A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma.
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ABSTRACT:
SUBMITTER: Wei JH
PROVIDER: S-EPMC4846314 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma.
Wei Jin-Huan JH Haddad Ahmed A Wu Kai-Jie KJ Zhao Hong-Wei HW Kapur Payal P Zhang Zhi-Ling ZL Zhao Liang-Yun LY Chen Zhen-Hua ZH Zhou Yun-Yun YY Zhou Jian-Cheng JC Wang Bin B Yu Yan-Hong YH Cai Mu-Yan MY Xie Dan D Liao Bing B Li Cai-Xia CX Li Pei-Xing PX Wang Zong-Ren ZR Zhou Fang-Jian FJ Shi Lei L Liu Qing-Zuo QZ Gao Zhen-Li ZL He Da-Lin DL Chen Wei W Hsieh Jer-Tsong JT Li Quan-Zhen QZ Margulis Vitaly V Luo Jun-Hang JH
Nature communications 20151030
Clear cell renal cell carcinomas (ccRCCs) display divergent clinical behaviours. Molecular markers might improve risk stratification of ccRCC. Here we use, based on genome-wide CpG methylation profiling, a LASSO model to develop a five-CpG-based assay for ccRCC prognosis that can be used with formalin-fixed paraffin-embedded specimens. The five-CpG-based classifier was validated in three independent sets from China, United States and the Cancer Genome Atlas data set. The classifier predicts the ...[more]
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Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer and is associated with poor prognosis if metastasized. Up to one third of patients with local disease at diagnosis will develop metastasis after nephrectomy, and there is a need for new molecular markers to identify patients with high risk of tumor progression. In the present study, we performed genome-wide promoter DNA methylation analysis at diagnosis to identify DNA methylation profiles associated with risk for progress.MethodDiagnostic tissue samples from 115 ccRCC patients were analysed by Illumina HumanMethylation450K arrays and methylation status of 155,931 promoter associated CpGs were related to genetic aberrations, gene expression and clinicopathological parameters.ResultsThe ccRCC samples separated into two clusters (cluster A/B) based on genome-wide promoter methylation status. The samples in these clusters differed in tumor diameter (p < 0.001), TNM stage (p < 0.001), morphological grade (p < 0.001), and patients outcome (5 year cancer specific survival (pCSS5yr) p < 0.001 and cumulative incidence of progress (pCIP5yr) p < 0.001. An integrated genomic and epigenomic analysis in the ccRCCs, revealed significant correlations between the total number of genetic aberrations and total number of hypermethylated CpGs (R = 0.435, p < 0.001), and predicted mitotic age (R = 0.407, p < 0.001). We identified a promoter methylation classifier (PMC) panel consisting of 172 differently methylated CpGs accompanying progress of disease. Classifying non-metastatic patients using the PMC panel showed that PMC high tumors had a worse prognosis compared with the PMC low tumors (pCIP5yr 38% vs. 8%, p = 0.001), which was confirmed in non-metastatic ccRCCs in the publically available TCGA-KIRC dataset (pCIP5yr 39% vs. 16%, p < 0.001).ConclusionDNA methylation analysis at diagnosis in ccRCC has the potential to improve outcome-prediction in non-metastatic patients at diagnosis.
| S-EPMC6332661 | biostudies-literature
Project description:For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. Multivariable logistic regression was used to combine of the CNNs prediction and clinicopathological parameters. A mean balanced accuracy of 72.0% (standard deviation [SD] = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval [CI]: 62.9-68.1%), 86.2% (95%-CI: 81.8-90.5%), 44.9% (95%-CI: 40.2-49.6%), and 0.70 (95%-CI: 0.69-0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70-8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60-5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92-4.94, p = 0.08) on external validation. The results demonstrate that the CNN's image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC.
| S-EPMC9385058 | biostudies-literature
Project description:ObjectivesThis study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).MethodsThe study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan-Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.ResultsThe PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).ConclusionThe CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.Critical relevance statementThe intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.Key pointsThe multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis. The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading. The nomogram and radiomics models have a broad range of clinical applications.
