WT1 Mutants Reveal SRPK1 to Be a Downstream Angiogenesis Target by Altering VEGF Splicing

Amin, Elianna; Oltean, Sebastian; Hua, Jing; Gammons, Melissa V.; Hamdollah-Zadeh, Maryam; Welsh, Gavin I.; Cheung, Man-Kim; Ni, Lan; Kase, Satoru; Rennel, Emma; Symonds, Kirsty E.; Nowak, Dawid G.; Royer‐Pokora, Brigitte; Saleem, Moin A.; Hagiwara, Masatoshi; Schumacher, Valérie; Harper, Steven J.; Hinton, David R.; Bates, David O.; Ladomery, Michael

doi:10.1016/j.ccr.2011.10.016

Cited by 217 publications

(306 citation statements)

References 46 publications

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“…For example, many genes that are involved in proliferation and invasiveness are frequently alternatively spliced, and their specific splice variants, which stimulate cell proliferation and migration, and thus contribute to the transformed phenotype, are often upregulated in tumours. These changes often involve dysregulated expression of RNA-binding proteins 16 and/or protein kinases that regulate their activities 17,18 . SR proteins (Serine/arginine-rich proteins) are well-characterized RNA-binding proteins that play critical roles in splicing regulation, and changes in the expression of these proteins can dramatically affect alternative splicing profiles in cells [8][9][10] .…”

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confidence: 99%

BCLAF1 and its splicing regulator SRSF10 regulate the tumorigenic potential of colon cancer cells

et al. 2014

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Bcl-2-associated transcription factor 1 (BCLAF1) is known to be involved in multiple biological processes. Although several splice variants of BCLAF1 have been identified, little is known about how BCLAF1 splicing is regulated or the contribution of alternative splicing to its developmental functions. Here we find that inclusion of alternative exon5a was significantly increased in colorectal cancer (CRC) samples. Knockdown of the BCLAF1 protein isoform resulting from exon5a inclusion inhibited growth and that its overexpression increased tumorigenic potential. We also found that the splicing factor SRSF10 stimulates inclusion of exon5a and has growth-inducing activity. Importantly, the upregulation of SRSF10 expression observed in clinical CRC samples parallels the increased inclusion of BCLAF1 exon5a, both of which are associated with higher tumour grade. These findings identify SRSF10 as a key regulator of BCLAF1 pre-mRNA splicing and the maintenance of oncogenic features in human colon cancer cells.

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confidence: 99%

BCLAF1 and its splicing regulator SRSF10 regulate the tumorigenic potential of colon cancer cells

et al. 2014

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“…SRPK1, a kinase that phosphorylates SR-proteins (the class of splice factors mentioned above), is overexpressed in several cancers [56][57][58] and also in DenysDrash syndrome, associated renal failure, and increased incidence of Wilms tumors. 59 SRPK1 is a determinant of angiogenesis through modulation of VEGF-A splicing and its abnormal expression maintains a pathologic loop by promoting proangiogenic and propermeability VEGF-A isoforms.…”

Section: Splice Factors or Splicing-specific Kinases Are Deregulatedmentioning

confidence: 99%

Alternative Splicing in CKD

Stevens

Oltean

2016

JASN

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Alternative splicing (AS) has emerged in the postgenomic era as one of the main drivers of proteome diversity, with $94% of multiexon genes alternatively spliced in humans. AS is therefore one of the main control mechanisms for cell phenotype, and is a process deregulated in disease. Numerous reports describe pathogenic mutations in splice factors, splice sites, or regulatory sequences. Additionally, compared with the physiologic state, disease often associates with an abnormal proportion of splice isoforms (or novel isoforms), without an apparent driver mutation. It is therefore essential to study how AS is regulated in physiology, how it contributes to pathogenesis, and whether we can manipulate faulty splicing for therapeutic advantage. Although the disease most commonly linked to deregulation of AS in several genes is cancer, many reports detail pathogenic splice variants in diseases ranging from neuromuscular disorders to diabetes or cardiomyopathies. A plethora of splice variants have been implicated in CKDs as well. In this review, we describe examples of these CKD-associated splice variants and ideas on how to manipulate them for therapeutic benefit.

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“…We reviewed state-of-the-art review papers [31,32,36] as well as individual studies [37][38][39][40][41][42][43][44][45][46] on EMT to construct the network to make sure that it includes the key players in EMT regulations. Especially, the network includes non-coding RNA regulations, which have been found in recent years to be associated with EMT [47].…”

Section: Construct Emt Regulatory Networkmentioning

confidence: 99%

Epithelial Mesenchymal Transition Network-Based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data

Shao

Carlo

Conrad

2017

Genomics Comput Biol

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Epithelial mesenchymal transition (EMT) process has been shown as highly relevant to cancer prognosis. However, although different biological network-based biomarker identification methods have been proposed to predict cancer prognosis, EMT network has not been directly used for this purpose. In this study, we constructed an EMT regulatory network consisting of 87 molecules and tried to select features that are useful for prognosis prediction in Lung Adenocarcinoma (LUAD). To incorporate multiple molecular profiles, we obtained four types of molecular data including mRNA-Seq, copy number alteration (CNA), DNA methylation, and miRNA-Seq data from The Cancer Genome Atlas. The data were mapped to the EMT network in three alternative ways: mRNA-Seq and miRNA-Seq, DNA methylation, and CNA and miRNA-Seq. Each mapping was employed to extract five different sets of features using discretization and network-based biomarker identification methods. Each feature set was then used to predict prognosis with SVM and logistic regression classifiers. We measured the prediction accuracy with AUC and AUPR values using 10 times 10-fold cross validation. For a more comprehensive evaluation, we also measured the prediction accuracies of clinical features, EMT plus clinical features, randomly picked 87 molecules from each data mapping, and using all molecules from each data type. Counter-intuitively, EMT features do not always outperform randomly selected features and the prediction accuracies of the five feature sets are mostly not significantly different. Clinical features are shown to give the highest prediction accuracies. In addition, the prediction accuracies of both EMT features and random features are comparable as using all features (more than 17,000) from each data type.

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WT1 Mutants Reveal SRPK1 to Be a Downstream Angiogenesis Target by Altering VEGF Splicing

Cited by 217 publications

References 46 publications

BCLAF1 and its splicing regulator SRSF10 regulate the tumorigenic potential of colon cancer cells

BCLAF1 and its splicing regulator SRSF10 regulate the tumorigenic potential of colon cancer cells

Alternative Splicing in CKD

Epithelial Mesenchymal Transition Network-Based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data

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