The Homeoprotein DLX4 Stimulates NF-κB Activation and CD44-Mediated Tumor–Mesothelial Cell Interactions in Ovarian Cancer

Haria, Dhwani; Trinh, Bon Q.; Ko, Song Yi; Barengo, Nicolas; Liu, Jinsong; Naora, Honami

doi:10.1016/j.ajpath.2015.04.004

Cited by 22 publications

(24 citation statements)

References 45 publications

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“…It has been reported that CARD11 is elevated in epithelial ovarian cancer [ 29 ]. Other studies reinforce that the NF-κB pathway can be activated by CARD11 [ 30 , 31 ] and plays a metastatic-associated role in ovarian cancer via various regulatory factors such as CD44 [ 32 ]. These studies strongly suggest that CARD11 may play an oncogenic role similar to that of CTD-2020K17.1.…”

Section: Discussionmentioning

confidence: 84%

CTD-2020K17.1, a Novel Long Non-Coding RNA, Promotes Migration, Invasion, and Proliferation of Serous Ovarian Cancer Cells In Vitro

Zhu

Guo

et al. 2018

Med Sci Monit

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BackgroundOvarian cancer is the most lethal malignant tumor of the female reproductive system, and the metastasis is one of the major factors that contribute to the poor outcome of patients with OC. Accumulating evidence indicates that lncRNAs are expressed and play important regulatory roles in ovarian cancer.Material/MethodsAberrant lncRNAs in primary ovarian cancer tissues (POCTs) and paired omental metastasis tissues (OMTs) of patients with HGSOC were studied via lncRNA microarray. Real-time PCR was performed to examine CTD-2020K17.1 expression in HGSOC tissues from 38 patients, a normal ovarian surface epithelium cell line, and 4 ovarian cancer cell lines. Additionally, Transwell assays, wound healing assays, CCK-8 proliferation assays, and flow cytometry were used to explore the biological function of CTD-2020K17.1 in ovarian cancer cells. Finally, Western blot analysis was used to verify the potential target gene of CTD-2020K17.1.ResultsA novel lncRNA named CTD-2020K17.1 was identified via microarray analysis. Expression of CTD-2020K17.1 was significantly increased in OMTs and in 4 ovarian cancer cell lines compared with POCTs (P<0.05) or normal ovarian surface epithelial cell line (P<0.05). Moreover, CTD-2020K17.1 overexpression promoted migration, invasion, and proliferation of ovarian cancer cells, and CTD-2020K17.1 regulated the expression of CARD11.ConclusionsCTD-2020K17.1 is significantly upregulated in OMTs and ovarian cancer cell lines. It can promote the migration, invasion, and proliferation of ovarian cancer cells, and CARD11 is regulated by CTD-2020K17.1.

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Section: Discussionmentioning

confidence: 84%

CTD-2020K17.1, a Novel Long Non-Coding RNA, Promotes Migration, Invasion, and Proliferation of Serous Ovarian Cancer Cells In Vitro

Zhu

Guo

et al. 2018

Med Sci Monit

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“…The methylation of DLX4 was strongly associated with high risk of recurrence and poor prognostic survival in lung cancer patients [31]. The gene can drive tumor progression in ovarian cancer through the NF-κB pathway by activating a regulatory factor cell surface molecule CD44 [32]. It was also found to promote ovarian cancer by inducing the expression of iNOS, an enzyme that stimulates angiogenesis [33].…”

