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. 2023 Jun 9:29:1610980.
doi: 10.3389/pore.2023.1610980. eCollection 2023.

Tumor subtypes and signature model construction based on chromatin regulators for better prediction of prognosis in uveal melanoma

Yue Li  1   2   3   4 , Chao Xiong  1   2   3   4 , Li Li Wu  1   2   3   4 , Bo Yuan Zhang  1   2   3   4 , Sha Wu  1   2   3   4 , Yu Fen Chen  1   2   3   4 , Qi Hua Xu  1   2   3   4 , Hong Fei Liao  1   2   3   4
Affiliations

Tumor subtypes and signature model construction based on chromatin regulators for better prediction of prognosis in uveal melanoma

Yue Li et al. Pathol Oncol Res. .

Abstract

Background: Uveal Melanoma (UM) is the most prevalent primary intraocular malignancy in adults. This study assessed the importance of chromatin regulators (CRs) in UM and developed a model to predict UM prognosis. Methods: Gene expression data and clinical information for UM were obtained from public databases. Samples were typed according to the gene expression of CRs associated with UM prognosis. The prognostic key genes were further screened by the protein interaction network, and the risk model was to predict UM prognosis using the least absolute shrinkage and selection operator (LASSO) regression analysis and performed a test of the risk mode. In addition, we performed gene set variation analysis, tumor microenvironment, and tumor immune analysis between subtypes and risk groups to explore the mechanisms influencing the development of UM. Results: We constructed a signature model consisting of three CRs (RUVBL1, SIRT3, and SMARCD3), which was shown to be accurate, and valid for predicting prognostic outcomes in UM. Higher immune cell infiltration in poor prognostic subtypes and risk groups. The Tumor immune analysis and Tumor Immune Dysfunction and Exclusion (TIDE) score provided a basis for clinical immunotherapy in UM. Conclusion: The risk model has prognostic value for UM survival and provides new insights into the treatment of UM.

Keywords: TCGA; chromatin regulators; prognosis; tumor subtypes; uveal melanoma.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Typing and Identification of CRs-based Subtypes: (A) The cluster heatmap of tumor subtypes. (B) PCA showed a significant difference between the two subtypes. (C) Survival curves of overall survival for patients with two subtypes. (D) Survival curves of progression-free survival for patients with two subtypes.
FIGURE 2
FIGURE 2
Different characteristics between two subtypes. (A) The heatmap of expression levels of prognosis-related CRs genes and clinical traits in the two subgroups. (B) Percentage distribution of clinical traits with significant differences between the two subtypes. (C) The ssGSEA analysis of the immune cell infiltration level in the two subgroups. (D) Difference analysis of TME scores. (E) KEGG pathway analyses of GSVA in the two subgroups. (F) Boxplot of the abundance of immune cells in the two subgroups. **p < 0.01, ***p < 0.001.
FIGURE 3
FIGURE 3
Screening of key CRs genes and construction of risk model. (A) The PPI network of prognostic CRs genes. (B) The names and number of nodes of the top 40 genes in the PPI network. (C) Coefficient curve. Different colors represent different genes. (D) The minimum lambda value of the lasso model, λ = 5, was determined at the minimum deviation from the partial likelihood, and five genes were available for the analysis.
FIGURE 4
FIGURE 4
Upper: Kaplan–Meier survival analyses based on the risk model. Bottom: 1 -, 3-, and 5-year ROC analyses based on the risk model. Overall survival of patients in the TCGA training group (A), the internal validation group of the TCGA testing group (B), the entire TCGA dataset (C), and the independent dataset GSE84976 (F). Progression-free survival of patients in TCGA dataset (D) and the independent dataset GSE22138 (E).
FIGURE 5
FIGURE 5
Prognostic assessment efficiency comparison of the ROC curve and construction of the nomogram based on risk score and clinical factors. (A) ROC curve analysis at 3 years. (B) The nomogram to predict the 1-, 2- and 3-year survival risk of UM patients. (C) Calibration curve for the 1-, 2-, and 3-year predicted survival nomogram. *p < 0.05, ***p < 0.001.
FIGURE 6
FIGURE 6
Tumor microenvironment and immune-related analysis in two groups. (A) KEGG pathway analyses of GSVA in the two groups. (B) Difference analysis of TME scores in two groups. (C) The ssGSEA analysis of the immune cell infiltration level in the two subgroups. (D) Comparison of immune cell infiltration in two groups. (E) Correlation of risk score and 3 risk model-related genes with the expression of immune checkpoints. (F) Comparison of the expression of immune checkpoints in two groups. (G) Difference analysis of TIDE score in two groups. *p < 0.05, **p < 0.01, ***p < 0.001.
FIGURE 7
FIGURE 7
Analysis of signature genes in the risk model. (A) Sankey diagram of the two subgroups, two risk groups, and two clinical outcomes. (B) Differences in the risk scores between the two subgroups. (C) Differential expression of three signature genes in subtypes. (D,E) Differential expression of three signature genes in risk groupings for datasets GSE22138 and GSE84976. (F) Differential expression of three signature genes in the metastasis subgroup of GSE22138. *p < 0.05, **p < 0.01, ***p < 0.001.
FIGURE 8
FIGURE 8
Survival analysis of signature genes in the risk model. Red represents patients in the high gene expression group and blue represents patients in the low-risk group. The x-axis represents the survival time and the y-axis represents the survival rate. (A–D) Survival analysis of OS and PFS in the RUVBL1 expression subgroup. (E–H) Survival analysis of OS and PFS in the SIRT3 expression subgroup. (I–L) Survival analysis of OS and PFS in the SMARCD3 expression subgroup.
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