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Project description:ObjectivesTo investigate the role of DNA methylation regulators in the prognosis of clear cell renal cell carcinoma (ccRCC) and to construct a DNA methylation regulator-based signature for predicting patient outcome.MethodsData from the TCGA dataset were downloaded and analyzed to identify differentially expressed DNA methylation regulators and their interaction as well as correlation. Consensus clustering was used to establish groups of ccRCC with distinct clinical outcomes. A prognostic signature based on two sets of DNA methylation regulators was established and validated in an independent cohort.ResultsOur analysis revealed that the expression levels of DNMT3B, MBD1, SMUG1, DNMT1, DNMT3A, TDG, TET3, MBD2, UHRF2, MBD3, UHRF1, and TET2 were significantly upregulated in ccRCC samples, while UNG, ZBTB4, TET1, ZBTB38, and MECP2 were markedly downregulated. UHRF1 was identified as a hub gene in the DNA methylation regulator interaction network. Significant differences were found regarding overall survival, gender, tumor status, and grade between ccRCC patients in the two risk groups. The prognostic signature, based on two sets of DNA methylation regulators, was an independent prognostic indicator, and these findings were validated in an external, independent cohort.ConclusionsThe study provides evidence that DNA methylation regulators play a significant role in the prognosis of ccRCC and the developed DNA methylation regulator-based signature could effectively predict patient outcome.
| S-EPMC10182479 | biostudies-literature
Project description:Clear-cell renal cell carcinoma (ccRCC) is the major renal cell carcinoma subtype, but its postsurgical prognosis varies among individual patients.We used gene expression, machine learning (random forest variable hunting), and Cox regression analysis to develop a risk score model based on 15 genes to predict survival of patients with ccRCC in the The Cancer Genome Atlas dataset (N?=?533). We validated this model in another cohort, and analyzed correlations between risk score and other clinical indicators.Patients in the high-risk group had significantly worse overall survival (OS) than did those in the low-risk group (P?=?5.6e-16); recurrence-free survival showed a similar pattern. This result was reproducible in another dataset, E-MTAB-1980 (N?=?101, P?=?.00029). We evaluated correlations between risk score and other clinical indicators. Risk was independent of age and sex, but was significantly associated with hemoglobin level, primary tumor size, and grade. Radiation therapy also had no effect on the prognostic value of the risk score. Cox multivariate regression showed risk score to be an important indicator for ccRCC prognosis. We plotted a nomogram for 3-year OS to facilitate use of risk score and other indicators.The risk score model based on expression of the 15 selected genes can predict survival of patients with ccRCC.
| S-EPMC6113007 | biostudies-other
Project description:BackgroundLittle is known about epigenetic silencing of genes by promoter hypermethylation in renal cell carcinoma (RCC). The aim of this study was to identify prognostic methylation markers in surgically treated clear cell RCC (ccRCC).MethodsMethylation patterns were assayed using the Infinium HumanMethylation450 BeadChip array on pairs of ccRCC and normal tissue from 12 patients. Using quantitative PSQ analysis, tumor-specific hypermethylated genes were validated in 25 independent cohorts and their clinical relevance was also verified in 152 independent cohorts.ResultsUsing genome-wide methylation array, Zinc finger protein 278 (ZNF278), Family with sequence similarity 155 member A (FAM155A) and Dipeptidyl peptidase 6 (DPP6) were selected for tumor-specific hypermethylated genes in primary ccRCC. The promoter methylation of these genes occurred more frequently in ccRCC than normal kidney in independent validation cohort. The hypermethylation of three genes were associated with advanced tumor stage and high grade tumor in ccRCC. During median follow-up of 39.2 (interquartile range, 15.4-79.1) months, 22 (14.5%) patients experienced distant metastasis. Multivariate analysis identified the methylation status of these three genes, either alone, or in a combined risk score as an independent predictor of distant metastasis.ConclusionThe promoter methylation of ZNF278, FAM155A and DPP6 genes are associated with aggressive tumor phenotype and early development of distant metastasis in patients with surgically treated ccRCC. These potential methylation markers, either alone, or in combination, could provide novel targets for development of individualized therapeutic and prevention regimens.
