2019
DOI: 10.1038/s41375-018-0358-8
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The transcriptional regulator FUBP1 influences disease outcome in murine and human myeloid leukemia

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Cited by 16 publications
(22 citation statements)
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“…Although three nuclear localization signals (NLS) confers the enrichment of Fubp1 in the nucleus [ 2 ], Fubp1 could translocate to the cytoplasm under various stimuli including viral infection and apoptosis to control mRNA stability or translation of its target genes [ 3 , 4 ]. Increasing evidence has demonstrated the oncogenic role of Fubp1 and deregulated expression of FUBP1 has been reported in several types of tumors, including hepatocellular carcinoma [ 5 , 6 ], nasopharyngeal carcinoma [ 7 ], gastric cancer [ 8 ], leukemia [ 9 ] and neuroblastoma [ 10 ]. The molecular mechanisms by which FUBP1 contributes to tumor propagation are currently being investigated.…”
Section: Introductionmentioning
confidence: 99%
“…Although three nuclear localization signals (NLS) confers the enrichment of Fubp1 in the nucleus [ 2 ], Fubp1 could translocate to the cytoplasm under various stimuli including viral infection and apoptosis to control mRNA stability or translation of its target genes [ 3 , 4 ]. Increasing evidence has demonstrated the oncogenic role of Fubp1 and deregulated expression of FUBP1 has been reported in several types of tumors, including hepatocellular carcinoma [ 5 , 6 ], nasopharyngeal carcinoma [ 7 ], gastric cancer [ 8 ], leukemia [ 9 ] and neuroblastoma [ 10 ]. The molecular mechanisms by which FUBP1 contributes to tumor propagation are currently being investigated.…”
Section: Introductionmentioning
confidence: 99%
“…We further illustrate how PaDETO detects samples that were intentionally switched by us ( Figure S3 ). Last, we show that PaDETO was able to detect an outlier sample ( Figure 4A ) that was not detected originally using manual PCA interpretation (18), thus leading to an improved analysis and conclusions (see below). Taking together, we concluded that PaDETO is a powerful algorithm to identify discrepancies in the data, which improves regular PCA interpretation when it is hard to consider all mathematical aspects.…”
Section: Resultsmentioning
confidence: 79%
“…To test whether BiSEK can improve findings from RNA-seq data that were also tested experimentally, we concentrated on data from CML cells of Mice bone marrow (BM) from a recent paper by (18). Hoang et al used the standard DESeq2 pipeline to test the impact of Far Upstream Element Binding Protein 1 (FUBP1) in CML Leukemia, by comparing between the RNA expression of mice that were transplanted with BM cells expressing the oncogene BCR-ABL1 and Fubp1 shRNA (Fubp1 depletion) and mice that were transplanted with BM cells expressing BCR-ABL1 and scrambled shRNA (control) (18). BiSEK algorithm, PaDETO, detected one of the Fubp1 shRNA+ samples as an outlier (Figure 4A).…”
Section: Resultsmentioning
confidence: 99%
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