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Transcriptome-wide identification and study of cancer-specific splicing events across multiple tumors.

Tsai YS, Dominguez D, Gomez SM, Wang Z - Oncotarget (2015)

Bottom Line: To obtain a global picture of cancer-specific splicing, we analyzed transcriptome sequencing data from 1149 patients in The Cancer Genome Atlas project, producing a core set of AS events significantly altered across multiple cancer types.These cancer-specific AS events are highly conserved, are more likely to maintain protein reading frame, and mainly function in cell cycle, cell adhesion/migration, and insulin signaling pathways.We also found that most genes whose expression is closely associated with cancer-specific splicing are key regulators of the cell cycle.

View Article: PubMed Central - PubMed

Affiliation: Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA.

ABSTRACT
Dysregulation of alternative splicing (AS) is one of the molecular hallmarks of cancer, with splicing alteration of numerous genes in cancer patients. However, studying splicing mis-regulation in cancer is complicated by the large noise generated from tissue-specific splicing. To obtain a global picture of cancer-specific splicing, we analyzed transcriptome sequencing data from 1149 patients in The Cancer Genome Atlas project, producing a core set of AS events significantly altered across multiple cancer types. These cancer-specific AS events are highly conserved, are more likely to maintain protein reading frame, and mainly function in cell cycle, cell adhesion/migration, and insulin signaling pathways. Furthermore, these events can serve as new molecular biomarkers to distinguish cancer from normal tissues, to separate cancer subtypes, and to predict patient survival. We also found that most genes whose expression is closely associated with cancer-specific splicing are key regulators of the cell cycle. This study uncovers a common set of cancer-specific AS events altered across multiple cancers, providing mechanistic insight into how splicing is mis-regulated in cancers.

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Related in: MedlinePlus

Examples of cancer-specific AS events(A) Changes of splicing across different cancer types. For all cancer-specific AS events, the differences of mean PSI values between cancer and normal samples were calculated. We plotted the percent of AS events with PSI changed in same direction among all cancers (purple) or in different directions for one specific type of cancer (BRCA, LUSC or LIHC). (B) Change of splicing in selected examples of cancer-specific AS events. The PSI values of paired samples are marked on the left (normal) and right (tumor) panel in each ladder plot with a colored line linking two PSI values. Blue lines represent AS events with increased PSIs in tumor, whereas red lines represent events with decreased PSIs and grey lines represent events with negligible change of PSI (< = 0.05). Box plots in the bottom are comparisons between all normal samples (white boxes) and all tumor samples (grey boxes). (C) PPI networks of genes containing cancer-specific AS events. The networks have 3 highly connected clusters defined by MCODE (color coded in pink, yellow and green). The hub proteins interacting with multiple clusters were coded with multiple colors. Three genes that are also frequently mutated in tumors were marked by red circles. The most enriched function/GO-term was labeled next to each cluster.
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Figure 2: Examples of cancer-specific AS events(A) Changes of splicing across different cancer types. For all cancer-specific AS events, the differences of mean PSI values between cancer and normal samples were calculated. We plotted the percent of AS events with PSI changed in same direction among all cancers (purple) or in different directions for one specific type of cancer (BRCA, LUSC or LIHC). (B) Change of splicing in selected examples of cancer-specific AS events. The PSI values of paired samples are marked on the left (normal) and right (tumor) panel in each ladder plot with a colored line linking two PSI values. Blue lines represent AS events with increased PSIs in tumor, whereas red lines represent events with decreased PSIs and grey lines represent events with negligible change of PSI (< = 0.05). Box plots in the bottom are comparisons between all normal samples (white boxes) and all tumor samples (grey boxes). (C) PPI networks of genes containing cancer-specific AS events. The networks have 3 highly connected clusters defined by MCODE (color coded in pink, yellow and green). The hub proteins interacting with multiple clusters were coded with multiple colors. Three genes that are also frequently mutated in tumors were marked by red circles. The most enriched function/GO-term was labeled next to each cluster.

Mentions: To determine if the cancer-specific AS events change consistently among different types of cancers, we compared the difference of PSI values between cancer and normal (ΔPSI) across three cancer types for each event (Figure 2A). The majority (i.e. 85%) of these cancer-specific AS events change consistently across different tumor types (i.e., with an increased or decreased PSI values in all three tumors) when comparing tumors to the cognate normal tissue, suggesting that the splicing change in these genes will likely generate similar functional consequences across different tumors. The remaining 15% of AS events, while being altered across all cancers, have different patterns of splicing changes depending on the cancer type.


