scholarly journals Comprehensive analysis of DNA methylation and gene expression reveals specific predictive biomarkers for renal cell carcinoma

2020 ◽  
Author(s):  
Yuan Zhang ◽  
Meng Xu ◽  
Jianmei Yin ◽  
Chunnv Li ◽  
Zhuang Ye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Clustering Analysis ◽  
Renal Cell ◽  
Cox Regression ◽  
Predictive Biomarkers ◽  
Unsupervised Clustering ◽  
Aberrant Methylation ◽  
Predictive Efficiency

Abstract Background We aimed to identify methylation-driven genes (MDGs) and predict the prognosis of clear cell renal cell carcinoma (ccRCC) based on several cohorts with high-throughput data.Methods The transcriptome profiles, 450K methylated data and corresponding clinical information were extracted from the cancer genome atlas (TCGA) database. MethylMix package was used to screen aberrant methylation events. Next, the Cox regression models were applied via survival package. Functional analysis was mainly performed based on ConsensusPathDB database. Besides, “mimifi” R package were adopted to analyze methylated alterations from three Gene Expression Omnibus (GEO) datasets. The predictive efficiency of constructed MDGs signature was assessed and validated in TCGA-KIRC and ICGC-RCC cohort, respectively. Unsupervised clustering analysis was conducted via ConsensusClusterPlus package. Moreover, MDGs-nomogram for OS prediction was conducted via glm and survival packages. Results Totally, we collected We finally identified 761 samples from TCGA-KIRC, ICGC-RCC, GSE61441, GSE105260 and GSE105261. We combined the expression data and 450K methylated data to find 5 hub prognostic MDGs (TAGLN2, PDK2, HHLA2, HOXA2, XAF1). We used the TCGA-KIRC cohort as the training dataset to verify the superior predictive significance of the 5 MDGs signature (AUC = 0.713), and validated it in ICGC-RCC cohort with AUC = 0.769. Kaplan-Meier analysis suggested that patients with high MDGs levels suffered from worse suvival outcomes. Besides, we further conducted the unsupervised clustering analysis in the whole ccRCC patients and identified the sub-cluster with the worst prognosis, indicating the MDGs could be used as efficient molecular classifier for ccRCC. We also identified 32 prognostic risk loci associated with hub MDGs in KIRC. Superior predictive efficiency was found in the MDGs-nomogram [Area Under Curve (AUC) of 3-year: 0.842, AUC of 5-year: 0.862], compared with traditional independent feature such as TNM stage (AUC of 3-year: 0.759, AUC of 5-year: 0.717). The 5 MDGs signature were mainly associated with oxygen metabolism, glycometabolism, even the HIF-1 signaling pathway. Conclusions Collectively, this study indicated several hub-MDGs and explored the prognostic value in ccRCC, which would provide new insights on the exploration of epigenetic pathogenesis and therapeutic targets for ccRCC.

scholarly journals Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Maolin Hu ◽  
Jiangling Xie ◽  
Huiming Hou ◽  
Ming Liu ◽  
Jianye Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Methylation Level ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

Background. Few previous studies have comprehensively explored the level of DNA methylation and gene expression in ccRCC. The purpose of this study was to identify the key clear cell renal cell carcinoma- (ccRCC-) related DNA methylation-driven genes (MDG) and to build a prognostic model based on the level of DNA methylation. Methods. RNA-seq transcriptome data and DNA methylation data were obtained from The Cancer Genome Atlas. Based on the MethylMix algorithm, we obtain ccRCC-related MDG. The univariate and multivariate Cox regression analyses were employed to investigate the correlation between patient overall survival and the methylation level of each MDG. Finally, a prognosis risk score was established based on a linear combination of the regression coefficient derived from the multivariate Cox regression model (β) multiplied with the methylation level of the gene. Results. 19 ccRCC-related MDG were identified. Three MDG (NCKAP1L, EVI2A, and BATF) were further screened and integrated into a prognostic risk score model, risk score=3.710∗methylation level of NCKAP1L+−3.892∗methylation level of EVI2A+−3.907∗methylation level of BATF. The risk model was independent from conventional clinical characteristics as a prognostic factor for ccRCC (HR=1.221, 95% confidence interval: 1.063–1.402, and P=0.005). The joint survival analysis showed that the gene expression and methylation levels of the prognostic genes EVI2A and BATF were significantly related with prognosis. Conclusion. This study provided an important bioinformatics foundation for in-depth studies of ccRCC DNA methylation.

