Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma
<p>Landscape of alternative splicing (AS) events in uveal melanoma (UM). AA, alternate acceptor; AD, alternate donor; AP, alternate promoter; AT, alternate terminator; ES, exon skip; ME, mutually exclusive exons; RI, retained intron. (<b>A</b>) Illustration for seven types of alternative splicing in this study. (<b>B</b>) The number of AS events and corresponding genes included in the present study; the x-axis stands for the types of alternative splicing, and y-axis means the number of genes and AS events. (<b>C</b>) UpSet plot of different types of alternative splicing types in UM. The dark bar on the left of drawing represents the amount of each type of AS event. The dark dots in the matrix at the right of drawing represent the intersections of AS events. One gene might possess several alternative splicing patterns (dark dot line), and even a single gene can undergo four types of alternative splicing (green dot line). ES was the most common type of the alternative splicing events (red bar).</p> "> Figure 2
<p>The 20 most significant AS events in UM. (<b>A</b>) The number of survival-related AS events and corresponding genes obtained by using univariable cox regression analysis. The x-axis stands for the types of alternative splicing, and the y-axis stands for the number of genes and AS events. (<b>B–H</b>) Forest plots of the top 20 significantly survival-related AS events for acceptor sites, the x-axis stands for z-scores, and the y-axis stands for survival-related AS events: (<b>B</b>) AA, alternate acceptors; (<b>C</b>) AD, alternate donors; (<b>D</b>) AP, alternate promoters; (<b>E</b>) ES, exon skip; (<b>F</b>) AT, alternate terminators; (<b>G</b>) ME, mutually exclusive exons; (<b>H</b>) RI, retained introns.</p> "> Figure 3
<p>Interaction network of survival-related alternative splicing (AS) events. (<b>A</b>) Protein–protein interaction network of genes with survival-related AS events in UM. (<b>B</b>) Gene ontology (GO) analysis of genes with survival-related AS events. The x-axis stands for gene ration in the background gene, and the y-axis stands for the term of pathway. (<b>C</b>) KEGG pathway analysis of genes with survival-related AS events. The x-axis stands for gene ratio in the background gene, and the y-axis stands for the term of pathway.</p> "> Figure 4
<p>Construction of seven splicing types of prognostic markers based on least absolute shrinkage and selection operator (LASSO) analysis. LASSO coefficient profiles of survival-related alternative splicing (AS) events: (<b>A</b>) AA, alternate acceptors; (<b>B</b>) AD, alternate donors; (<b>C</b>) AP, alternate promoters; (<b>D</b>) ES, exon skip; (<b>E</b>) AT, alternate terminators; (<b>F</b>) ME, mutually exclusive exons; (<b>G</b>) RI, retained introns. The lines stand for each survival-related AS events in seven splicing types respectively and candidate AS events were selected by using 10 fold cross-validation via minimum criteria; X-axis is stand for LASSO coefficient profiles of survival-related alternative splicing (AS) events. Y-axis means tuning parameter (lambda) selection in the Lasso regression.</p> "> Figure 5
<p>Kaplan–Meier plots and receiver operating characteristic (ROC) curves of predictive factors in UM cohort. (<b>A</b>–<b>G</b>) Kaplan–Meier curves of prognostic models built with alternative splicing (AS) events of alternate acceptor (AA), alternate donor (AD), alternate promoter (AP), alternate terminator (AT), exon skip (ES), retained intron (RI), and mutually exclusive exon (ME) splicing types for patients with UM. Time-dependent numbers at risk are listed at the middle panels and the number of censor patients are listed at the bottom panels. (<b>H</b>) The ROC curves of predictive models for each splicing type in UM.</p> "> Figure 6
<p>The final overall prognostic model in UM. (<b>A</b>–<b>C</b>) The process of building the signature containing all survival-related AS events and the coefficients calculated by LASSO method: (<b>A</b>) Partial likelihood distribution with the corresponding λ-logarithm value and the left variants of model (<b>B</b>) LASSO coefficient profiles of all survival-related alternative splicing (AS) events. A vertical line is drawn at the value chosen by 10-fold cross-validation. (<b>C</b>) The distribution of risk score, overall survival (OS) and life status for the 80 patients in UM. (<b>D</b>) Kaplan–Meier overall survival curves of the final prognostic model. Time-dependent numbers at risk are listed at the middle panel and the number of censor patients are listed at the bottom panel. (<b>E</b>) The ROC curves of predictive model for all splicing types in UM. (<b>F</b>) The expression values of 11 spliced genes in UM and healthy normal tissue (** <span class="html-italic">p</span> < 0.01, ns means no significance).</p> "> Figure 7
