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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

Molecular features of AS events changed in cancers(A) The genes containing 163 cancer-specific AS events were analyzed by gene ontology, and the significantly enriched (p < 0.005) GO terms are plotted. (B) Box plots of the length of alterative exons in the SE events from the MISO database, the SE events that were changed in LIHC, LUSC, BRCA, and the SE events altered in all cancers (from the left to the right). (C) Percent of skipped exons in each exon phase. Exons are classified into phase 0, 1, and 2 depending on the reminders when dividing their length by 3. Phase 0 (white boxes): events without frame-shift; Phase1 and 2 (black and gray boxes): events with frame-shift. The order is same as (B). Asterisks indicate significant increase of phase 0 exon (p < 0.05 by Fisher's exact test). (D) Sequence conservation near the cancer-specific skipped exons and all skipped exons. Black line represents average conservation score from the 124 cancer-specific SE events; grey line represents average conservation score from all the SE events in MISO database (control). (E) We compared the distribution of PSI standard deviation between control AS (left) and AS events that change in each cancer (right). We also compared those between normal (white box) and tumor samples (grey box).
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Figure 3: Molecular features of AS events changed in cancers(A) The genes containing 163 cancer-specific AS events were analyzed by gene ontology, and the significantly enriched (p < 0.005) GO terms are plotted. (B) Box plots of the length of alterative exons in the SE events from the MISO database, the SE events that were changed in LIHC, LUSC, BRCA, and the SE events altered in all cancers (from the left to the right). (C) Percent of skipped exons in each exon phase. Exons are classified into phase 0, 1, and 2 depending on the reminders when dividing their length by 3. Phase 0 (white boxes): events without frame-shift; Phase1 and 2 (black and gray boxes): events with frame-shift. The order is same as (B). Asterisks indicate significant increase of phase 0 exon (p < 0.05 by Fisher's exact test). (D) Sequence conservation near the cancer-specific skipped exons and all skipped exons. Black line represents average conservation score from the 124 cancer-specific SE events; grey line represents average conservation score from all the SE events in MISO database (control). (E) We compared the distribution of PSI standard deviation between control AS (left) and AS events that change in each cancer (right). We also compared those between normal (white box) and tumor samples (grey box).

Mentions: To further study the functional consequence of cancer-specific AS events in an unbiased fashion, we performed gene ontology (GO) analysis on genes containing cancer-specific AS events using the DAVID online tool (http://david.abcc.ncifcrf.gov/) [30, 31]. We found that the most enriched functional categories included cell adhesion, cell division, cell cycle and so on. (Figure 3A). We also did GO analysis on each individual cancer type and ranked enriched GO terms by their p-value (Supplementary Figure S2A to S2C). The top enriched terms were cytoskeleton proteins and proteins associated with cell adhesion, ATP-binding, cell cycle.


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

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

Molecular features of AS events changed in cancers(A) The genes containing 163 cancer-specific AS events were analyzed by gene ontology, and the significantly enriched (p < 0.005) GO terms are plotted. (B) Box plots of the length of alterative exons in the SE events from the MISO database, the SE events that were changed in LIHC, LUSC, BRCA, and the SE events altered in all cancers (from the left to the right). (C) Percent of skipped exons in each exon phase. Exons are classified into phase 0, 1, and 2 depending on the reminders when dividing their length by 3. Phase 0 (white boxes): events without frame-shift; Phase1 and 2 (black and gray boxes): events with frame-shift. The order is same as (B). Asterisks indicate significant increase of phase 0 exon (p < 0.05 by Fisher's exact test). (D) Sequence conservation near the cancer-specific skipped exons and all skipped exons. Black line represents average conservation score from the 124 cancer-specific SE events; grey line represents average conservation score from all the SE events in MISO database (control). (E) We compared the distribution of PSI standard deviation between control AS (left) and AS events that change in each cancer (right). We also compared those between normal (white box) and tumor samples (grey box).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Molecular features of AS events changed in cancers(A) The genes containing 163 cancer-specific AS events were analyzed by gene ontology, and the significantly enriched (p < 0.005) GO terms are plotted. (B) Box plots of the length of alterative exons in the SE events from the MISO database, the SE events that were changed in LIHC, LUSC, BRCA, and the SE events altered in all cancers (from the left to the right). (C) Percent of skipped exons in each exon phase. Exons are classified into phase 0, 1, and 2 depending on the reminders when dividing their length by 3. Phase 0 (white boxes): events without frame-shift; Phase1 and 2 (black and gray boxes): events with frame-shift. The order is same as (B). Asterisks indicate significant increase of phase 0 exon (p < 0.05 by Fisher's exact test). (D) Sequence conservation near the cancer-specific skipped exons and all skipped exons. Black line represents average conservation score from the 124 cancer-specific SE events; grey line represents average conservation score from all the SE events in MISO database (control). (E) We compared the distribution of PSI standard deviation between control AS (left) and AS events that change in each cancer (right). We also compared those between normal (white box) and tumor samples (grey box).
Mentions: To further study the functional consequence of cancer-specific AS events in an unbiased fashion, we performed gene ontology (GO) analysis on genes containing cancer-specific AS events using the DAVID online tool (http://david.abcc.ncifcrf.gov/) [30, 31]. We found that the most enriched functional categories included cell adhesion, cell division, cell cycle and so on. (Figure 3A). We also did GO analysis on each individual cancer type and ranked enriched GO terms by their p-value (Supplementary Figure S2A to S2C). The top enriched terms were cytoskeleton proteins and proteins associated with cell adhesion, ATP-binding, cell cycle.

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