Risk model- recurrence risk groups for patients with localised renal cell carcinoma

2019 ◽  
Vol 18 (11) ◽  
pp. e3550-e3551
Author(s):  
L. Polanco Pujol ◽  
F. Herranz Amo ◽  
J. Caño Velasco ◽  
J. Mayor De Castro ◽  
J. Aragon Chamizo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Risk Model ◽  
Risk Groups ◽  
Recurrence Risk

scholarly journals Prognostic Value of Plasma hPG80 (Circulating Progastrin) in Metastatic Renal Cell Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 375
Author(s):  
Manish Kohli ◽  
Winston Tan ◽  
Bérengère Vire ◽  
Pierre Liaud ◽  
Mélina Blairvacq ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Prognostic Impact ◽  
Significant Difference

Precise management of kidney cancer requires the identification of prognostic factors. hPG80 (circulating progastrin) is a tumor promoting peptide present in the blood of patients with various cancers, including renal cell carcinoma (RCC). In this study, we evaluated the prognostic value of plasma hPG80 in 143 prospectively collected patients with metastatic RCC (mRCC). The prognostic impact of hPG80 levels on overall survival (OS) in mRCC patients after controlling for hPG80 levels in non-cancer age matched controls was determined and compared to the International Metastatic Database Consortium (IMDC) risk model (good, intermediate, poor). ROC curves were used to evaluate the diagnostic accuracy of hPG80 using the area under the curve (AUC). Our results showed that plasma hPG80 was detected in 94% of mRCC patients. hPG80 levels displayed high predictive accuracy with an AUC of 0.93 and 0.84 when compared to 18–25 year old controls and 50–80 year old controls, respectively. mRCC patients with high hPG80 levels (>4.5 pM) had significantly lower OS compared to patients with low hPG80 levels (<4.5 pM) (12 versus 31.2 months, respectively; p = 0.0031). Adding hPG80 levels (score of 1 for patients having hPG80 levels > 4.5 pM) to the six variables of the IMDC risk model showed a greater and significant difference in OS between the newly defined good-, intermediate- and poor-risk groups (p = 0.0003 compared to p = 0.0076). Finally, when patients with IMDC intermediate-risk group were further divided into two groups based on hPG80 levels within these subgroups, increased OS were observed in patients with low hPG80 levels (<4.5 pM). In conclusion, our data suggest that hPG80 could be used for prognosticating survival in mRCC alone or integrated to the IMDC score (by adding a variable to the IMDC score or by substratifying the IMDC risk groups), be a prognostic biomarker in mRCC patients.

Recurrence risk groups after nephrectomy for renal cell carcinoma

2020 ◽  
Vol 44 (2) ◽  
pp. 111-118
Author(s):  
L. Polanco Pujol ◽  
F. Herranz Amo ◽  
J. Caño Velasco ◽  
D. Subirá Ríos ◽  
M. Moralejo Gárate ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Risk Groups ◽  
Recurrence Risk

Oncologic Surveillance After Surgical Resection for Renal Cell Carcinoma: A Novel Risk-Based Approach

2015 ◽  
Vol 33 (35) ◽  
pp. 4151-4157 ◽  
Author(s):  
Suzanne B. Stewart-Merrill ◽  
R. Houston Thompson ◽  
Stephen A. Boorjian ◽  
Sarah P. Psutka ◽  
Christine M. Lohse ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Risk Model ◽  
Recurrence Risk ◽  
Parametric Models ◽  
Competing Risk ◽  
Time To Failure ◽  
Risk Of Recurrence

Purpose The appropriate duration of surveillance for renal cell carcinoma (RCC) after radical or partial nephrectomy remains unknown, and evidence to support current guidelines are lacking. Herein, we provide an approach to surveillance that balances the risk of recurrence versus the risk of non-RCC death. Patients and Methods We identified 2,511 patients who underwent surgery for M0 RCC between 1990 and 2008. Patients were stratified for analysis by pathologic stage (pT1Nx-0, pT2Nx-0, pT3/4Nx-0, and pTanyN1), relapse location (abdomen, chest, bone, and other), age (< 50, 50 to 59, 60 to 69, 70-79 and ≥ 80 years), and Charlson comorbidity index (CCI; ≤ 1 and ≥ 2). Risks of disease recurrence and non-RCC death were estimated by using parametric models for time-to-failure with Weibull distributions. Surveillance duration was estimated at the point when the risk of non-RCC death exceeded the risk of recurrence. Results At a median follow-up of 9.0 years (interquartile range, 6.4 to 12.7 years), a total of 676 patients developed recurrence. By using a competing-risk model, vastly different surveillance durations were appreciated. Specifically, among patients with pT1Nx-0 disease and a CCI ≤ 1, risk of non-RCC death exceeded that of abdominal recurrence risk at 6 months in patients age 80 years and older but failed to do so for greater than 20 years in patients younger than age 50 years. For patients with pT1Nx-0 disease but a CCI ≥ 2, the risk of non-RCC death exceeded that of abdominal recurrence risk already at 30 days after surgery, regardless of patient age. Conclusion We present an individualized approach to RCC surveillance that bases the duration of follow-up on the interplay between competing risk factors of recurrence and non-RCC death. This strategy may improve the balance between the derived benefit from surveillance and medical resource allocation.

