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

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.

Download Full-text

Genomic alterations to refine prognostication of patients with metastatic renal cell carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2018.36.6_suppl.626 ◽

2018 ◽

Vol 36 (6_suppl) ◽

pp. 626-626 ◽

Cited By ~ 1

Author(s):

Dominick Bosse ◽

Wanling Xie ◽

Aly-Khan A. Lalani ◽

Guillermo de Velasco ◽

Martin Henner Voss ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Metastatic Renal Cell Carcinoma ◽

Renal Cell ◽

Prognostic Value ◽

Kinase Inhibitors ◽

Cox Regression ◽

Risk Groups ◽

Genomic Alterations ◽

Endothelial Growth Factor

626 Background: The IMDC risk score is a valid and simple tool to prognosticate patients (pts) with metastatic renal cell carcinoma (mRCC). Some non-VHL common genomic alterations may be associated with outcomes. We therefore assessed the prognostic value of most commonly mutated genes in mRCC beside VHL overall, and within IMDC risk groups. Methods: We identified patients treated at Dana-Farber Cancer Institute (n = 65) or part of TCGA (n = 33) who had genomic data available and were treated with first line vascular endothelial growth factor tyrosine kinase inhibitors. Information on genomic alterations (GA) focused on PBRM1, BAP1, SETD2, KDM5C and TP53 was extracted. Cox regression was performed to assess the association of each GA with overall survival (OS), adjusting for IMDC risk groups and age. Results: Overall, 98 pts were identified. 96/98 pts had clear-cell histology. Pts distribution by IMDC risk groups was: 7% good, 58% intermediate, 27% poor and 8% unknown. Mutation rates were 27% PBRM1, 17% BAP1, 29% SETD2, 9% KDM5C and 8% TP53. In multivariable models, there was an association between GA and worse OS for BAP1 and BAP1 or TP53 combined (Table). When stratified by IMDC risk groups, GA in BAP1 or TP53 was associated with shorter median OS in poor risk pts [12.1 mo (95%CI 8.3- 24.0) v. 27.6 mo (95%CI 18.9- 53.4), aHR 4.64 (95%CI 1.32-16.4), p = 0.017] and a trend toward worse median OS in intermediate risk pts [20.5 mo (95%CI 7.4-54.6) v. 36.3 mo (95%CI 21.1, NR), aHR 2.11 (95%CI 0.94-4.74)] compared to pts without GA in BAP1 or TP53. Too few death events were observed in good risk pts to assess the prognostic value of GA in BAP1 or TP53. Conclusions: GA in BAP1 or TP53 are prognostic in mRCC and further discriminate pts with distinct outcomes within IMDC risk groups. Validation in larger dataset is ongoing. [Table: see text]

Download Full-text

A novel prognostic cancer-related lncRNA signature in papillary renal cell carcinoma

Cancer Cell International ◽

10.1186/s12935-021-02247-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Binghai Chen ◽

Di Dong ◽

Qin Yao ◽

Yuanzhang Zou ◽

Wei Hu

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

High Efficiency ◽

Risk Model ◽

Cox Regression ◽

Papillary Renal Cell Carcinoma ◽

Clinical Feature ◽

Roc Curve Analysis ◽

Significant Difference

Abstract Background Papillary renal cell carcinoma (pRCC) ranks second in renal cell carcinoma and the prognosis of pRCC remains poor. Here, we aimed to screen and identify a novel prognostic cancer-related lncRNA signature in pRCC. Methods The RNA-seq profile and clinical feature of pRCC cases were downloaded from TCGA database. Significant cancer-related lncRNAs were obtained from the Immlnc database. Differentially expressed cancer-related lncRNAs (DECRLs) in pRCC were screened for further analysis. Cox regression report was implemented to identify prognostic cancer-related lncRNAs and establish a prognostic risk model, and ROC curve analysis was used to evaluate its precision. The correlation between RP11-63A11.1 and clinical characteristics was further analyzed. Finally, the expression level and role of RP11-63A11.1 were studied in vitro. Results A total of 367 DECRLs were finally screened and 26 prognostic cancer-related lncRNAs were identified. Among them, ten lncRNAs (RP11-573D15.8, LINC01317, RNF144A-AS1, TFAP2A-AS1, LINC00702, GAS6-AS1, RP11-400K9.4, LUCAT1, RP11-63A11.1, and RP11-156L14.1) were independently associated with prognosis of pRCC. These ten lncRNAs were incorporated into a prognostic risk model. In accordance with the median value of the riskscore, pRCC cases were separated into high and low risk groups. Survival analysis indicated that there was a significant difference on overall survival (OS) rate between the two groups. The area under curve (AUC) in different years indicated that the model was of high efficiency in prognosis prediction. RP11-63A11.1 was mainly expressed in renal tissues and it correlated with the tumor stage, T, M, N classifications, OS, PFS, and DSS of pRCC patients. Consistent with the expression in pRCC tissue samples, RP11-63A11.1 was also down-regulated in pRCC cells. More importantly, up-regulation of RP11-63A11.1 attenuated cell survival and induced apoptosis. Conclusions Ten cancer-related lncRNAs were incorporated into a powerful model for prognosis evaluation. RP11-63A11.1 functioned as a cancer suppressor in pRCC and it might be a potential therapeutic target for treating pRCC.