Section: Discussionmentioning

confidence: 99%

Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes

2019

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BackgroundIdentifying cancer driver genes (CDG) is a crucial step in cancer genomic toward the advancement of precision medicine. However, driver gene discovery is a very challenging task because we are not only dealing with huge amount of data; but we are also faced with the complexity of the disease including the heterogeneity of background somatic mutation rate in each cancer patient. It is generally accepted that CDG harbor variants conferring growth advantage in the malignant cell and they are positively selected, which are critical to cancer development; whereas, non-driver genes harbor random mutations with no functional consequence on cancer. Based on this fact, function prediction based approaches for identifying CDG have been proposed to interrogate the distribution of functional predictions among mutations in cancer genomes (eLS 1–16, 2016). Assuming most of the observed mutations are passenger mutations and given the quantitative predictions for the functional impact of the mutations, genes enriched of functional or deleterious mutations are more likely to be drivers. The promises of these methods have been continually refined and can therefore be applied to increase accuracy in detecting new candidate CDGs. However, current function prediction based approaches only focus on coding mutations and lack a systematic way to pick the best mutation deleteriousness prediction algorithms for usage.ResultsIn this study, we propose a new function prediction based approach to discover CDGs through a gene-based permutation approach. Our method not only covers both coding and non-coding regions of the genes; but it also accounts for the heterogeneous mutational context in cohort of cancer patients. The permutation model was implemented independently using seven popular deleteriousness prediction scores covering splicing regions (SPIDEX), coding regions (MetaLR, and VEST3) and pan-genome (CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding). We applied this new approach to somatic single nucleotide variants (SNVs) from whole-genome sequences of 119 breast and 24 lung cancer patients and compared the seven deleteriousness prediction scores for their performance in this study.ConclusionThe new function prediction based approach not only predicted known cancer genes listed in the Cancer Gene Census (CGC), but also new candidate CDGs that are worth further investigation. The results showed the advantage of utilizing pan-genome deleteriousness prediction scores in function prediction based methods. Although VEST3 score, a deleteriousness prediction score for missense mutations, has the best performance in breast cancer, it was topped by CADD and Fathmm-MKL coding, two pan-genome deleteriousness prediction scores, in lung cancer.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0452-9) contains supplementary material, which is available to authorized users.

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“…Tjhay et al, for instance, have shown that disseminated ovarian tumors in the peritoneum contain highly enriched CD44v6-positive cancer cells and an increased number of CD44v6-positive cancer cells in primary tumors was associated with a shortened overall survival in stage III-IV ovarian cancer [29]. In a xenograft model, Haria et al found that DLX4, a transcription factor encoded by a homeobox gene, induced expression of CD44 in ovarian tumor cells, and inhibition of CD44 abrogated the ability of DLX4 to stimulate tumormesothelial cell interactions [30]. The fact that CD44 drives inflammatory signaling in PC is also consistent with our finding of differential regulation of SERPINB3, associated with host-immune-response [31].…”

Section: Discussionmentioning

confidence: 99%

Dynamic changes of tumor gene expression during repeated Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) in women with peritoneal cancer

Rezniczek

Jüngst

Tannapfel

et al. 2016

Geburtshilfe Frauenheilkd

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Background: Intraperitoneal chemotherapy is used to treat peritoneal cancer. The pattern of gene expression changes of peritoneal cancer during intraperitoneal chemotherapy has not been studied before. Pressurized intraperitoneal aerosol chemotherapy is a new form of intraperitoneal chemotherapy using repeated applications and allowing repeated tumor sampling during chemotherapy. Here, we present the analysis of gene expression changes during pressurized intraperitoneal aerosol chemotherapy with doxorubicin and cisplatin using a 22-gene panel. Methods: Total RNA was extracted from 152 PC samples obtained from 63 patients in up to six cycles of intraperitoneal chemotherapy. Quantitative real-time PCR was used to determine the gene expression levels. For select genes, immunohistochemistry was used to verify gene expression changes observed on the transcript level on the protein level. Observed (changes in) expression levels were correlated with clinical outcomes. Results: Gene expression profiles differed significantly between peritoneal cancer and non-peritoneal cancer samples and between ascites-producing and non ascites-producing peritoneal cancers. Changes of gene expression patterns during repeated intraperitoneal chemotherapy cycles were prognostic of overall survival, suggesting a molecular tumor response of peritoneal cancer. Specifically, downregulation of the whole gene panel during intraperitoneal chemotherapy was associated with better treatment response and survival. Conclusions: In summary, molecular changes of peritoneal cancer during pressurized intraperitoneal aerosol chemotherapy can be documented and may be used to refine individual treatment and prognostic estimations.

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The Homeoprotein DLX4 Stimulates NF-κB Activation and CD44-Mediated Tumor–Mesothelial Cell Interactions in Ovarian Cancer

Cited by 22 publications

References 45 publications

CTD-2020K17.1, a Novel Long Non-Coding RNA, Promotes Migration, Invasion, and Proliferation of Serous Ovarian Cancer Cells In Vitro

CTD-2020K17.1, a Novel Long Non-Coding RNA, Promotes Migration, Invasion, and Proliferation of Serous Ovarian Cancer Cells In Vitro

Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes

Dynamic changes of tumor gene expression during repeated Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) in women with peritoneal cancer

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