NOD‐like receptor C4 Inflammasome Regulates the Growth of Colon Cancer Liver Metastasis in NAFLD

Ohashi, Kenichi; Wang, Zhijun; Yang, Yoon Mee; Billet, Sandrine; Tu, Wei; Pimienta, Michael; Cassel, Suzanne L.; Pandol, Stephen J.; Lu, Shelly C.; Sutterwala, Fayyaz S.; Bhowmick, Neil A.; Seki, Ekihiro

doi:10.1002/hep.30693

Cited by 76 publications

(46 citation statements)

References 46 publications

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“…Some animal research also presented a different conclusion. The murine models revealed that hepatic steatosis induced by a high-fat diet increased the metastatic tumor burden of colon cancer tumors metastatic to the liver, and the changes in the liver microenvironment caused by fatty liver established a more sensitive microenvironment for metastasis when compared with normal liver (38)(39)(40). However, the opposite conclusion was discovered in a rat model (41).…”

Section: Discussionmentioning

confidence: 99%

“…More importantly, this study explored the role of NLRC4 and interleukin (IL)-1β in the growth of metastatic liver tumors under NAFLD-related liver microenvironment, emphasizing the effect of inflammation changes in NAFLD on the metastatic microenvironment. The study concluded that in NAFLD, NLRC4 contributes to M2 TAMs polarization, IL-1β and vascular endothelial growth factor (VEGF) production in TAMs, which promotes the growth of metastatic liver tumors in CRC (40).…”

Section: Discussionmentioning

confidence: 99%

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Effect of Non-alcoholic Fatty Liver Disease on the Risk of Synchronous Liver Metastasis: Analysis of 451 Consecutive Patients of Newly Diagnosed Colorectal Cancer

Zhang

2020

Front. Oncol.

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Background: The purpose of this study was to investigate the effect of non-alcoholic fatty liver disease (NAFLD) on the risk of synchronous colorectal liver metastasis (synCRLM).Methods: A retrospective analysis was performed on 451 consecutive patients with newly diagnosed colorectal cancer (CRC) from January 2014 to January 2019. According to the presence of NAFLD, the CRC patients were divided into two groups, NAFLD group (60 cases) and the control group (391 cases). The clinicopathological features and the prevalence of synCRLM between the two groups were compared. Logistic regression analysis was used to analyze the risk factors of synCRLM. Different non-invasive liver fibrosis scoring models were used to evaluate the effect of advanced fibrosis and cirrhosis stage in NAFLD on the prevalence of synCRLM.Results: The prevalence of synCRLM was significantly higher in patients with NAFLD than that in patients without NAFLD (18.33 vs. 7.42%; χ 2 = 7.669, P = 0.006). A logistic regression analysis indicated that NAFLD, CEA, CA19-9, and lymph node status were risk factors for synCRLM, and NAFLD showed the highest hazard ratio (3.930 [95% confidence interval: 1.616 ∼ 9.560]). In NAFLD patients, both fibrosis-4 index (FIB-4) and NAFLD fibrosis score (NFS) were significantly lower in those with synCRLM compared to those without synCRLM [FIB-4: 1.246 (0.833 ∼ 1.276) vs. 1.436 (1.016 ∼ 2.699), Z = −2.130, P = 0.033; NFS: −1.282 (−2.407 ∼ −0.262) vs. −0.255 (−1.582 ∼ 0.755), Z = −2.302, P = 0.021; Mann-Whitney test].Conclusion: NAFLD may be associated with increased liver metastasis, and for NAFLD-related advanced liver fibrosis and cirrhosis may be associated with reduced synchronous liver metastasis in CRC patients. However, the correlation between simple steatosis and steatohepatitis remains to be further determined. Certain factors such as NAFLD, lymph node metastasis, elevated levels of preoperative CEA and CA19-9 are suggesting a high risk of synCRLM.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Effect of Non-alcoholic Fatty Liver Disease on the Risk of Synchronous Liver Metastasis: Analysis of 451 Consecutive Patients of Newly Diagnosed Colorectal Cancer

Zhang

2020

Front. Oncol.

