Distribution of 16 trace elements in renal cell carcinoma tissue and adjacent-normal tissue

2016 ◽  
Vol 33 (10) ◽  
pp. 148-154
Author(s):  
Linlin Zhang ◽  
Hongwei Yang ◽  
Shengkun Sun ◽  
Axiang Xu
Keyword(s):  
Renal Cell Carcinoma ◽  
Trace Elements ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Normal Tissue ◽  
Adjacent Normal Tissue ◽  
Carcinoma Tissue ◽  
Renal Cell Carcinoma Tissue

Differential Radioactive Proteomic Analysis of Microdissected Renal Cell Carcinoma Tissue by 54 cm Isoelectric Focusing in Serial Immobilized pH Gradient Gels

2005 ◽  
Vol 4 (6) ◽  
pp. 2117-2125 ◽  
Author(s):  
Slobodan Poznanović ◽  
Wojciech Wozny ◽  
Gerhard P. Schwall ◽  
Chaturvedula Sastri ◽  
Christian Hunzinger ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Isoelectric Focusing ◽  
Proteomic Analysis ◽  
Renal Cell ◽  
Ph Gradient ◽  
Carcinoma Tissue ◽  
Renal Cell Carcinoma Tissue

scholarly journals Human renal-cell carcinoma tissue contains dendritic cells

1996 ◽  
Vol 68 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Martin Thurnher ◽  
Christian Radmayr ◽  
Reinhold Ramoner ◽  
Susanne Ebner ◽  
Günther Böck ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Dendritic Cells ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Human Renal Cell Carcinoma ◽  
Carcinoma Tissue ◽  
Renal Cell Carcinoma Tissue

scholarly journals 3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Tae-Yun Kim ◽  
Nam-Hoon Cho ◽  
Goo-Bo Jeong ◽  
Ewert Bengtsson ◽  
Heung-Kook Choi
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Texture Analysis ◽  
Renal Cell ◽  
Laser Scanning ◽  
Texture Features ◽  
Confocal Laser ◽  
Carcinoma Tissue ◽  
Grade Classification ◽  
Renal Cell Carcinoma Tissue

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system.

T Lymphocyte Recruitment into Renal Cell Carcinoma Tissue: A Role for Chemokine Receptors CXCR3, CXCR6, CCR5, and CCR6

2012 ◽  
Vol 61 (2) ◽  
pp. 385-394 ◽  
Author(s):  
Kimberley A. Oldham ◽  
Greg Parsonage ◽  
Rupesh I. Bhatt ◽  
D. Michael A. Wallace ◽  
Nayneeta Deshmukh ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
T Lymphocyte ◽  
Renal Cell ◽  
Chemokine Receptors ◽  
Carcinoma Tissue ◽  
Renal Cell Carcinoma Tissue

scholarly journals MiR-122 promotes renal cancer cell proliferation by targeting Sprouty2

Tumor Biology ◽  
2017 ◽  
Vol 39 (2) ◽  
pp. 101042831769118 ◽  
Author(s):  
Zijie Wang ◽  
Chao Qin ◽  
Jing Zhang ◽  
Zhijian Han ◽  
Jun Tao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Carcinoma Cell ◽  
Tissue Samples ◽  
Renal Cell Carcinoma Cell ◽  
Carcinoma Tissue ◽  
Primary Renal Cell Carcinoma ◽  
Renal Cell Carcinoma Tissue

MicroRNAs are short non-coding RNAs, which have been implicated in several biological processes. Aberrant expression of the microRNA miR-122 has frequently been reported in malignant cancers. However, the mechanism underlying the effects of miR-122 in renal cell carcinoma remains unknown. The aim of this study was to determine the biological function of miR-122 in renal cell carcinoma and to identify a novel molecular target regulated by miR-122. We measured the expression levels of Sprouty2 in six renal cell carcinoma tissue samples and adjacent non-tumor tissues by western blot analysis. We then used reverse transcription polymerase chain reaction to measure miR-122 levels in 40 primary renal cell carcinoma and adjacent non-malignant tissue samples. The effects of miR-122 down-regulation or Sprouty2 knockdown were evaluated via Cell Counting Kit-8 assay, flow cytometry, and western blot analysis. The relationship between miR-122 and Sprouty2 was determined using dual-luciferase reporter assays. Sprouty2 was down-regulated in renal cell carcinoma tissue samples compared with adjacent normal tissue. In contrast, miR-122 was up-regulated in primary renal cell carcinoma tissue samples compared with adjacent normal tissue samples. Down-regulation of miR-122 substantially weakened the proliferative ability of renal cell carcinoma cell lines in vitro. In contrast, Sprouty2 knockdown promoted the in vitro proliferation of renal cell carcinoma cell lines. The spry2 gene could therefore be a direct target of miR-122. In conclusion, miR-122 could act as a tumor promoter and potentially target Sprouty2. MiR-122 promotes renal cell carcinoma cell proliferation, migration, and invasion and could be a molecular target in novel therapies for renal cell carcinoma.

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