scholarly journals Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

2020 ◽  
Author(s):  
Chengjian Ji ◽  
Yichun Wang ◽  
Liangyu Yao ◽  
Jiaochen Luan ◽  
Rong Cong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract Background Renal cell carcinoma (RCC) is one of the major malignant tumors of the urinary system, with a high mortality rate and a poor prognosis. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC. Although the diagnosis and treatment methods have been significantly improved, the incidence and mortality of ccRCC are high and still increasing. The occurrence and development of ccRCC are closely related to the changes of classic metabolic pathways. This article aims to explore the relationship between metabolic genes and the prognosis of patients with ccRCC. Patients and methods: Gene expression profiles of 63 normal kidney tissues and 446 ccRCC tissues from TCGA database and gene expression profiles of 39 ccRCC tissues from GEO database were used to obtain differentially expressed genes (DEGs) in ccRCC. Through the the KEGG gene sets of GSEA database, we obtained metabolic genes (MGs). Univariate Cox regression analysis was used to identify prognostic MGs. Lasso regression analysis was used to eliminate false positives because of over-fitting. Multivariate Cox regression analysis was used to established a prognostic model. Gene expression data and related survival data of 101 ccRCC patients from ArrayExpress database were used for external validation. Survival analysis, ROC curve analysis, independent prognostic analysis and clinical correlation analysis were performed to evaluate this model. Results We found that there were 479 abnormally expressed MGs in ccRCC tissues. Through univariate Cox regression analysis, Lasso regression analysis and multivariate Cox regression analysis, we identified 4 prognostic MGs (P4HA3, ETNK2, PAFAH2 and ALAD) and established a prognostic model (riskScore). Whether in the training cohort, the testing cohort or the entire cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 4 MGs was also confirmed in the ArrayExpress database. Results of GSEA analysis show that DEGs in patients with better prognosis were enriched in metabolic pathways. Then, a new Nomogram with higher prognostic value was constructed to better predict the 1-year OS, 3-year OS and 5-year OS of ccRCC patients. In addition, we successfully established a ceRNA network to further explain the differences in the expression of these MGs between high-risk patients and low-risk patients Conclusion We have successfully established a risk model (riskScore) based on 4 MGs, which could accurately predict the prognosis of patients with ccRCC. Our research may shed new light on ccRCC patients' prognosis and treatment management.

Screening and identification of novel promoter signatures in clear cell renal cell carcinoma metastasis.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 737-737
Author(s):  
Yuan-Yuan Qu ◽  
Xi Tian ◽  
Wenhao Xu ◽  
Aihemutaijiang Anwaier ◽  
Dingwei Ye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma

737 Background: Clear cell renal cell carcinoma (ccRCC) patient usually face aggressive progression when metastasis occurs. Therefore, in-depth investigation is needed to elucidate underlying mechanisms behind the metastasis of ccRCC to promote therapeutic benefits.This study aims to explore and investigate prognostic gene expression profiles based on multi-cohorts. Methods: Three microarray datasets were obtained from the Gene Expression Omnibus (GEO) database to screen and identify differentially expressed genes (DEGs) according to normalization annotation information. A total of 112 DEGs with functional enrichment were identified as candidate prognostic biomarkers. A protein–protein interaction network (PPI) of DEGs was developed, and the modules were analyzed using STRING and Cytoscape. Results: LASSO Cox regression suggested 31 significant involved genes, and 10 hub genes were identified as independent oncogenes in ccRCC patients. Distinct integrated scores of the hub genes mRNA expression showed statistical significance in predicting disease-free survival (DFS; p<0.001) and overall survival (OS; p<0.001) in TCGA and real-world cohorts. Meanwhile, ROC curves were constructed to validate specificity and sensitivity of the Cox regression penal to predict prognosis. The AUC index for the integrated genes scores was 0.758 for OS and 0.772 for DFS. Conclusions: In conclusion,the present study identifies DEGs and hub genes that may be involved in earlier recurrence and poor prognosis of ccRCC. The expression levels of ADAMTS9, C1S, DPYSL3, H2AFX, MINA, PLOD2, RUNX1, SLC19A1, TPX2 and TRIB3 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ccRCC.

