The homeoprotein DLX4 controls inducible nitric oxide synthase-mediated angiogenesis in ovarian cancer

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

doi:10.1186/s12943-015-0368-3

Cited by 17 publications

(14 citation statements)

References 45 publications

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“…Therefore, we evaluated STAT1 activation in order to explore the underlying mechanism of IL-17-mediated upregulation of NOS2 induced by IFN-γ. Previous research has shown that Tyr701-p-STAT1 is pivotal in STAT1-dependent regulation of NOS2 expression ( 21 ). Fig.…”

Section: Resultsmentioning

confidence: 99%

IL-17 intensifies IFN-γ-induced NOS2 upregulation in RAW 264.7 cells by further activating STAT1 and NF-κB

Gao¹,

Liu²,

Wu³

et al. 2015

International Journal of Molecular Medicine

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Interleukin-17 (IL-17) is a signature cytokine of Th17 cells. Previous research has indicated that IL-17 plays a proinflammatory role by exacerbating interferon-γ (IFN-γ)-induced inflammation. However, prior to this study, it was not known whether inducible nitric oxide synthase (iNOS or NOS2), a signature molecule of inflammation, could be intensified by IL-17 when combined with IFN-γ. Thus, we explored the roles and underlying mechanisms of IL-17 and IFN-γ in the regulation of NOS2 expression in RAW 264.7 cells using qPCR, western blot analysis, colorimetric analysis, ChIP assay and statistical analysis. Although IL-17 alone did not induce NOS2 expression or nitric oxide (NO) production, as shown by western blot analysis and colorimetric analysis, it intensified IFN-γ-induced NOS2 upregulation and NO production in RAW 264.7 cells. The alteration of relevant transcription factors demonstrated that a combination of IFN-γ and IL-17 enhanced Tyr701-phosphorylated signal transducer and activator of transcription 1 [p-STAT1(Y701)] and nuclear factor-κB (NF-κB) activation, nuclear translocations and their binding to the NOS2 promoter, compared with IFN-γ alone, as illustrated by the results of the western blot analysis and ChIP assay. Also, using the corresponding inhibitors of STAT1 and NF-κB, we noted downregulation of the expression of NOS2 induced by IFN-γ alone or in combination with IL-17, respectively. In addition, IFN-γ increased phosphorylated (p-)p38 mitogen-activated protein kinase (MAPK), and accelerated the activation of the NF-κB pathway and the expression of NOS2, but phosphorylated extracellular signal-regulated kinase 1/2 (p-ERK1/2) was reduced by treatment with IFN-γ and IL-17. IL-17 intensified the activation of the NF-κB pathway and NOS2 upregulation induced by IFN-γ by increasing the phosphorylation of p38 MAPK and limiting the phosphorylation of ERK1/2. Taken together, these results suggest that IL-17 intensified IFN-γ-induced NOS2 upregulation and NO production by increasing the transcription activity of p-STAT1(Y701) and NF-κB in RAW 264.7 cells. Further activation of the NF-κB pathway induced by IL-17 relied on enhanced phosphorylation of p38 MAPK and decreased phosphorylation of ERK1/2. The mechanism suggested in this study provides novel information which may be used for anti-inflammatory therapy with IL-17.

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Section: Resultsmentioning

confidence: 99%

IL-17 intensifies IFN-γ-induced NOS2 upregulation in RAW 264.7 cells by further activating STAT1 and NF-κB

Gao¹,

Liu²,

Wu³

et al. 2015

International Journal of Molecular Medicine

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“…In metastases, c-Fos and Fos B expression were significantly lower than in the respective primary OC tissues, which also indicated that FOS might be a potential cancer suppressor gene [24]. Trinh B. revealed that STAT1 could interact with DLX4 and promote ovarian tumor angiogenesis in part by stimulating iNOS expression [25]. STAT1 was found overexpressed in patients with high-grade serous OC after the development of clinical platinum resistance.…”

Section: Discussionmentioning

confidence: 99%

Survival-associated transcriptome analysis in ovarian cancer

Xu¹,

Zhou²,

Wang³

et al. 2020

EJGO

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Purpose of investigation: Ovarian Cancer (OC) is one of the most lethal gynecologic cancers worldwide. Despite the standard treatment, including radical resection, systemic chemotherapy, and targeted drugs for patients, survival rates remain low. This study provides new ideas for the diagnosis and treatment of Ovarian Cancer. Material and Methods: We performed Kaplan-Meier analysis on the transcriptome of Ovarian Cancer based on RNA-Seq data from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment were used for pathway and functional enrichment. Protein-protein interaction (PPI) network was constructed and visualized by SRING and Cytoscape. Results: A total of 1693 genes associated with survival were identified. The Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analysis revealed that these selected genes were differently enriched in numerous functional pathways. The top ten hub genes (RIPK4, HSPA8, FOS, STAT1, CD40LG, FGF2, RAC1, CXCR4, PRPF19, and CXCL10) were identified in our PPI network. Three highly connected cluster modules were differently enriched in several functional pathways. Conclusion: These key biomarkers in Ovarian Cancer may have diagnostic and therapeutic value in the future.

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“…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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The homeoprotein DLX4 controls inducible nitric oxide synthase-mediated angiogenesis in ovarian cancer

Cited by 17 publications

References 45 publications

IL-17 intensifies IFN-γ-induced NOS2 upregulation in RAW 264.7 cells by further activating STAT1 and NF-κB

IL-17 intensifies IFN-γ-induced NOS2 upregulation in RAW 264.7 cells by further activating STAT1 and NF-κB

Survival-associated transcriptome analysis in ovarian cancer

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

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