scholarly journals The immune infiltration in clear cell Renal Cell Carcinoma and their clinical implications: A study based on TCGA and GEO databases

2020 ◽  
Vol 11 (11) ◽  
pp. 3207-3215 ◽  
Author(s):  
Qiufeng Pan ◽  
Longwang Wang ◽  
Shuaishuai Chai ◽  
Hao Zhang ◽  
Bing Li
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Clinical Implications ◽  
Cell Renal Cell Carcinoma ◽  
Immune Infiltration

scholarly journals Metabolic features of clear-cell renal cell carcinoma: mechanisms and clinical implications

2011 ◽  
Vol 5 (4) ◽  
pp. 274-282 ◽  
Author(s):  
Jehonathan H. Pinthus ◽  
Kaitlyn F. Whelan ◽  
Daniel Gallino ◽  
Jian-Ping Lu ◽  
Nathan Rothschild
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Clinical Implications ◽  
Cell Renal Cell Carcinoma

scholarly journals Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yusa Chen ◽  
Yumei Liang ◽  
Ying Chen ◽  
Shaxi Ouyang ◽  
Kanghan Liu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Random Forest ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Renal Cell ◽  
Multivariate Regression ◽  
Clinical Characteristics ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Immune Infiltration

Background. Clear cell renal cell carcinoma (ccRCC) is a cancer with abnormal metabolism. The purpose of this study was to investigate the effect of metabolism-related genes on the prognosis of ccRCC patients. Methods. The data of ccRCC patients were downloaded from the TCGA and the GEO databases and clustered using the nonnegative matrix factorization method. The limma software package was used to analyze differences in gene expression. A random forest model was used to screen for important genes. A novel Riskscore model was established using multivariate regression. The model was evaluated based on the metabolic pathway, immune infiltration, immune checkpoint, and clinical characteristics. Results. According to metabolism-related genes, kidney clear cell carcinoma (KIRC) datasets downloaded from TCGA were clustered into two groups and showed significant differences in prognosis and immune infiltration. There were 667 differentially expressed genes between the two clusters, of which 408 were screened by univariate analysis. Finally, 12 differentially expressed genes (MDK, SLC1A1, SGCB, C4orf3, MALAT1, PILRB, IGHG1, FZD1, IFITM1, MUC20, KRT80, and SALL1) were filtered out using the random forest model. The model of Riskscore was obtained by multiplying the expression levels of these 12 genes with the corresponding coefficients of the multivariate regression. We found that the Riskscore correlated with the expression of these 12 genes; the high Riskscore matched the low survival rate verified in the verification set. The analysis found that the Riskscore model was associated with most of the metabolic processes, immune infiltration of cells such as plasma cells, immune checkpoints such as PD-1, and clinical characteristics such as M stage. Conclusion. We established a new Riskscore model for the prognosis of ccRCC based on metabolism. The genes in the model provided several novel targets for the study of ccRCC.

JAK3 in clear cell renal cell carcinoma: Mutational screening and clinical implications

2013 ◽  
Vol 31 (6) ◽  
pp. 930-937 ◽  
Author(s):  
Michela de Martino ◽  
Maddalena Gigante ◽  
Luigi Cormio ◽  
Clelia Prattichizzo ◽  
Elisabetta Cavalcanti ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Clinical Implications ◽  
Cell Renal Cell Carcinoma ◽  
Mutational Screening

scholarly journals Metabolic features of clear-cell renal cell carcinoma: mechanisms and clinical implications

2013 ◽  
Vol 5 (4) ◽  
pp. 274
Author(s):  
Jehonathan H. Pinthus H. Pinthus ◽  
Kaitlyn F. Whelan ◽  
Daniel Gallino ◽  
Jian-Ping Lu ◽  
Nathan Rothschild
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Cancer Cells ◽  
Renal Cell ◽  
Clear Cell ◽  
Response To Therapy ◽  
Clinical Implications ◽  
Cell Renal Cell Carcinoma ◽  
Normal Cells ◽  
Dietary Nutrients

