TRIP13 is a predictor for poor prognosis and regulates cell proliferation, migration and invasion in prostate cancer

Dong, Liming; Ding, Honglin; Li, Yanpei; Xue, Dongwei; Li, Zhi; Liu, Yili; Zhang, Teng; Zhou, Jian; Wang, Ping

doi:10.1016/j.ijbiomac.2018.09.168

Cited by 35 publications

(31 citation statements)

References 25 publications

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“…Studies in various types of cancers have demonstrated that overexpression of TRIP13 promotes cell proliferation, while its suppression with siRNA or shRNA inhibits proliferation and induces cell death [6,66,[85], [86], [87], [88], [89]]. Moreover, TRIP13-overexpressing cancer cells showed a significant increase in proliferation, invasion and migration compared with control cells [66].…”

Section: Elevated Trip13 Promotes Tumor Progressionmentioning

confidence: 99%

Insights into a Crucial Role of TRIP13 in Human Cancer

Qian

Guo

et al. 2019

Computational and Structural Biotechnology Journal

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Thyroid Hormone Receptor Interacting Protein 13 (TRIP13) plays a key role in regulating mitotic processes, including spindle assembly checkpoint and DNA repair pathways, which may account for Chromosome instability (CIN). As CIN is a predominant hallmark of cancer, TRIP13 may act as a tumor susceptibility locus. Amplification of TRIP13 has been observed in various human cancers and implicated in several aspects of malignant transformation, including cancer cell proliferation, drug resistance and tumor progression. Here, we discussed the functional significance of TRIP13 in cell progression, highlighted the recent findings on the aberrant expression in human cancers and emphasized its significance for the therapeutic potential.

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Section: Elevated Trip13 Promotes Tumor Progressionmentioning

confidence: 99%

Insights into a Crucial Role of TRIP13 in Human Cancer

Qian

Guo

et al. 2019

Computational and Structural Biotechnology Journal

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“…In addition to enrichment for known cancer drivers, Rank-1 cis genes that frequently occur across cancer types (Supplemental Table S10) include putative or novel genes implicated in cancer initiation or progression. These include: TRIP13 , a mitosis regulator that was shown to promote tumor growth in colorectal cancer 34 and is a predictor of poor prognosis in prostate cancer 35 ; ORAOV1 , a gene overexpressed in many solid tumors that is linked to generation of reactive oxygen species 36 ; TPX2 , an interactor and substrate of Aurora-A that is a potent oncogene amplified in many cancers and a promising therapeutic target 37,38 ; and DUSP22 , which has been shown to behave as a tumor suppressor gene in peripheral T-cell lymphomas 39 and regulates ERα dependent transcription in breast cancer cells 40 .…”

Section: Resultsmentioning

confidence: 99%

Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis

Chapuy

Varelas

et al. 2019

Sci Rep

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The emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators – such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others – as predictive features of cancer outcome. However, identification of “driver genes” associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively. iEDGE first identifies the cis and trans gene expression signatures associated with the presence/absence of a particular epi-DNA alteration across samples. It then applies tests of statistical mediation to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks. We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE. In summary, integrative analysis of SCNAs and gene expression using iEDGE successfully identified known cancer driver genes and putative cancer therapeutic targets across 19 cancer types in the TCGA. The proposed method can easily be applied to the integration of gene expression profiles with other epi-DNA assays in a variety of disease contexts.

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“…In addition to enrichment for known cancer drivers, Rank-1 cis genes that frequently occur across cancer types (Supplemental Table S10) include putative or novel genes implicated in cancer initiation or progression. These include: TRIP13 , a mitosis regulator that was shown to promote tumor growth in colorectal cancer (Sheng et al, 2018) and is a predictor of poor prognosis in prostate cancer (Dong et al 2019); ORAOV1 , a gene overexpressed in many solid tumors that is linked to generation of reactive oxygen species (Zhai et al 2014); TPX2 , an interactor and substrate of Aurora-A that is a potent oncogene amplified in many cancers and a promising therapeutic target (Kufer et al 2002; Yan et al, 2016); and DUSP22 , which has been shown to behave as a tumor suppressor gene in peripheral T-cell lymphomas (Mélard et al, 2016) and regulates ERα dependent transcription in breast cancer cells (Sekine et al, 2007).…”

Section: Resultsmentioning

confidence: 99%

iEDGE: integration of Epi-DNA and Gene Expression and applications to the discovery of somatic copy number-associated drivers in cancer

Chapuy

Varelas

et al. 2019

Preprint

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22The emergence of large-scale multi-omics data warrants method development for data integration. 23Genomic studies from cancer patients have identified epigenetic and genetic regulatorssuch as 24 methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among othersas 25 predictive features of cancer outcome. However, identification of "driver genes" associated with a given 26 alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and 27 trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes 28 denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, 29 respectively. 30First, iEDGE identifies the cis and trans genes associated with the presence/absence of a particular epi-31 DNA alteration across samples. Tests of statistical mediation are then performed to determine the cis 32 genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway 33 enrichment analysis to gain insights into the underlying regulatory networks. 34We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer 35 and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers 36 identified by iEDGE were found to be significantly enriched for known driver genes from multiple 37 compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal 38 importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with 39 amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the 40 analyses results are accessible at https://montilab.bu.edu/iEDGE. 41 55 development. Furthermore, the generated analysis results are often static, and not accessible in an 56 interactive fashion. The approach presented here aims to address both of these shortcomings. 57The central hypothesis behind integrative approaches is that the integration of multi-level genomics data 58 allows for prioritization of putative cancer "drivers" that are potential biomarkers or therapeutic targets. 59An important genetic alteration type in cancer is somatic copy-number alterations (SCNAs). SCNAs 60 harbor many known cancer drivers (oncogenes or tumor suppressors) and play an important role in cancer 61 initiation and/or progression through activation of oncogenes and inactivation of tumor suppressors 62 (Beroukhim et al. 2010; Zack et al. 2013). Identification of unknown SCNA-associated cancer drivers is 63 complicated by the fact that each SCNA contains many genes, often even a complete chromosome arm, 64 the majority of which is likely not to confer any selective advantage (i.e., passengers). One approach to 111 SCNAs. For instance, gene sets HALLMARK_ESTROGEN_RESPONSE_LATE, 112 HALLMARK_ESTROGEN_RESPONSE_EARLY, HALLMARK_G2M_CHECKPOINT were 113 significant hits in more than 75% of SCNAs. These gene s...

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TRIP13 is a predictor for poor prognosis and regulates cell proliferation, migration and invasion in prostate cancer

Cited by 35 publications

References 25 publications

Insights into a Crucial Role of TRIP13 in Human Cancer

Insights into a Crucial Role of TRIP13 in Human Cancer

Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis

iEDGE: integration of Epi-DNA and Gene Expression and applications to the discovery of somatic copy number-associated drivers in cancer

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