Tumor STAT1 Transcription Factor Activity Enhances Breast Tumor Growth and Immune Suppression Mediated by Myeloid-derived Suppressor Cells

Hix, Laura M.; Karavitis, John; Khan, Mohammad W.; Shi, Yihui; Khazaie, Khashayarsha; Zhang, Ming

doi:10.1074/jbc.m112.441402

Cited by 113 publications

(100 citation statements)

References 60 publications

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“…The transcription factor STAT1 has been thought of as an onco-suppressive factor, acting through the stimulation of anti-proliferative and pro-apoptotic genes in tumor cells, but recent evidence supports a multivalent role for this factor in advancing cancer. STAT1 expression is up-regulated in a number of late-stage cancers, including breast cancer [63][64][65], and high STAT1 levels have been correlated with poor survival, presence of metastases and with chemo-and radiotherapy resistance in breast cancer patients [66,67]. Potential functions of STAT1 in aggressive cancers include the maintenance of pro-survival genes [68] and the regulation of the local immune response [65].…”

Section: Mapping Of Key Genes Onto Breast Cancer Progression Pathsmentioning

confidence: 99%

“…STAT1 expression is up-regulated in a number of late-stage cancers, including breast cancer [63][64][65], and high STAT1 levels have been correlated with poor survival, presence of metastases and with chemo-and radiotherapy resistance in breast cancer patients [66,67]. Potential functions of STAT1 in aggressive cancers include the maintenance of pro-survival genes [68] and the regulation of the local immune response [65]. There are conflicting reports of STAT1 expression levels in breast cancer subtypes [59], but in the large METABRIC dataset, STAT1 expression was significantly increased in the malignant phenotypes.…”

Section: Mapping Of Key Genes Onto Breast Cancer Progression Pathsmentioning

confidence: 99%

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Cancer progression modeling using static sample data

Sun¹,

Ye²,

Nowak³

et al. 2014

Genome Biol

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As molecular profiling data continue to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression. An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.

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Section: Mapping Of Key Genes Onto Breast Cancer Progression Pathsmentioning

confidence: 99%

Section: Mapping Of Key Genes Onto Breast Cancer Progression Pathsmentioning

confidence: 99%

Cancer progression modeling using static sample data

Sun¹,

Ye²,

Nowak³

et al. 2014

Genome Biol

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“…Many of these constitutively expressed ISGs have been characterized as antiviral genes (11). Recently, enhanced levels of constitutively expressed ISGs have been reported in advanced cancers and were often associated with a poor prognosis related to aggressive tumor growth, metastatic spread, resistance to IR/chemotherapy, or combinations of these factors (11)(12)(13)(14)(15)(16)(17)(18). The studies presented in this report are based on the hypothesis that a specific set of constitutively expressed ISGs, whose enhanced expression is by cytotoxic stress, confers a selective advantage to individual tumor clones (5,9,10,13,19).…”

mentioning

confidence: 99%

RIG-I–like receptor LGP2 protects tumor cells from ionizing radiation

Widau¹,

Parekh²,

Ranck³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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An siRNA screen targeting 89 IFN stimulated genes in 14 different cancer cell lines pointed to the RIG-I (retinoic acid inducible gene I)-like receptor Laboratory of Genetics and Physiology 2 (LGP2) as playing a key role in conferring tumor cell survival following cytotoxic stress induced by ionizing radiation (IR). Studies on the role of LGP2 revealed the following: (i) Depletion of LGP2 in three cancer cell lines resulted in a significant increase in cell death following IR, (ii) ectopic expression of LGP2 in cells increased resistance to IR, and (iii) IR enhanced LGP2 expression in three cell lines tested. Studies designed to define the mechanism by which LGP2 acts point to its role in regulation of IFNβ. Specifically (i) suppression of LGP2 leads to enhanced IFNβ, (ii) cytotoxic effects following IR correlated with expression of IFNβ inasmuch as inhibition of IFNβ by neutralizing antibody conferred resistance to cell death, and (iii) mouse embryonic fibroblasts from IFN receptor 1 knockout mice are radioresistant compared with wild-type mouse embryonic fibroblasts. The role of LGP2 in cancer may be inferred from cumulative data showing elevated levels of LGP2 in cancer cells are associated with more adverse clinical outcomes. Our results indicate that cytotoxic stress exemplified by IR induces IFNβ and enhances the expression of LGP2. Enhanced expression of LGP2 suppresses the IFN stimulated genes associated with cytotoxic stress by turning off the expression of IFNβ.innate immunity | cytoplasmic sensor | interferon beta | DHX58

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“…STAT1 regulates neural stem-cell function through transcriptional regulation of SOX9 (Imitola, 2013). STAT1 transcription factor activity enhances breast tumor growth and immune suppression (Hix et al, 2013). However, STAT1 was also reported to negatively regulate hepatocellular carcinoma cell proliferation (Chen et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Identification of differently expressed genes in leukemia using multiple microarray datasets

Zhang¹,

Xu²,

Guo³

et al. 2014

Genet. Mol. Res.

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ABSTRACT. The purpose of this study was to identify differentially expressed genes and analyze biological processes related to leukemia. A meta-analysis was performed using the Rank Product package of Gene Expression Omnibus datasets for leukemia. Next, Gene Ontology-enrichment analysis and pathway analysis were performed using the Gene Ontology website and Kyoto Encyclopedia of Genes and Genomes. A protein-protein interaction network was constructed using the Cytoscape software. Using the Rank Product package for leukemia, we identified a total of 1294 differentially expressed genes, 357 of which were not involved in individual differentially expressed genes. Gene Ontology-enrichment analyses showed that these 357 genes were enriched in biological processes such as mRNA metabolism, RNA splicing, and mRNA processing. Pathwayenrichment analysis showed that the genes were involved in the intestinal immune network for IgA production, endocytosis, and the mitogen-activated protein kinase signaling pathway. The proteinprotein interaction network indicated that HRAS, CD44, STAT1, SMAD2, and COPS5 were important in many interactions. Our study revealed genes that were consistently differentially expressed Differentially expressed genes in leukemia in leukemia, as well as the biological pathways and protein-protein interaction network associated with these genes.

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Tumor STAT1 Transcription Factor Activity Enhances Breast Tumor Growth and Immune Suppression Mediated by Myeloid-derived Suppressor Cells

Cited by 113 publications

References 60 publications

Cancer progression modeling using static sample data

Cancer progression modeling using static sample data

RIG-I–like receptor LGP2 protects tumor cells from ionizing radiation

Identification of differently expressed genes in leukemia using multiple microarray datasets

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