scholarly journals COLGALT1 is a Prognostic Biomarker in Clear Renal Cell Carcinoma Correlated with Immune Infiltrates: A Study Based on TCGA Data

2020 ◽  
Author(s):  
Bangmin Han ◽  
Shiwei Liu ◽  
Qianwei Xing ◽  
Yang Yu ◽  
Yi Wang
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Prognostic Factor ◽  
Cell Carcinoma ◽  
Metabolic Pathways ◽  
Renal Cell ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Immune Microenvironment

Abstract Background: COLGALT1, as one gene enriched in metabolic pathways, which may be related to the tumorigenesis and progression. We aim to explore the potential value of COLGALT1 in clear cell renal cell carcinoma (ccRCC) through the study.Methods: We searched The Cancer Genome Atlas (TCGA) database to collecte ccRCC patients’ information including clinicopathologic parameters and COLGALT1 gene expression. We also validiated the COLGALT1 mRNA expression by qRT-PCR. Then, We evaluated the relationship between COLGALT1 and overall survival (OS) by the Cox regression analyses. Gene Set Enrichment Analysis (GSEA) was utilized to compare between tissues with different COLGALT1 expression levels. Microsatellite Instability (MSI), Tumor Mutational Burden (TMB) and Neoantigen were evaluated through different tools. By TIMER, correlations between COLGALT1 and immune cell infiltrations were analyzed. The ESTIMATE algorithm was used to calculate the estimate, stromal and immune scores for ccRCC. Finally, CIBERSORT was carried out to explore the connection between the COLGALT1 and the tumor immune microenvironment.Results: Significant gene expression of COLGALT1 was identified between normal and ccRCC tissues. Multivariate analysis indicated that high expression of COLGALT1 was linked to poor OS (P = 0e+00). GSEA results demonstrated that high COLGALT1 expression was associated with metabolic pathways. COLGALT1 was identified to be one independent prognostic factor through the univariate and multivariate Cox regression analyses. One nomogram was integrated including both the clinicopathologic variables and COLGALT1 expression to provide a quantitative approach to clinicians for predicting prognostic risk. Futher more, we find out some genes which are significantly correlated with COLGALT1. Besides, MSI and TMB showed strong correlations with COLGALT1 in ccRCC. Also, the correlations between COLGALT1 with immune infiltrations were found in ccRCC. Finally, immune microenvironment including immune checkpoint molecules, immune cells and mismatch repair protein were proved to be linked to COLGALT1 in ccRCC.Conclusions: Our results revealed that COLGALT1 could act as a favorable prognostic factor for ccRCC. Besides, this study also provided one method to determine the immune infiltration of patients and some signal pathways which are potential regulated by COLGALT1 in ccRCC.

Markers of sunitinib-resistance in clear cell renal cell carcinoma: A gene expression analysis.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 533-533
Author(s):  
Òscar Reig ◽  
Mercedes Marín-Aguilera ◽  
Juan José Lozano ◽  
Blanca Gonzalez ◽  
Carme Mallofré ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Expression Analysis ◽  
Renal Cell ◽  
Gene Expression Analysis ◽  
Predictive Biomarkers ◽  
Intrinsic Resistance ◽  
Primary Tumors ◽  
Novel Biomarkers

533 Background: Sunitinib is a standard first line treatment of metastatic renal cell carcinoma (ccRCC). Approximately 20% of patients experience primary resistance to therapy and no predictive biomarkers are available. The aim of our study is to discover novel biomarkers to predict response to sunitinib and to generate hypothesis about mechanisms of intrinsic resistance. Methods: Gene expression analysis was performed in formalin-fixed paraffin embedded (FFPE) samples from 44 patients with metastatic ccRCC treated with sunitinib in our institution. Affymetrix Human Gene 2.0 ST array was performed in primary tumors from 6 extremely sensitive (progression free survival (PFS) > 24 months) and 8 refractory (progression disease as best response) ccRCC. Technical validation with qPCR (Fluidigm Dynamic 96.96 Array) was performed in the whole cohort. The ΔΔCt method was used to quantify the relative amount of mRNA. Clinical and pathological data were correlated with gene expression. Results: 330 genes were differentially expressed between refractory and sensitive patients (p≤0.05). Network analysis showed 16 significant networks represented. Gene expression of 96 selected markers was tested in 47 primary tumors and 24 metastases. We found IL8, VEGF and NOTCH pathways upregulated on refractory patients. Survival analysis showed that the overexpression of IL8 correlated with a worse PFS (HR: 2.42, 95%CI1.27 – 4.63, p=0.0075) and overall survival (OS) (HR: 3.42, 95%CI 1.50 – 7.79, p=0.0034). Overexpression of VEGFBcorrelated with a prolonged PFS (HR 0.52, 95% CI 0.28 – 0.98, p=0.0415). Conclusions: Our results confirm the predictive value of IL8 expression in a cohort of ccRCC patients treated with sunitinib and suggests novel biomarkers of response to sunitinib. These data will be further validated in an independent cohort of patients.