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“…www.nature.com/scientificreports www.nature.com/scientificreports/ cancer tumorigenesis and participates in anti-apoptotic pathways downstream of p53 75 . In addition, a recent study showed that NLRC4 mediates M2 TAM infiltration and angiogenesis through VEGF production in metastatic colon cancer 76 .…”

Section: Characterization Of Genes and Pathways Involved In Patients'mentioning

confidence: 99%

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Gliozzo

Perlasca

Mesiti

et al. 2020

Sci Rep

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Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification. Phenotype and outcome prediction using sets of selected biomarkers are well-established prediction tasks in the context of computational biology, including different prediction problems ranging from the response to a specific drug 1,2 , diagnosis and prognosis 3-5 , classification of cancer subtypes 6 , outcome and recurrence prediction 7-9 and other related prediction problems 10. State-of-the-art methods for these problems are largely based on inductive supervised models that use sets of selected biomarkers, usually represented as vectors, to predict the phenotype or outcome of interest (see, e.g. 11-13), without taking into account the relationships between individuals. Several works proposed "network-based" methods by constructing graphs of patients, in order to discover the underlying structure of the data (e.g. discovery of subtypes of diseases, clinical stratification of patients) 14-17. These methods mainly used unsupervised approaches and hence have been not specifically designed and are not appropriate for phenotype/outcome prediction problems. Recently a few works proposed semi-supervised "network-based" approaches for the prediction of the phenotype/outcome of patients, on the basis of their bio-molecular profiles (e.g. gene expression of genotypic profiles) 18,19 , including also methods able to integrate multiple sources of omics data 20 , and methods based on Supervised Random Walks 21 , specifically modified for the classification of tumors 22. In this work, we introduce a novel network-based method for modeling in the "patient space". In this context the nodes of the network represent patients through an n-dimensional set of biomarker values (e.g. a set of gene expression values), and edges represent similarities between the biomarkers of a pair of patients. Hence, this "patient-space" differs from the classical "biomarker-space", where nodes represent biomarkers and edges similarities between biomarkers and not between patients 23,24...

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“…Recent reports have also implicated NLRC4 in tumor progression. NLRC4 and Caspase-1 were found to promote the metastasis of breast and colon cancer in obese mice, but not those fed on normal-fat diets (Kolb et al, 2016;Ohashi et al, 2019). In contrast, NLRC4 and Caspase-1 were reported to protect mice from chemically-induced colon cancer (AOM/DSS) (Hu et al, 2010), although another report found that Caspase-1 but not NLRC4 was protective in the same model (Allen et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

New mutant mouse models clarify the role of NAIPs, phosphorylation, NLRP3, and tumors in NLRC4 inflammasome activation

Tenthorey

Chavez²,

Thompson³

et al. 2019

Preprint

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The NAIP/NLRC4 inflammasome is a cytosolic sensor of bacteria that activates Caspase-1 and initiates potent downstream immune responses. Structural, biochemical, and genetic data all demonstrate that the NAIP proteins act as receptors for specific bacterial ligands, while NLRC4 is a downstream adaptor protein that multimerizes with NAIPs to form a macromolecular structure called an inflammasome. However, several aspects of NLRC4 biology remain unresolved. For example, in addition to its clear function in responding to bacteria, NLRC4 has also been proposed to initiate anti-tumor responses, though the underlying mechanism is unknown. NLRC4 has also been shown to be phosphorylated on serine 533, and this modification was suggested to be important for NLRC4 function. In the absence of S533 phosphorylation, it was further proposed that another inflammasome component, NLRP3, can induce NLRC4 activation. We generated a new Nlrc4deficient mouse line as well as mice encoding phosphomimetic S533D and nonphosphorylatable S533A NLRC4 proteins. Using these genetic models in vivo and in vitro, we fail to observe a role for phosphorylation in NLRC4 inflammasome function.Furthermore, we find no role for NLRP3 in NLRC4 function, or for NLRC4 in a model of melanoma. These results simplify and clarify our understanding of the mechanism of NAIP/NLRC4 activation and its biological functions.

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NOD‐like receptor C4 Inflammasome Regulates the Growth of Colon Cancer Liver Metastasis in NAFLD

Cited by 76 publications

References 46 publications

Effect of Non-alcoholic Fatty Liver Disease on the Risk of Synchronous Liver Metastasis: Analysis of 451 Consecutive Patients of Newly Diagnosed Colorectal Cancer

Effect of Non-alcoholic Fatty Liver Disease on the Risk of Synchronous Liver Metastasis: Analysis of 451 Consecutive Patients of Newly Diagnosed Colorectal Cancer

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

New mutant mouse models clarify the role of NAIPs, phosphorylation, NLRP3, and tumors in NLRC4 inflammasome activation

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