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. 2020 Oct 15:10:568469.
doi: 10.3389/fonc.2020.568469. eCollection 2020.

Aberrant RNA Splicing Events Driven by Mutations of RNA-Binding Proteins as Indicators for Skin Cutaneous Melanoma Prognosis

Affiliations

Aberrant RNA Splicing Events Driven by Mutations of RNA-Binding Proteins as Indicators for Skin Cutaneous Melanoma Prognosis

Chao Mei et al. Front Oncol. .

Abstract

The worldwide incidence of skin cutaneous melanoma (SKCM) is increasing at a more rapid rate than other tumors. Aberrant alternative splicing (AS) is found to be common in cancer; however, how this process contributes to cancer prognosis still remains largely unknown. Mutations in RNA-binding proteins (RBPs) may trigger great changes in the splicing process. In this study, we comprehensively analyzed DNA and RNA sequencing data and clinical information of SKCM patients, together with widespread changes in splicing patterns induced by RBP mutations. We screened mRNA expression-related and prognosis-related mutations in RBPs and investigated the potential affections of RBP mutations on splicing patterns. Mutations in 853 RBPs were demonstrated to be correlated with splicing aberrations (p < 0.01). Functional enrichment analysis revealed that these alternative splicing events (ASEs) may participate in tumor progress by regulating the modification process, cell-cycle checkpoint, metabolic pathways, MAPK signaling, PI3K-Akt signaling, and other important pathways in cancer. We also constructed a prediction model based on overall survival-related AS events (OS-ASEs) affected by RBP mutations, which exhibited a good predict efficiency with the area under the curve of 0.989. Our work highlights the importance of RBP mutations in splicing alterations and provides effective biomarkers for prediction of prognosis of SKCM.

Keywords: RNA-binding protein; alternative splicing; mutation; prognosis; skin cutaneous melanoma.

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Figures

Figure 1
Figure 1
Flow diagram of the research methodology. RBP, RNA-binding protein; ASE, alternative splicing event; SF, splicing factor; PPI, protein–protein interaction.
Figure 2
Figure 2
RBP mutations triggered splicing event alterations. (A) Among the 853 RBPs whose mutations showed significant correlations with aberrant ASEs (Wilcoxon test P-values < 0.01). The mutation situations of the top 50 RBPs with the highest mutant frequency were exhibited. The y-axis represents each RBP and sorted by mutation frequency. The x-axis stands for different SKCM samples. The color indicates different mutation types. The bar plot above represents the mutation frequency with synonymous (red) and non-synonymous (blue) translational effects for each SKCM patient. (B) Upset plot of each splicing pattern affected by RBP mutations. The black bar on the left side indicates the event number of specific splicing type, and the red dots on the right side represent the intersections of ASEs. (C) Distribution of seven types of ASEs and their corresponding genes. The x-axis represents each kind of ASEs (AA, AD, AP, AT, ES, ME, RI), and the y-axis represents the number of ASEs (red) or the corresponding spliced genes (blue). (D) Overview of splicing events per RBP with the highest mutation frequency. The color intensity indicates the occurrence frequency of each splicing pattern, and the above histogram represented the counts of ASEs affected by corresponding RBPs.
Figure 3
Figure 3
RBP mutations induced mRNA expression alterations. (A) A total of 91 RBPs exhibit significant different mRNA expression levels induced by mutations (Wilcoxon test P-values < 0.05). The left and right panels indicate the up- and downregulation of the mRNA expression level of tested RBPs. The circle size corresponds to the mutation frequency of SKCM samples. (B) Upset plot showed splicing alterations induced by mRNA expression-associated RBP mutations. The black bar on the left represents the counts of different splicing patterns, while the red dots and lines on the right indicate the intersections of ASEs. (C) Bar graph represents the number of each splicing type as well as corresponding spliced genes. (D) Distribution of splicing events per RBP with mRNA expression-affecting mutations. The color intensity indicates the occurrence counts of each type of ASEs.
Figure 4
Figure 4
RBP mutations correlated with SKCM prognosis. (A) A total of 43 RBPs were demonstrated to carry prognosis-associated mutations (Kruskal–Wallis H test P-values < 0.05). The y-axis represents each RBP and sorted by mutation frequency. The x-axis stands for different SKCM samples. The color indicates different mutation type. The bar plot above represents the mutation frequency with synonymous (red) and non-synonymous (blue) translational effects of each sample. (B) High-risk factors (right panel) and low-risk factors (left panel) of prognosis-related RBP mutations. The circle size stands for count of samples with mutations.
Figure 5
Figure 5
Construction of the protein–protein interaction network and functional enrichment analysis. (A) Protein–protein interaction network of spliced genes affected by RBP mutations. Red dots represent each spliced gene. Line thickness represents the strength of interaction. (B–E) GO and KEGG analysis outcomes of RBPs and spliced genes. The top 20 most significant enriched pathways were exhibited in a bar plot. (B,C) Enriched pathways in GO analysis, (B) KEGG analysis, and (C) spliced genes regulated by RBPs. (D,E) GO, (D) KEGG, and (E) enrichment analysis outcomes of RBPs.
Figure 6
Figure 6
Overview of OS-ASEs affected by mRNA expression-related RBP mutations. (A) The volcano plot of all the ASEs. The red and blue dots represent OS-related and insignificant ASEs, respectively. (B) Upset plot of each type of OS-ASEs in SKCM. The black bar on the left side indicates the number of specific types of ASEs, while the red dots on the right side stand for the splicing intersections. (C) Distribution of seven types of ASEs and their corresponding genes. The x-axis represents each kind of ASEs (AA, AD, AP, AT, ES, RI), and the y-axis represents the number of ASEs (red) as well as the corresponding genes (blue). (D–J) Bubble plot of the most significant OS-ASEs in SKCM. (D) AA; (E) AD; (F) AP; (G) AT; (H) ES; (I) ME; (J) RI. The x-axis represents the z-score of each type of ASEs, while the y-axis stands for the OS-ASEs.
Figure 7
Figure 7
Correlation network of RBPs and OS-ASEs. The yellow dots represent RBPs. The blue and red dots indicate OS-ASEs that have been negatively regulated or positively regulated by the corresponding RBPs. The blue and red lines represent the existence of negative or positive regulation effects.
Figure 8
Figure 8
Prognostic model based on all types of OS-ASEs. (A) Cross-validation for tuning parameter selection in the proportional hazard model. (B) LASSO regression analysis for screening coefficients in all types of OS-ASEs. (C) The ROC curves for evaluating the efficiency of the prognostic model. (D) The risk curve of 93 SKCM patients matched with intact follow-up data. (E) The scatter plots of SKCM samples. The red and green plots represent alive and death endpoints, respectively. (F) Kaplan–Meier overall survival curves based on all types of OS-ASEs. The numbers of patients in the high-risk and low-risk groups at different survival times are listed at the bottom panel, respectively. (G) The heatmap of 10 OS-ASEs selected by LASSO regression analysis.
Figure 9
Figure 9
Cox regression analysis of prognosis-related clinical features and OS-ASEs. (A) Univariate Cox regression analysis and (B) multivariate Cox regression analysis.

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