Prognostic significance of deregulated microRNAs in uveal melanomas

Luca Falzone, Giovanni L Romano, Rossella Salemi, Claudio Bucolo, Barbara Tomasello, Gabriella Lupo, Carmelina D Anfuso, Demetrios A Spandidos, Massimo Libra, Saverio Candido, Luca Falzone, Giovanni L Romano, Rossella Salemi, Claudio Bucolo, Barbara Tomasello, Gabriella Lupo, Carmelina D Anfuso, Demetrios A Spandidos, Massimo Libra, Saverio Candido

Abstract

Uveal melanoma (UM) represents the most frequent primary tumor of the eye. Despite the development of new drugs and screening programs, the prognosis of patients with UM remains poor and no effective prognostic biomarkers are yet able to identify high‑risk patients. Therefore, in the present study, microRNA (miRNA or miR) expression data, contained in the TCGA UM (UVM) database, were analyzed in order to identify a set of miRNAs with prognostic significance to be used as biomarkers in clinical practice. Patients were stratified into 2 groups, including tumor stage (high‑grade vs. low‑grade) and status (deceased vs. alive); differential analyses of miRNA expression among these groups were performed. A total of 20 deregulated miRNAs for each group were identified. In total 7 miRNAs were common between the groups. The majority of common miRNAs belonged to the miR‑506‑514 cluster, known to be involved in UM development. The prognostic value of the 20 selected miRNAs related to tumor stage was assessed. The deregulation of 12 miRNAs (6 upregulated and 6 downregulated) was associated with a worse prognosis of patients with UM. Subsequently, miRCancerdb and microRNA Data Integration Portal bioinformatics tools were used to identify a set of genes associated with the 20 miRNAs and to establish their interaction levels. By this approach, 53 different negatively and positively associated genes were identified. Finally, DIANA‑mirPath prediction pathway and Gene Ontology enrichment analyses were performed on the lists of genes previously generated to establish their functional involvement in biological processes and molecular pathways. All the miRNAs and genes were involved in molecular pathways usually altered in cancer, including the mitogen‑activated protein kinase (MAPK) pathway. Overall, the findings of the presents study demonstrated that the miRNAs of the miR‑506‑514 cluster, hsa‑miR‑592 and hsa‑miR‑199a‑5p were the most deregulated miRNAs in patients with high‑grade disease compared to those with low‑grade disease and were strictly related to the overall survival (OS) of the patients. However, further in vitro and translational approaches are required to validate these preliminary findings.

Figures

Figure 1.
Figure 1.
The overall survival (OS) of patients with uveal melanoma according to miRNA expression. (A) OS of patients with uveal melanoma according to the downregulation and upregulation of the top 10 downregulated related miRNAs related to tumor stage; (B) OS of patients with uveal melanoma according to the downregulation and upregulation of the top 10 upregulated miRNAs related to tumor stage. Only Kaplan-Meier estimates of OS with a log-rank test value of P

Figure 2.

Heatmap of the miRCancerdb correlation…

Figure 2.

Heatmap of the miRCancerdb correlation analysis. The upper side of the heatmap reports…

Figure 2.
Heatmap of the miRCancerdb correlation analysis. The upper side of the heatmap reports the 20 computationally selected miRNAs. In bold are reported the miRNAs with prognostic significance for the definition of the overall survival of patients with uveal melanoma. The miRNAs in common between tumor stage and vital status stratification are marked with an asterisk. On the left side of the heatmap all the genes shared and correlated with all the 20 miRNAs are listed. The green squares indicate a negative correlation, the red squares a positive correlation.

Figure 3.

mirDIP gene target analysis -…

Figure 3.

mirDIP gene target analysis - interaction between selected miRNAs and the 53 genes…

Figure 3.
mirDIP gene target analysis - interaction between selected miRNAs and the 53 genes identified through miRCancerdb. For each miRNA is reported the level of interaction with the 53 genes positively and negatively correlated. The intensity miRNA-gene interaction is highlighted with a color scale ranging from dark red (very high interaction) to yellow (low interaction).

Figure 4.

Gene Ontology enrichment analysis by…

Figure 4.

Gene Ontology enrichment analysis by PANTHER for the 53 genes identified through miRCancerdb.…

Figure 4.
Gene Ontology enrichment analysis by PANTHER for the 53 genes identified through miRCancerdb. (A) Distribution of genes according to molecular function; (B) Distribution of genes according to biological process; (C) Distribution of genes according to the type of cellular component; (D) Distribution of genes according to protein class; (E) Distribution of genes according to the analysis of pathway. Beside each category, the percentage of gene frequency was reported. The number of assigned genes may be greater than the number of recognized genes as the same gene can be included in different categories.

Figure 5.

Gene Ontology enrichment analysis by…

Figure 5.

Gene Ontology enrichment analysis by PANTHER for the 743 genes identified through DIANA-mir-Path.…

Figure 5.
Gene Ontology enrichment analysis by PANTHER for the 743 genes identified through DIANA-mir-Path. (A) Distribution of genes according to molecular function; (B) Distribution of genes according to biological process; (C) Distribution of genes according to the type of cellular component; (D) Distribution of genes according to protein class; (E) Distribution of genes according to the analysis of pathway. Beside each category, the percentage of gene frequency was reported. For each Ontology the 15 most represented categories are displayed. The number of assigned genes may be greater than the number of recognized genes as the same gene can be included in different categories.
Figure 2.
Figure 2.
Heatmap of the miRCancerdb correlation analysis. The upper side of the heatmap reports the 20 computationally selected miRNAs. In bold are reported the miRNAs with prognostic significance for the definition of the overall survival of patients with uveal melanoma. The miRNAs in common between tumor stage and vital status stratification are marked with an asterisk. On the left side of the heatmap all the genes shared and correlated with all the 20 miRNAs are listed. The green squares indicate a negative correlation, the red squares a positive correlation.
Figure 3.
Figure 3.
mirDIP gene target analysis - interaction between selected miRNAs and the 53 genes identified through miRCancerdb. For each miRNA is reported the level of interaction with the 53 genes positively and negatively correlated. The intensity miRNA-gene interaction is highlighted with a color scale ranging from dark red (very high interaction) to yellow (low interaction).
Figure 4.
Figure 4.
Gene Ontology enrichment analysis by PANTHER for the 53 genes identified through miRCancerdb. (A) Distribution of genes according to molecular function; (B) Distribution of genes according to biological process; (C) Distribution of genes according to the type of cellular component; (D) Distribution of genes according to protein class; (E) Distribution of genes according to the analysis of pathway. Beside each category, the percentage of gene frequency was reported. The number of assigned genes may be greater than the number of recognized genes as the same gene can be included in different categories.
Figure 5.
Figure 5.
Gene Ontology enrichment analysis by PANTHER for the 743 genes identified through DIANA-mir-Path. (A) Distribution of genes according to molecular function; (B) Distribution of genes according to biological process; (C) Distribution of genes according to the type of cellular component; (D) Distribution of genes according to protein class; (E) Distribution of genes according to the analysis of pathway. Beside each category, the percentage of gene frequency was reported. For each Ontology the 15 most represented categories are displayed. The number of assigned genes may be greater than the number of recognized genes as the same gene can be included in different categories.

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