Central to the malignant behaviour that endows cancer cells withgrowth advantage is their unique metabolism. Cancer cells canprocess nutrient molecules differently from normal cells and useit to overcome stress imposed on them by various therapies. Thismetabolic conversion is controlled by specific genetic mutationsthat are associated with activation of oncogenes and loss of tumoursuppressor proteins. Understanding these processes is importantas it can lead to the discovery of biomarkers that can predict theaggressiveness of the disease and its response to therapy, and evenmore importantly, to the development of novel therapeutics. A classictumour in this respect is clear-cell renal cell carcinoma (RCC). Inthis review, we will begin with a brief summary of normal cellularbioenergetic pathways, which will be followed by a descriptionof the characteristic metabolism of glucose and lipids in clear-cellRCC cells and its clinical implications. Data relating to the potentialeffect of dietary nutrients on RCC will also be reviewed alongwith potential therapies targeted at interrupting specific metabolicpathways in clear-cell RCC.Le métabolisme unique des cellules cancéreuses est au coeur ducomportement malin qui leur confère un avantage sur le plan de lacroissance. Les cellules cancéreuses peuvent traiter les moléculesde nutriment différemment des cellules normales et utilisent cesmolécules pour surmonter le stress imposé par les différents traitements.La conversion métabolique est contrôlée par des mutationsgénétiques précises associées à l’activation d’oncogènes et à laperte de protéines de suppression tumorale. Il est important debien saisir ces processus, car leur élucidation peut mener à ladécouverte de biomarqueurs permettant de prédire l’agressivité dela maladie et la réponse au traitement et, fait encore plus important,elle peut mener à la mise au point de nouveaux médicaments. À cetégard, l’hypernéphrome à cellules claires représente une tumeurclassique. Dans cet article, nous commençons par résumer brièvementles voies bioénergétiques cellulaires normales, puis nouspoursuivons avec une description du métabolisme caractéristiquedu glucose et des lipides dans les cellules de l’hypernéphrome àcellules claires et ses répercussions cliniques. Les données associéesà l’effet potentiel des nutriments sur l’hypernéphrome à cellulesclaires seront aussi passées en revue, ainsi que les thérapiesciblées potentielles visant l’interruption de voies métaboliquesparticulières dans l’hypernéphrome à cellules claires.

scholarly journals Clinical Implications of HSC70 Expression in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 18 (1) ◽  
pp. 239-244
Author(s):  
Yixiao Zhang ◽  
Xudong Zhu ◽  
Xinbo Qiao ◽  
Lisha Sun ◽  
Tianhui Xia ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Clinical Implications ◽  
Cell Renal Cell Carcinoma

scholarly journals Prognostic significance and tumor-immune infiltration of mTOR in clear cell renal cell carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11901
Author(s):  
Na Li ◽  
Jie Chen ◽  
Qiang Liu ◽  
Hongyi Qu ◽  
Xiaoqing Yang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Univariate Analysis ◽  
Prognostic Significance ◽  
Clinical Role ◽  
Cell Renal Cell Carcinoma ◽  
Cancer Specific Survival ◽  
Immune Infiltration

Mammalian target of rapamycin (mTOR), a serine/threonine kinase involved in cell proliferation, survival, metabolism and immunity, was reportedly activated in various cancers. However, the clinical role of mTOR in renal cell carcinoma (RCC) is controversial. Here we detected the expression and prognosis of total mTOR and phosphorylated mTOR (p-mTOR) in clear cell RCC (ccRCC) patients, and explored the interactions between mTOR and immune infiltrates in ccRCC. The protein level of mTOR and p-mTOR was determined by western blotting (WB), and their expression was evaluated in 145 ccRCC and 13 non-tumor specimens by immunohistochemistry (IHC). The relationship to immune infiltration of mTOR was further investigated using TIMER and TISIDB databases, respectively. WB demonstrated the ratio of p-mTOR to mTOR was higher in ccRCC than adjacent specimens (n = 3), and IHC analysis elucidated that p-mTOR expression was positively correlated with tumor size, stage and metastasis status, and negatively correlated with cancer-specific survival (CSS). In univariate analysis, high grade, large tumor, advanced stage, metastasis, and high p-mTOR expression were recognized as prognostic factors of poorer CSS, and multivariate survival analysis elucidated that tumor stage, p-mTOR and metastasis were of prognostic value for CSS in ccRCC patients. Further TIMER and TISIDB analyses uncovered that mTOR gene expression was significantly associated with numerous immune cells and immunoinhibitors in patients with ccRCC. Collectively, these findings revealed p-mTOR was identified as an independent predictor of poor survival, and mTOR was associated with tumor immune infiltrates in ccRCC patients, which validated mTOR could be implicated in the initiation and progression of ccRCC.

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