scholarly journals Coffee consumption and risk of renal cell carcinoma in the NIH-AARP Diet and Health Study

2021 ◽  
Author(s):  
Jongeun Rhee ◽  
Erikka Loftfield ◽  
Neal D Freedman ◽  
Linda M Liao ◽  
Rashmi Sinha ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Coffee Consumption ◽  
Health Study ◽  
Coffee Intake ◽  
Decaffeinated Coffee ◽  
Incident Cases ◽  
Reduced Risk

Abstract Background Coffee consumption has been associated with a reduced risk of some cancers, but the evidence for renal cell carcinoma (RCC) is inconclusive. We investigated the relationship between coffee and RCC within a large cohort. Methods Coffee intake was assessed at baseline in the National Institutes of Health–American Association of Retired Persons Diet and Health Study. Among 420 118 participants eligible for analysis, 2674 incident cases were identified. We fitted Cox-regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for coffee consumption vs non-drinkers. Results We observed HRs of 0.94 (95% CI 0.81, 1.09), 0.94 (0.81, 1.09), 0.80 (0.70, 0.92) and 0.77 (0.66, 0.90) for usual coffee intake of &lt;1, 1, 2–3 and ≥4 cups/day, respectively (Ptrend = 0.00003). This relationship was observed among never-smokers (≥4 cups/day: HR 0.62, 95% CI 0.46, 0.83; Ptrend = 0.000003) but not ever-smokers (HR 0.85, 95% CI 0.70, 1.05; Ptrend = 0.35; Pinteraction = 0.0009) and remained in analyses restricted to cases diagnosed &gt;10 years after baseline (HR 0.65, 95% CI 0.51, 0.82; Ptrend = 0.0005). Associations were similar between subgroups who drank predominately caffeinated or decaffeinated coffee (Pinteraction = 0.74). Conclusion In this investigation of coffee and RCC, to our knowledge the largest to date, we observed a 20% reduced risk for intake of ≥2 cups/day vs not drinking. Our findings add RCC to the growing list of cancers for which coffee consumption may be protective.

scholarly journals Multidiscipline Immunotherapy-Based Rational Combinations for Robust and Durable Efficacy in Brain Metastases from Renal Cell Carcinoma

2021 ◽  
Vol 22 (12) ◽  
pp. 6290
Author(s):  
Hye-Won Lee
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Kinase Inhibitors ◽  
Checkpoint Inhibitors ◽  
Imaging Techniques ◽  
Predictive Biomarkers ◽  
Effective Control ◽  
Extrinsic Factors ◽  
Medical Needs

Advanced imaging techniques for diagnosis have increased awareness on the benefits of brain screening, facilitated effective control of extracranial disease, and prolonged life expectancy of metastatic renal cell carcinoma (mRCC) patients. Brain metastasis (BM) in patients with mRCC (RCC-BM) is associated with grave prognoses, a high degree of morbidity, dedicated assessment, and unresponsiveness to conventional systemic therapeutics. The therapeutic landscape of RCC-BM is rapidly changing; however, survival outcomes remain poor despite standard surgery and radiation, highlighting the unmet medical needs and the requisite for advancement in systemic therapies. Immune checkpoint inhibitors (ICIs) are one of the most promising strategies to treat RCC-BM. Understanding the role of brain-specific tumor immune microenvironment (TIME) is important for developing rationale-driven ICI-based combination strategies that circumvent tumor intrinsic and extrinsic factors and complex positive feedback loops associated with resistance to ICIs in RCC-BM via combination with ICIs involving other immunological pathways, anti-antiangiogenic multiple tyrosine kinase inhibitors, and radiotherapy; therefore, novel combination approaches are being developed for synergistic potential against RCC-BM; however, further prospective investigations with longer follow-up periods are required to improve the efficacy and safety of combination treatments and to elucidate dynamic predictive biomarkers depending on the interactions in the brain TIME.

