Identification of S100A14 as a metastasis-promoting molecule in a murine organotropic metastasis model

2019 ◽  
Vol 36 (4) ◽  
pp. 411-422
Author(s):  
Takashi Sugino ◽  
Naoki Ichikawa-Tomikawa ◽  
Mizuko Tanaka ◽  
Namiko Shishito ◽  
Tomiko Miura ◽  
...  
Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Aira Matsugaki ◽  
Yumi Kimura ◽  
Ryota Watanabe ◽  
Fumihito Nakamura ◽  
Ryo Takehana ◽  
...  

Malignant melanoma favors spreading to bone, resulting in a weakened bone with a high fracture risk. Here, we revealed the disorganized alignment of apatite crystals in the bone matrix associated with the homing of cancer cells by developing an artificially controlled ex vivo melanoma bone metastasis model. The ex vivo metastasis model reflects the progressive melanoma cell activation in vivo, resulting in decreased bone mineral density and expression of MMP1-positive cells. Moreover, less organized intercellular connections were observed in the neighboring osteoblasts in metastasized bone, indicating the abnormal and randomized organization of bone matrix secreted by disconnected osteoblasts. Our study revealed that the deteriorated microstructure associated with disorganized osteoblast arrangement was a determinant of malignant melanoma-related bone dysfunction.


Oncology ◽  
2000 ◽  
Vol 59 (1) ◽  
pp. 75-80 ◽  
Author(s):  
Hiroki Wakabayashi ◽  
Hiroshige Hibasami ◽  
Kohji Iida ◽  
Norifumi Satoh ◽  
Takashi Yamazaki ◽  
...  

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Wonyoung Kang ◽  
Leigh Maher ◽  
Michael Michaud ◽  
Seong-Woo Bae ◽  
Seongyeong Kim ◽  
...  

Abstract Background Gastric cancer metastasis is a highly fatal disease with a five-year survival rate of less than 5%. One major obstacle in studying gastric cancer metastasis is the lack of faithful models available. The cancer xenograft mouse models are widely used to elucidate the mechanisms of cancer development and progression. Current procedures for creating cancer xenografts include both heterotopic (i.e., subcutaneous) and orthotopic transplantation methods. Compared to the heterotopic model, the orthotopic model has been shown to be the more clinically relevant design as it enables the development of cancer metastasis. Although there are several methods in use to develop the orthotopic gastric cancer model, there is not a model which uses various types of tumor materials, such as soft tissues, semi-liquid tissues, or culture derivatives, due to the technical challenges. Thus, developing the applicable orthotopic model which can utilize various tumor materials is essential. Results To overcome the known limitations of the current orthotopic gastric cancer models, such as exposure of tumor fragments to the neighboring organs or only using firm tissues for the orthotopic implantation, we have developed a new method allowing for the complete insertion of soft tissue fragments or homogeneously minced tissues into the stomach submucosa layer of the immunodeficient NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mouse. With this completely-closed transplantation method, tumors with various types of tissue may be used to establish orthotopic gastric cancer models without the risks of exposure to nearby organs or cell leakage. This surgical procedure was highly reproducible in generating forty-eight mouse models with a surgery success rate of 96% and tumor formation of 93%. Among four orthotopic patient-derived xenograft (PDX) models that we generated in this study, we verified that the occurrence of organotropic metastasis in either the liver or peritoneal cavity was the same as that of the donor patients. Conclusion Here we describe a new protocol, step by step, for the establishment of orthotopic xenograft of gastric cancer. This novel technique will be able to increase the use of orthotopic models in broader applications for not only gastric cancer research but also any research related to the stomach microenvironment.


2007 ◽  
Vol 96 (10) ◽  
pp. 1526-1531 ◽  
Author(s):  
W C M Duivenvoorden ◽  
S Vukmirović-Popović ◽  
M Kalina ◽  
E Seidlitz ◽  
G Singh

2021 ◽  
Author(s):  
Zhaolin Yang ◽  
Jiale Zhou ◽  
Yizheng Xue ◽  
Yu Zhang ◽  
Kaijun Zhou ◽  
...  

Abstract Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.


2014 ◽  
Vol 13 (1) ◽  
pp. e188
Author(s):  
M.P. Valta ◽  
H. Zhao ◽  
A. Ingels ◽  
A.E. Thong ◽  
R. Nolley ◽  
...  

2002 ◽  
Vol 89 (10) ◽  
pp. 1302-1309 ◽  
Author(s):  
E. A. te Velde ◽  
J. M. Vogten ◽  
M. F. G. B. Gebbink ◽  
J. M. van Gorp ◽  
E. E. Voest ◽  
...  

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