Prognostic risk model for advanced renal cell carcinoma (RCC) with immune-related genes

2020 ◽  
Author(s):  
Peng Cao ◽  
Jian-Dong Zhang ◽  
Ze-Jia Sun ◽  
Xiang Zheng ◽  
Bao-Zhong Yu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Cells ◽  
Renal Cell ◽  
Risk Model ◽  
Risk Groups ◽  
Cancer Genome ◽  
Low Risk ◽  
Landscape Analysis ◽  
Immune Related Genes

Abstract Background Renal cell carcinoma (RCC) is a common tumor of the urinary system. Nowadays, Immunotherapy is a hot topic in the treatment of solid tumors, especially for those tumors with pre-activated immune state. Methods In this study, we downloaded genomic and clinical data of RCC samples from The Cancer Genome Atlas (TCGA) database. Four immune-related genetic signatures were used to predict the prognosis of RCC by Cox regression analysis. We selected the most relevant genes from each signature to construct a prognostic risk model to predict prognosis via Kaplan-Meier (KM) survival analysis. And subgroups of the TCGA samples and external data from International Cancer Genome Consortium (ICGC) database were used to verify predictive stability of the model. We performed landscape analysis to assess the difference of gene mutant based on the data from TCGA. Finally, we explored the correlation between the selected genes and the level of tumor immune infiltration via Tumor Immune Estimation Resource (TIMER) platform. Results We found that the four prognostic risk models constructed by the signatures all could divide the RCC samples into high- and low-risk groups with significantly different prognosis, especially in advanced RCC. And the prognostic risk model was constructed by 8 candidate genes (HLA-B, HLA-A, HLA-DRA, IDO1, TAGAP, CIITA, PRF1 and CD8B) which divided the advanced RCC samples from TCGA database into high-risk and low-risk groups. And there was a significant difference in overall survival (OS) between the two groups. The validity of the model was verified by independent data from ICGC database. And the classification efficiency of the model was stable for the samples from different subgroups. landscape analysis showed that mutation ratios of some genes were different between two risk groups. In addition, the expression levels of the selected genes were significantly correlated with the infiltration degree of immune cells in the advanced RCC. Conclusions Sum up, eight immune-related genes were screened in our study to construct prognostic risk model with great predictive value for the prognosis of advanced RCC, and the genes were associated with infiltrating immune cells in tumors which have potential to conduct personalized treatment for advanced RCC.

A promising prognostic risk model for advanced renal cell carcinoma (RCC) with immune-related genes

2020 ◽  
Author(s):  
Peng Cao ◽  
Jian-Dong Zhang ◽  
Ze-Jia Sun ◽  
Xiang Zheng ◽  
Bao-Zhong Yu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Cells ◽  
Renal Cell ◽  
Risk Model ◽  
Risk Groups ◽  
Low Risk ◽  
Independent Data ◽  
Immune Related Genes ◽  
Genetic Signatures

Abstract Background Renal cell carcinoma (RCC) is a common tumor of the urinary system. Nowadays, immunotherapy is a hot topic in treatment of solid tumors, especially those with pre-activated immune state. Methods In this study, we have downloaded genomic and clinical data of RCC samples from TCGA database. Four immune-related genetic signatures were used to predict the prognosis of RCC by Cox regression analysis. We have established a prognostic risk model. The model consists of the genes most related to prognosis from the four genes signatures and aims at prognosis of the RCC samples via Kaplan-Meier survival analysis. Independent data from the ICGC database were used to test the predictive stability of the model. Furthermore, we have performed landscape analysis to assess the presence of mutations in the genes of interest in the RCC samples from the TCGA. Finally, we have explored the correlation between the selected genes and the level of tumor immune infiltration via TIMER platform. Results We have used four genetic signatures to construct prognostic risk models and found that each of the models divide the RCC samples into high- and low-risk groups, each of the groups correlating with significantly different prognosis, especially in the advanced RCC cases. A comprehensive prognostic risk model was constructed with eight candidate genes from four signatures (HLA-B, HLA-A, HLA-DRA, IDO1, TAGAP, CIITA, PRF1 and CD8B) dividing the advanced RCC samples from the TCGA database into high-risk and low-risk groups with a significant difference in the overall survival. The stability of the model was verified by independent data from the ICGC database. The samples from different subgroups. Landscape analysis showed that the mutation ratios in some genes were different between two risk groups. Besides, the expression levels of the selected genes were interrelated with the infiltration degree of the immune cells in the advanced RCC. ConclusionsEight immune-related genes were screened in our study to construct a promising prognostic risk model with a great predictive value for the prognosis of advanced RCC. The selected genes were associated with infiltrating immune cells in tumors which presents a chance for personalized treatment for advanced RCC.

scholarly journals Geographic Variation of Chronic Kidney Disease Prevalence: Correlation with the Incidence of Renal Cell Carcinoma or Urothelial Carcinoma?

