scholarly journals Glutathione-S-transferase-pi (GST-pi) expression in renal cell carcinoma

2015 ◽  
Vol 2 (1) ◽  
pp. 25-29 ◽  
Author(s):  
Christina Kaprilian ◽  
Maria Horti ◽  
Kosmas Kandilaris ◽  
Andreas Skolarikos ◽  
Nikolaos Trakas ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Multidrug Resistance ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Acquired Resistance ◽  
Tumor Grade ◽  
Glutathione S Transferase ◽  
Kidney Tumors ◽  
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Multidrug resistance correlates with unfavourable treatment outcomes in numerous cancers including renal cell carcinoma. The expression and clinical relevance of Glutathione-S-transferase-pi (GST-pi), a multidrug resistance factor, in kidney tumors remain controversial. We analyzed the expression of GST-pi in 60 formalin-fixed, paraffin-embedded renal cell carcinoma samples by immunohistochemistry and compared them with matched normal regions of the kidney. A significantly higher expression of GST-pi was observed in 87% of clear cell carcinoma and 50% of papillary subtypes. GST-pi expression did not correlate with tumor grade or patient survival. GST-pi is unlikely to be a prognostic factor for renal cell carcinoma. However, further studies with large number of samples are warranted to establish the role of GST-pi, if any, in intrinsic or acquired resistance of renal cell carcinoma to conventional treatments. Supplementary files: The supplementary files of this article are found under 'Article Tools' at the left side bar.

scholarly journals Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections

2020 ◽  
Author(s):  
Uwe Möginger ◽  
Niels Marcussen ◽  
Ole N. Jensen
Keyword(s):  
Mass Spectrometry ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cancer ◽  
Renal Cell ◽  
Mass Spectrometry Imaging ◽  
Tissue Sections ◽  
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AbstractPathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub)types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections.In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n=11), clear cell renal cell carcinoma (ccRCC, n=12) and chromophobe renal cell carcinoma (ChRCC, n=5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients.This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases.

scholarly journals Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

2021 ◽  
Author(s):  
Zahra Khodabakhshi ◽  
Mehdi Amini ◽  
Shayan Mostafaei ◽  
Atlas Haddadi Avval ◽  
Mostafa Nazari ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Clinical Features ◽  
Renal Cell ◽  
Radical Nephrectomy ◽  
Clinical Information ◽  
Tumor Grade ◽  
Imaging Biomarkers ◽  
Area Density

AbstractThe aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.

scholarly journals Modeling Renal Cell Carcinoma in Mice: Bap1 and Pbrm1 Inactivation Drive Tumor Grade

2017 ◽  
Vol 7 (8) ◽  
pp. 900-917 ◽  
Author(s):  
Yi-Feng Gu ◽  
Shannon Cohn ◽  
Alana Christie ◽  
Tiffani McKenzie ◽  
Nicholas Wolff ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Tumor Grade
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