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.

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.

scholarly journals Prognostic Risk Model for Advanced Renal Cell Carcinoma (RCC) with Immune-Related Genes

2020 ◽  
Author(s):  
Peng Cao ◽  
Jiandong Zhang ◽  
Zejia Sun ◽  
Xiang Zheng ◽  
Baozhong Yu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Immune Cells ◽  
Risk Model ◽  
Risk Groups ◽  
Cancer Genome ◽  
Low Risk ◽  
Landscape Analysis ◽  
Cox Regression Analysis ◽  
Significant Difference ◽  
Immune Related Genes

Abstract BackgroundRenal 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.MethodsIn this study, we downloaded genomic and clinical data of RCC samples from The Cancer Genome Atlas 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 the subgroups of the TCGA samples and external data from International Cancer Genome Consortium 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.ResultsWe 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.ConclusionsSum 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.

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.

scholarly journals Nomogram Predicting Survival to Assist Decision-Making of Metastasectomy in Patients With Metastatic Renal Cell Carcinoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Kan Wu ◽  
Zhihong Liu ◽  
Yanxiang Shao ◽  
Xiang Li
Keyword(s):  
Renal Cell Carcinoma ◽  
Decision Tree ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Risk Groups ◽  
Low Risk ◽  
Patient Stratification ◽  
Decision Tree Analysis ◽  
Tree Analysis

The survival benefit of metastasectomy (MSX) in patients with metastatic renal cell carcinoma (mRCC) remains unclear. A reliable model to predict an individuals’ risk of cancer-specific mortality (CSM) and to identify optimal candidates for MSX is needed. We identified 2,911 mRCC patients who underwent cytoreductive nephrectomy from the Surveillance, Epidemiology, and End Results database (2010–2015). Based on the Fine and Gray competing risks analyses, we created a nomogram to predict the survival of mRCC patients. Decision tree analysis was useful for patient stratification. The impact of MSX was assessed among three different subgroups. Overall, 579 (19.9%) cases underwent MSX. In the entire patients, the 1-, 2-, and 3-year cumulative incidence of CSM were 32.8, 47.2, and 57.9%, respectively. MSX was significantly associated with improved survival (hazard ratio [HR] = 0.875, 95% confidence interval [CI] 0.773–0.991; P = 0.015). Based on risk scores, patients were divided into three risk groups using decision tree analysis. In the low-risk group, MSX was significantly associated with a 12.8% risk reduction of 3-year CSM (HR = 0.689, 95% CI 0.507–0.938; P = 0.008), while MSX was not associated with survival in intermediate- and high-risk groups. We proposed a novel nomogram and patient stratification approach to identify suitable patients for MSX. The newly identified patient subgroup with a low-risk of CSM might benefit more from aggressive surgery. These results should be further validated and improved by the prospective trials.

scholarly journals Identification of Potential Biomarkers Associated With Immune Infiltration in Papillary Renal Cell Carcinoma

2021 ◽  
Author(s):  
Ran Deng ◽  
Jianpeng Li ◽  
Hong Zhao ◽  
Zhirui Zou ◽  
Jiangwei Man ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Cells ◽  
Renal Cell ◽  
Cox Regression ◽  
Papillary Renal Cell Carcinoma ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis

Abstract Background: Immunotherapeutic approaches have recently emerged as effective treatment regimens against various types of cancer. However, the immune-mediated mechanisms surrounding papillary renal cell carcinoma (pRCC) remain unclear. This study aimed to investigate the tumor microenvironment (TME) and identify the potential immune-related biomarkers for pRCC.Methods: The CIBERSORT algorithm was used to calculate the abundance ratio of immune cells in each pRCC sample downloaded from the database UCSC Xena. Univariate Cox analysis was used to select the prognostic-related tumor-infiltrating immune cells (TIICs). Multivariate Cox regression analysis was performed to develop a signature based on the selected prognostic-related TIICs. Then, these pRCC samples were divided into low- and high-risk groups according to the obtained signature. Analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to investigate the biological function of the DEGs (differentially expressed genes) between the high- and low-risk groups. The hub genes were identified using a Weighted Gene Co-Expression Network Analysis (WGCNA) and a Protein-Protein Interaction (PPI) analysis. The hub genes were subsequently validated using Kaplan-Meier survival analysis, Receiver Operating Characteristic (ROC) analysis, a nomogram prediction model, and via the Gene Expression Omnibus (GEO) database, and the Human Protein Atlas (HPA) database. Finally, we validated the correlation between the nine hub genes and immune cells using the XCELL algorithm.Results: According to our analyses, nine immune cells play a vital role in the TME of pRCC. Our analyses also obtained nine potential immune-related biomarkers for pRCC, including TOP2A, BUB1B, BUB1, TPX2, PBK, CEP55, ASPM, RRM2, and CENPF.Conclusion: In this study, our data revealed the crucial TIICs and potential immune-related biomarkers for pRCC and provided compelling insights into the pathogenesis and potential therapeutic targets for pRCC.

