of patients in high/low risk about OS in module A. (C) Univariate (above) and multivariate analysis (below) of clinical characteristics. (D) ROC curve of risk score and clinical characteristics in module A. (E) Kaplan–Meier curves of patients in high/low risk about OS based on GSE84976.

FIGURE 6

Verification of risk model and ferroptosis-related genes. (A) Kaplan–Meier curves of patients in risk model about OS based on GSE44295. (B) Kaplan–Meier curves of patients in risk model about PFS based on GSE27831. (C) Kaplan–Meier OS curves for patients in key ferroptosis-related genes based on TCGA. (D) Kaplan–Meier OS curves for patients in key ferroptosis-related genes based on GSE84976. (E) Kaplan–Meier OS curves for patients in key ferroptosis-related genes based on GSE44295. (F) Kaplan–Meier PFS curves for patients in key ferroptosis-related genes based on GSE27831.

FIGURE 7

Construction of ceRNA network key genes. (A) Heatmap of differential expression lncRNAs. (B) Volcano plot of differential expression lncRNAs. (C) Heatmap of differential expression miRNAs. (D) Volcano plot of differential expression miRNAs. (E) Venn plot of target mRNAs and highly associated ferroptosis-related regulators. (F) ceRNA building of the 29 lncRNAs (light blue) and 12 miRNAs (dark blue) and 25 mRNAs (red).

FIGURE 8

Evaluation of key genes in immune landscape. (A) Relationship between ABCC1 expression and ESTIMATEScore, ImmuneScore, as well as StromalScore. (B) Relationship between CHAC1 expression and ESTIMATEScore, ImmuneScore, as well as StromalScore. (C) Relationship between GSS expression and ESTIMATEScore, ImmuneScore, as well as StromalScore. (D) GSEA analysis of ABCC1, CHAC1, and GSS in UVM. (E) Correlation among ABCC1, CHAC1, and GSS expression and immune-related mRNAs.