scholarly journals Efficacy and Safety of Nivolumab and Ipilimumab for Advanced or Metastatic Renal Cell Carcinoma: A Multicenter Retrospective Cohort Study

2021 ◽  
Vol 28 (2) ◽  
pp. 1402-1411
Author(s):  
Koji Iinuma ◽  
Koji Kameyama ◽  
Kei Kawada ◽  
Shota Fujimoto ◽  
Kimiaki Takagi ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Objective Response ◽  
Predictive Biomarkers ◽  
Progression Free Survival ◽  
Predictive Marker ◽  
Efficacy And Safety ◽  
Lymphocyte Ratio

We conducted a multicenter, retrospective study to evaluate the efficacy and safety of combination nivolumab plus ipilimumab (NIVO+IPI) in 35 patients with advanced or metastatic renal cell carcinoma (mRCC). In this study, we focused on patients who received NIVO+IPI and were stratified into intermediate- or poor-risk disease according to the International Metastatic Renal Cell Carcinoma Database Consortium model at five institutions in Japan. The primary endpoint was overall survival (OS). Secondary endpoints were disease control rate (DCR), best overall response (BOR), objective response rate (ORR), and progression-free survival (PFS). In addition, we evaluated the role of inflammatory cell ratios, namely neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), as predictive biomarkers in patients with mRCC. The median follow-up period was 1 year, and the 1-year OS rate was 95.8%. The ORR and DCR were 34.3% and 80.0%, respectively. According to BOR, four patients (11.4%) achieved complete response. According to NLR stratification, the 1-year PFS rates were 82.6% and 23.7% when the NLR was ≤4.6 and >4.6, respectively (p = 0.04). Based on PLR stratification, the 1-year PFS rates were 81.7% and 34.3% when the PLR was ≤188.1 and >188.1, respectively (p = 0.033). Although 71.4% of the patients experienced treatment-related adverse events (TRAEs) with NIVO+IPI, only four patients discontinued NIVO+IPI due to grade 3/4 TRAEs. Patients treated with NIVO+IPI as a first-line therapy for advanced or mRCC achieved relatively better oncological outcomes. Therefore, NIVO+IPI may have potential advantages and may lead to a treatment effect compared to those receiving targeted therapies. In addition, PLR >188.1 may be a useful predictive marker for mRCC patients who received NIVO+IPI.

scholarly journals MET Expression in Primary and Metastatic Clear Cell Renal Cell Carcinoma: Implications of Correlative Biomarker Assessment to MET Pathway Inhibitors

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Brian Shuch ◽  
Ryan Falbo ◽  
Fabio Parisi ◽  
Adebowale Adeniran ◽  
Yuval Kluger ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Predictive Biomarkers ◽  
Cell Renal Cell Carcinoma ◽  
Primary Tumors ◽  
Distant Tissue ◽  
Significant Expression ◽  
Metastatic Sites

Aims. Inhibitors of the MET pathway hold promise in the treatment for metastatic kidney cancer. Assessment of predictive biomarkers may be necessary for appropriate patient selection. Understanding MET expression in metastases and the correlation to the primary site is important, as distant tissue is not always available.Methods and Results. MET immunofluorescence was performed using automated quantitative analysis and a tissue microarray containing matched nephrectomy and distant metastatic sites from 34 patients with clear cell renal cell carcinoma. Correlations between MET expressions in matched primary and metastatic sites and the extent of heterogeneity were calculated. The mean expression of MET was not significantly different between primary tumors when compared to metastases (P=0.1). MET expression weakly correlated between primary and matched metastatic sites (R=0.5) and a number of cases exhibited very high levels of discordance between these tumors. Heterogeneity within nephrectomy specimens compared to the paired metastatic tissues was not significantly different (P=0.39).Conclusions. We found that MET expression is not significantly different in primary tumors than metastatic sites and only weakly correlates between matched sites. Moderate concordance of MET expression and significant expression heterogeneity may be a barrier to the development of predictive biomarkers using MET targeting agents.

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