Transcriptome-wide identification and study of cancer-specific splicing events across multiple tumors.

Tsai YS, Dominguez D, Gomez SM, Wang Z - Oncotarget (2015)

Examples of cancer-specific AS events(A) Changes of splicing across different cancer types. For all cancer-specific AS events, the differences of mean PSI values between cancer and normal samples were calculated. We plotted the percent of AS events with PSI changed in same direction among all cancers (purple) or in different directions for one specific type of cancer (BRCA, LUSC or LIHC). (B) Change of splicing in selected examples of cancer-specific AS events. The PSI values of paired samples are marked on the left (normal) and right (tumor) panel in each ladder plot with a colored line linking two PSI values. Blue lines represent AS events with increased PSIs in tumor, whereas red lines represent events with decreased PSIs and grey lines represent events with negligible change of PSI (< = 0.05). Box plots in the bottom are comparisons between all normal samples (white boxes) and all tumor samples (grey boxes). (C) PPI networks of genes containing cancer-specific AS events. The networks have 3 highly connected clusters defined by MCODE (color coded in pink, yellow and green). The hub proteins interacting with multiple clusters were coded with multiple colors. Three genes that are also frequently mutated in tumors were marked by red circles. The most enriched function/GO-term was labeled next to each cluster.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4466652&req=5

Figure 2: Examples of cancer-specific AS events(A) Changes of splicing across different cancer types. For all cancer-specific AS events, the differences of mean PSI values between cancer and normal samples were calculated. We plotted the percent of AS events with PSI changed in same direction among all cancers (purple) or in different directions for one specific type of cancer (BRCA, LUSC or LIHC). (B) Change of splicing in selected examples of cancer-specific AS events. The PSI values of paired samples are marked on the left (normal) and right (tumor) panel in each ladder plot with a colored line linking two PSI values. Blue lines represent AS events with increased PSIs in tumor, whereas red lines represent events with decreased PSIs and grey lines represent events with negligible change of PSI (< = 0.05). Box plots in the bottom are comparisons between all normal samples (white boxes) and all tumor samples (grey boxes). (C) PPI networks of genes containing cancer-specific AS events. The networks have 3 highly connected clusters defined by MCODE (color coded in pink, yellow and green). The hub proteins interacting with multiple clusters were coded with multiple colors. Three genes that are also frequently mutated in tumors were marked by red circles. The most enriched function/GO-term was labeled next to each cluster.
Mentions: To determine if the cancer-specific AS events change consistently among different types of cancers, we compared the difference of PSI values between cancer and normal (ΔPSI) across three cancer types for each event (Figure 2A). The majority (i.e. 85%) of these cancer-specific AS events change consistently across different tumor types (i.e., with an increased or decreased PSI values in all three tumors) when comparing tumors to the cognate normal tissue, suggesting that the splicing change in these genes will likely generate similar functional consequences across different tumors. The remaining 15% of AS events, while being altered across all cancers, have different patterns of splicing changes depending on the cancer type.

Bottom Line: To obtain a global picture of cancer-specific splicing, we analyzed transcriptome sequencing data from 1149 patients in The Cancer Genome Atlas project, producing a core set of AS events significantly altered across multiple cancer types.These cancer-specific AS events are highly conserved, are more likely to maintain protein reading frame, and mainly function in cell cycle, cell adhesion/migration, and insulin signaling pathways.We also found that most genes whose expression is closely associated with cancer-specific splicing are key regulators of the cell cycle.

View Article: PubMed Central - PubMed

Affiliation: Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA.

ABSTRACT
Dysregulation of alternative splicing (AS) is one of the molecular hallmarks of cancer, with splicing alteration of numerous genes in cancer patients. However, studying splicing mis-regulation in cancer is complicated by the large noise generated from tissue-specific splicing. To obtain a global picture of cancer-specific splicing, we analyzed transcriptome sequencing data from 1149 patients in The Cancer Genome Atlas project, producing a core set of AS events significantly altered across multiple cancer types. These cancer-specific AS events are highly conserved, are more likely to maintain protein reading frame, and mainly function in cell cycle, cell adhesion/migration, and insulin signaling pathways. Furthermore, these events can serve as new molecular biomarkers to distinguish cancer from normal tissues, to separate cancer subtypes, and to predict patient survival. We also found that most genes whose expression is closely associated with cancer-specific splicing are key regulators of the cell cycle. This study uncovers a common set of cancer-specific AS events altered across multiple cancers, providing mechanistic insight into how splicing is mis-regulated in cancers.

Show MeSH
Related in: MedlinePlus