Download Full-text

A Novel Prognostic Cancer-related lncRNA Signature in Papillary Renal Cell Carcinoma

10.21203/rs.3.rs-576394/v1 ◽

2021 ◽

Author(s):

Binghai Chen ◽

Di Dong ◽

Qin Yao ◽

Yuanzhang Zou ◽

Wei Hu

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

High Efficiency ◽

Risk Model ◽

Cox Regression ◽

Clinical Feature ◽

Roc Curve Analysis ◽

Significant Difference

Abstract BackgroundPapillary renal cell carcinoma (pRCC) ranks second in renal cell carcinoma and the prognosis of pRCC remains poor. Here, we aimed to screen and identify a novel prognostic cancer-related lncRNA signature in pRCC. MethodsThe RNA-seq profile and clinical feature of pRCC cases were downloaded from TCGA database. Significant cancer-related lncRNAs were obtained from the Immlnc database. Differentially expressed cancer-related lncRNAs (DECRLs) in pRCC were screened for further analysis. Cox regression report was implemented to identify prognostic cancer-related lncRNAs and establish a prognostic risk model, and ROC curve analysis was used to evaluate its precision. The correlation between RP11-63A11.1 and clinical characteristics was further analyzed. Finally, the expression level and role of RP11-63A11.1 were studied in vitro. ResultsA total of 367 DECRLs were finally screened and 26 prognostic cancer-related lncRNAs were identified. Among them, ten lncRNAs (RP11-573D15.8, LINC01317, RNF144A-AS1, TFAP2A-AS1, LINC00702, GAS6-AS1, RP11-400K9.4, LUCAT1, RP11-63A11.1, and RP11-156L14.1) were independently connected with prognosis of pRCC. These ten lncRNAs were incorporated into a prognostic risk model. In accordance with the median value of the riskscore, pRCC cases were separated into high and low risk groups. Survival analysis indicated that there was a significant difference on overall survival (OS) rate between the two groups. The area under curve (AUC) in different years indicated that the model was of high efficiency in prognosis prediction. RP11-63A11.1 was mainly expressed in renal tissues and it correlated with the tumor stage, T, M, N classifications, OS, PFS, and DSS of pRCC patients. Consistent with the expression in pRCC tissue samples, RP11-63A11.1 was also downregulated in pRCC cells. More importantly, upregulation of RP11-63A11.1 attenuated cell survival and induced apoptosis. ConclusionsTen cancer-related lncRNAs were incorporated into a powerful model for prognosis evaluation. RP11-63A11.1 functioned as a cancer suppressor in pRCC and it might be a potential therapeutic target for treating pRCC.

Download Full-text

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

10.21203/rs.3.rs-44822/v1 ◽

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.

Download Full-text

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Bioscience Reports ◽

10.1042/bsr20211451 ◽

2021 ◽

Vol 41 (8) ◽

Author(s):

Wei Ma ◽

Manli Zhong ◽

Xiaowu Liu

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Roc Curve ◽

Renal Cell ◽

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Clear Cell ◽

Cell Renal Cell Carcinoma ◽

Kaplan Meier

Abstract Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC). Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model. Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways. Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.

Download Full-text

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

European Urology Supplements ◽

10.1016/s1569-9056(19)34690-1 ◽

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

Download Full-text

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

10.21203/rs.3.rs-44822/v2 ◽

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.

Download Full-text

Contemporary incidence and epidemiologic trends of brain metastases at renal cell carcinoma diagnosis.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.6_suppl.529 ◽

2017 ◽

Vol 35 (6_suppl) ◽

pp. 529-529

Author(s):

Maxine Sun ◽

Guillermo de Velasco ◽

Priscilla Kaliopi Brastianos ◽

Raphael Brandao Moreira ◽

Paul L. Nguyen ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Variable Selection ◽

Brain Metastases ◽

Cell Carcinoma ◽

Renal Cell ◽

Risk Model ◽

Predictive Accuracy ◽

External Validation ◽

Incidence Trends ◽

Oncological Outcomes

529 Background: The incidence of brain metastases (BM) in patients with renal cell carcinoma (RCC) is hypothesized to have increased in the last two decades.Our objective was to provide an overview of recent incidence trends in patients with primary renal cell carcinoma (RCC) and BM at diagnosis using a nationally representative cancer cohort originating from the US. Secondary objectives include developing a tool for prediction of BM at diagnosis and to assess their oncological outcomes, and externally validating it in second database. Methods: All patients with a primary diagnostic confirmation of RCC within the Surveillance, Epidemiology, and End Results (SEER, 2010–2013) database and the National Cancer Database (NCDB, 2010–2012) were abstracted. The incidence proportions (% IP) and 95% confidence intervals (CI) of BM were calculated. A 1000-bootstrap corrected multivariable logistic regression models was developed for prediction of BM at diagnosis using the SEER cohort. Backward variable selection was conducted to identify the most parsimonious model. Model performance was evaluated via measures of predictive accuracy in the NCDB cohort. Results: The overall % IP of BM at RCC diagnosis was 1.51% (95% CI: 1.39–1.64) in the SEER and 1.37% (95% CI: 1.29–1.45%) in the NCDB. The odds of harbouring BM at RCC diagnosis varied significantly according to sociodemographic and clinical characteristics. Following backward variable selection within the SEER database, only histology, tumor size, and cN stages were retained in the final model. Predictive accuracy was adequate in the external validation cohort (C-statistic: 0.778). Median time to any death was 6.37 months in patients with BM. After adjusting for confounders, patients with BM were more likely to succumb to any death than those without BM at diagnosis (hazard ratio: 1.87, 95% CI: 1.71–2.05, p<0.001). Conclusions: The incidence of BM in patients with RCC is increasing. The oncological outcomes of such patients remain poor and their treatment management variable. A clinical risk model can predict the presence of BM at diagnosis and may justify baseline imaging in asymptomatic but high-risk patients.

Download Full-text