<p>Differential landscape of somatic mutation burden between high and low risk groups. (<b>A</b>) The waterfall plots of the top 20 mutant genes in high risk group. (<b>B</b>) The waterfall plots of top 20 mutant genes in low risk group. (<b>C</b>) Forestplot suggested that three genes GNAQ, SF3B1 and EIF1AX which are highly mutated in low-risk group compared to high-risk group (*** <span class="html-italic">p</span> < 0.001, ** <span class="html-italic">p</span> < 0.01, * <span class="html-italic">p</span> < 0.05). (D) Kaplan–Meier overall survival curves of four different mutated genes. The mutant of GNAQ and SF3B1 have a longer survival time than the wild type. With log-rank P = 0.043 and log-rank P = 0.007 respectively.</p> "> Figure 8
<p>Kaplan-Meier survival analysis for 18 survival-associated splicing factor genes of UM. Their expression levels were classified into two groups by median value. Blue, low-level group; red, high-level group.</p> "> Figure 9
<p>Survival-associated splicing factors and splicing correlation network in UM. (<b>A</b>) Splicing correlation network in UM patients constructed by Cytoscape. Eighteen survival-associated splicing factors (purple dots) were positively (red lines) or negatively (green lines) associated with AS events, which predicted good (green dots) or poor (red dots) outcomes in UM patients. (<b>B</b>) High RBM10 expression was significantly associated with poor overall survival in UM. Positive correlations between RBM10 expression and the Percent Spliced In (PSI) value of NEDD4L. (<b>C</b>) Low ZC3H18 expression was significantly associated with poor overall survival in UM. Negative correlations between ZC3H18 expression and the PSI value of CD47.</p> ">
Abstract
:1. Introduction
2. Material and Methods
2.1. Raw Data Process
2.2. Identification of Survival-Related Alternative Splicing Events
2.3. Construction of the Spliced Gene Correlation Network
2.4. LASSO Multivariate Cox Analysis
2.5. Construction of Splicing Factor Correlation Network
2.6. Statistical Analysis
3. Results
3.1. Alternative Splicing Events in UM
3.2. Identification of Survival-Related Alternative Splicing Events
3.3. Construction of Spliced Gene Correlation Network
3.4. LASSO Analysis Based on Selected Splicing Events
3.5. Construction of Splicing Factor Correlation Network
4. Discussion
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Actual Long-Term Survival | Actual Short-Term Survival | |
---|---|---|
Predicted long-term survival | 38 | 0 |
Predicted short-term survival | 2 | 40 |
Sensitivity: 0.950 | Specificity: 1 | Accuracy: 0.975 |
High-Risk | Low-Risk | p | |
---|---|---|---|
n | 40 | 40 | |
vital_status = DEAD (%) | 21 (52.5) | 2 (5.0) | <0.001 |
Race = white (%) | 25 (100.0) | 30 (100.0) | NA |
Age (mean(SD)) | 67.26 (14.10) | 60.30 (13.91) | 0.029 |
Gender = MALE (%) | 26 (65.0) | 19 (47.5) | 0.176 |
Stage (%) | 0.148 | ||
1 (2.5) | 0 (0.0) | ||
StageII | 18 (45.0) | 21 (52.5) | |
StageIII | 17 (42.5) | 19 (47.5) | |
StageIV | 4 (10.0) | 0 (0.0) | |
m (%) | 0.22 | ||
m0 | 25 (64.1) | 26 (66.7) | |
m1 | 2 (5.1) | 0 (0.0) | |
m1b | 2 (5.1) | 0 (0.0) | |
mx | 10 (25.6) | 13 (33.3) | |
n = nx (%) | 13 (33.3) | 14 (35.0) | 1 |
t (%) | 0.4 | ||
t2a | 3 (7.5) | 9 (22.5) | |
t2b | 1 (2.5) | 1 (2.5) | |
t3 | 1 (2.5) | 0 (0.0) | |
t3a | 14 (35.0) | 11 (27.5) | |
t3b | 1 (2.5) | 4 (10.0) | |
t3c | 1 (2.5) | 0 (0.0) | |
t4a | 11 (27.5) | 9 (22.5) | |
t4b | 4 (10.0) | 5 (12.5) | |
t4c | 1 (2.5) | 1 (2.5) | |
t4d | 2 (5.0) | 0 (0.0) | |
t4e | 1 (2.5) | 0 (0.0) | |
histological_type (%) | 0.003 | ||
EpithelioidCell | 10 (25.0) | 3 (7.5) | |
SpindleCell | 8 (20.0) | 22 (55.0) | |
SpindleCell|EpithelioidCell | 22 (55.0) | 15 (37.5) | |
age_group = younger (%) | 17 (42.5) | 23 (57.5) | 0.264 |
time (mean (SD)) | 11.83 (9.94) | 18.58 (17.40) | 0.036 |
AGE => 60 (%) | 28 (70.0) | 19 (47.5) | 0.069 |
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Wan, Q.; Sang, X.; Jin, L.; Wang, Z. Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma. Genes 2020, 11, 227. https://doi.org/10.3390/genes11020227
Wan Q, Sang X, Jin L, Wang Z. Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma. Genes. 2020; 11(2):227. https://doi.org/10.3390/genes11020227
Chicago/Turabian StyleWan, Qi, Xuan Sang, Lin Jin, and Zhichong Wang. 2020. "Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma" Genes 11, no. 2: 227. https://doi.org/10.3390/genes11020227
APA StyleWan, Q., Sang, X., Jin, L., & Wang, Z. (2020). Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma. Genes, 11(2), 227. https://doi.org/10.3390/genes11020227