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

Hypoxia-related lncRNA Signature to Predict the Survival of Patients with Clear Cell Renal Cell Carcinoma

2022 ◽  
Author(s):  
Fu Liu ◽  
Xinyuan Li ◽  
Xiang Zhou ◽  
Hang Tong ◽  
Xin Gou
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Risk Group ◽  
Clear Cell Carcinoma ◽  
Risk Groups ◽  
Renal Clear Cell Carcinoma ◽  
Low Risk ◽  
Cell Renal Cell Carcinoma

Abstract Background: Renal cell carcinoma is the most common aggressive tumor of the genitourinary system. The main pathological subtype is clear cell renal cell carcinoma (ccRCC), and its treatment options are very limited. Therefore, identifying specific markers of renal clear cell carcinoma is of great significance for diagnosis and prognosis.Methods: From the TCGA database, we obtained information on 611 patients with renal clear cell carcinoma to analyze the relationship between hypoxia-related lncRNAs and overall survival. According to the coexpression of hypoxia genes and lncRNAs, genes related to hypoxia were identified. Difference analysis and Cox regression analysis were applied to assess survival-related risk factors. According to the median risk score of hypoxia-related genes, patients were divided into high-risk and low-risk groups. According to these gene characteristics and clinical parameters, a nomogram map was built, and GSEA was used for gene function annotation. RT-qRCR, Western Blot and Flow Cytometry were used to determine the role of SNHG19 in RCC cells.Results: By analyzing the coexpression of hypoxia genes and lncRNAs, 310 hypoxia-related genes were obtained. Six sHRlncRs were significantly correlated with the clinical outcomes of patients with ccRCC. Four sHRlncRs (AC011445.2, PTOV1-AS2, AP004609.3, and SNHG19) with the highest prognostic values were included in the group to construct the HRRS model. The high-risk group had a shorter OS than the low-risk group. HR-lncRNAs were considered to be an independent prognostic factor and associated with OS. The high- and low-risk groups showed different pathways in GSEA. Experiments showed that SNHG19 plays essential roles in autophagy and apoptosis of RCC cells.Conclusion: Our research shows that we established and verified a hypoxia-related lncRNA model that accurately correlates with ccRCC patients. This study also provides novel insights into hypoxia-based mechanisms and provides new biomarkers for the poor prognosis of ccRCC patients.

Prospective evaluation of carbonic anhydrase IX (CAIX) as a molecular marker in metastatic renal cell carcinoma

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5112-5112 ◽  
Author(s):  
T. Klatte ◽  
N. Zomorodian ◽  
F. F. Kabbinavar ◽  
A. S. Belldegrun ◽  
A. J. Pantuck
Keyword(s):  
Renal Cell Carcinoma ◽  
Molecular Marker ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Risk Groups ◽  
Low Risk ◽  
Risk Of Death ◽  
Caix Expression ◽  
Metastatic Rcc

5112 Background: CAIX is an important molecular marker of survival and response to immunotherapy in patients with metastatic renal cell carcinoma (RCC). The purpose of this study was to prospectively evaluate the performance of CAIX in patients with metastatic RCC. Methods: This study accrued 32 consecutive patients who were treated for metastatic clear cell RCC between January 2004 and May 2006. Immunohistochemical staining of the primary tumor was performed using the mouse monoclonal antibody MN-75. Patients were stratified into two groups: high CAIX expression (>85%) and low CAIX expression (=85%) according to the percentage of cells staining positive for CAIX. Study endpoints included disease-specific survival time (DSS) and response to treatment according to RECIST criteria. Results: Four (12.5%), 21 (65.6%), and 7 patients (21.9%) were classified into low risk, intermediate risk, and high risk groups according to the University of California Integrated Staging System (UISS). Twenty (62.5%) of the 32 patients had high CAIX expression. The median follow-up was 11.4 months. Patients with low CAIX expression had significantly worse prognosis than patients with high CAIX expression (median survival: 15.2 months vs. not reached, p=0.01, 1-year DSS rate: 63% vs. 83%) and a 3.9 fold increased risk of death from RCC (95% CI, 1.2–12.7). All 4 patients in the low risk UISS group had high CAIX expression, and all were alive at 1 year. Nine patients received high dose IL-2 based immunotherapy, including 8 who had high CAIX expression, and a 1-year DSS rate of 87.5%. Of the patients expressing high CAIX, 3 (37.5%) were responders to IL-2 including 1 partial and 2 complete responses, 3 (37.5%) exhibited stable disease and 2 (25.0%) progressed during treatment. Conclusions: The results of this first prospective study of CAIX in metastatic RCC confirm that high CAIX expression is associated with better survival and enhanced response to IL-2 based immunotherapy. Patients with high CAIX expression, specifically those with low risk RCC, should be considered candidates to receive immunotherapy, whereas patients with low CAIX expression or in higher risk groups may be better candidates for targeted or other experimental therapy. No significant financial relationships to disclose.

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