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Yit-Sheung Yap ◽  
Kai-Wen Chuang ◽  
Chun-Ju Chiang ◽  
Hung-Yi Chuang ◽  
Sheng-Nan Lu
Keyword(s):  
Chronic Kidney Disease ◽  
Renal Cell Carcinoma ◽  
Kidney Disease ◽  
Urothelial Carcinoma ◽  
Cell Carcinoma ◽  
Late Stage ◽  
Renal Cell ◽  
Risk Groups ◽  
Incidence Rates ◽  
Geographic Variations

Background. The aim of this study is to evaluate whether geographic variations in the prevalence of late-stage chronic kidney disease (CKD) exist and are associated with incidence rates of renal cell carcinoma (RCC), upper tract urothelial carcinoma (UTUC), or lower tract urothelial carcinoma (LTUC).Methods. Prevalence rates of late-stage CKD for 366 townships (n>30) in Taiwan were calculated for 1,518,241 and 1,645,151 subjects aged 40 years or older in years 2010 and 2009, respectively. Late-stage CKD prevalence in year 2010 was used as a training set and its age-adjusted standardized morbidity rates (ASMR) were divided into three groups as defined <1.76%, 1.76% ≤ ASMR < 2.64%, and ≥2.64%, respectively. Year 2009, defined as the validation set, was used to validate the results.Results. The ASMR of late-stage CKD in years 2010 and 2009 were 1.76%, and 2.09%, respectively. Geographic variations were observed, with notably higher rates of disease in areas of the central, southwestern mountainside, and southeastern seaboard. There were no significant differences among different combined risk groups of RCC, UTUC, and LTUC incidence.Conclusion. The substantial geographic variations in the prevalence of late-stage CKD exist, but are not correlated with RCC, UTUC, or LTUC incidence.

Analysis of molecular profiling of renal cell carcinoma: Identification of a 4-microRNA signature as a prognostic value in patients with stage I-II disease.

2013 ◽  
Vol 31 (6_suppl) ◽  
pp. 395-395 ◽  
Author(s):  
Daniel E. Castellano ◽  
Carlos A. Farfán ◽  
Gamez Angelo ◽  
Juan M. Sepúlveda ◽  
Guillermo De Velasco ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Mirna Expression ◽  
Renal Cell ◽  
Recurrence Risk ◽  
Stage I ◽  
Risk Of Relapse ◽  
Mirna Expression Signature

395 Background: Renal-cell carcinoma (RCC) accounts for 3% of all cancers and produces over 12,000 deaths every year in the EE.UU. Currently, there is a need to identify stage I/II RCC patients with high risk of relapse following nephrectomy, given that these patients do not receive adjuvant treatment and ≈ 25% of them relapse. MicroRNAs (miRNAs) are a class of small noncoding RNAs that control gene expression by targeting mRNA and playing an important role as regulators of gene expression during tumorigenesis. Our willing is to define a miRNA expression profile associated with a high risk of relapse in early RCC. Methods: We analyzed 113 pts. with RCC stage I-II of a local data-base who had undergone nephrectomy from 2000 to 2008. RNA was extracted from FFPE samples using RecoverAll (Ambion). RNA samples were hybridized to Human miRNA Microarray Release 14.0, 8x15K (Agilent Technologies. Data were normalized using Quantile Normalization. Only 396 miRNAs with detectable signal in at least 10% of the hybridized samples were considered for further analysis. Identification of miRNAs related with recurrence risk and subsequent developing and validation of miRNA expression-based prediction models of recurrence risk were performed in BRB-ArrayTools v4.2.1. Results: We identified a 4-miRNA expression signature that distinguishes early stage RCC patients with low and high recurrence risk (p value = 0.0013; HR = 4.68 [1.82-12.0]). Distant recurrence free survival rate at five years was 97.4 and 81.2 for the low and high recurrence risk groups respectively. High levels of miR-424 were related with a high recurrence risk (p = 0.023). Conclusions: The TNM staging system lacks accuracy to identify prognostic markers of survival in early RCC. Our results suggest that specific miRNAs are involved in the recurrence of early disease. We have found a miRNA expression signature that identifies patients with high risk of developing distant metastasis using the FFPE sample of primary tumors. High expression levels of miR-424 were related with a high recurrence risk. MiR-424 is a hypoxia induced miRNA whose expression has been related with HIF-1α and HIF-2α stabilization and angiogenesis induction.

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