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Project description:BackgroundIn this study, we aimed to screen methylation signatures associated with the prognosis of patients with clear cell renal cell carcinoma (ccRCC).MethodsGene expression and methylation profiles of ccRCC patients were downloaded from publicly available databases, and differentially expressed genes (DEGs)-differentially methylated genes (DMGs) were obtained. Subsequently, gene set enrichment and transcription factor (TF) regulatory network analyses were performed. In addition, a prognostic model was constructed and the relationship between disease progression and immunity was analyzed.ResultsA total of 23 common DEGs-DMGs were analyzed, among which 14 DEGs-DMGs were obtained with a cutoff value of PCC < 0 and p < 0.05. The enrichment analysis showed that the 14 DEGs-DMGs were enriched in three GO terms and three KEGG pathways. In addition, a total of six TFs were shown to be associated with the 14 DEGs-DMGs, including RP58, SOX9, NF-κB65, ATF6, OCT, and IK2. A prognostic model using five optimized DEGs-DMGs which efficiently predicted survival was constructed and validated using the GSE105288 dataset. Additionally, four types of immune cells (NK cells, macrophages, neutrophils, and cancer-associated fibroblasts), as well as ESTIMATE, immune, and stromal scores were found to be significantly correlated with ccRCC progression (normal, primary, and metastasis) in addition to the five optimized DEGs-DMGs.ConclusionA five-gene methylation signature with the predictive ability for ccRCC prognosis was investigated in this study, consisting of CCNB2, CDKN1C, CTSH, E2F2, and ERMP1. In addition, potential targets for methylation-mediated immunotherapy were highlighted.
| S-EPMC8649352 | biostudies-literature
Project description:BackgroundThe role of glycolysis in tumorigenesis has received increasing attention and multiple glycolysis-related genes (GRGs) have been proven to be associated with tumor metastasis. Hence, we aimed to construct a prognostic signature based on GRGs for clear cell renal cell carcinoma (ccRCC) and to explore its relationships with immune infiltration.MethodsClinical information and RNA-sequencing data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) and ArrayExpress datasets. Key GRGs were finally selected through univariate COX, LASSO and multivariate COX regression analyses. External and internal verifications were further carried out to verify our established signature.ResultsFinally, 10 GRGs including ANKZF1, CD44, CHST6, HS6ST2, IDUA, KIF20A, NDST3, PLOD2, VCAN, FBP1 were selected out and utilized to establish a novel signature. Compared with the low-risk group, ccRCC patients in high-risk groups showed a lower overall survival (OS) rate (P = 5.548Ee-13) and its AUCs based on our established signature were all above 0.70. Univariate/multivariate Cox regression analyses further proved that this signature could serve as an independent prognostic factor (all P < 0.05). Moreover, prognostic nomograms were also created to find out the associations between the established signature, clinical factors and OS for ccRCC in both the TCGA and ArrayExpress cohorts. All results remained consistent after external and internal verification. Besides, nine out of 21 tumor-infiltrating immune cells (TIICs) were highly related to high- and low- risk ccRCC patients stratified by our established signature.ConclusionsA novel signature based on 10 prognostic GRGs was successfully established and verified externally and internally for predicting OS of ccRCC, helping clinicians better and more intuitively predict patients' survival.
| S-EPMC8034085 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (SAA1, FUT6, SPATA18, SHROOM3, AJAP1, and NPEPL1) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.
| S-EPMC9623106 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent renal malignant cancer, whose survival rate and quality of life of patients are still not satisfactory. Nevertheless, the TNM staging system currently used in clinical cannot make accurate survival predictions and precise treatment decisions for ccRCC patients. Therefore, there is an urgent need for more reliable biomarkers to identify high-risk subgroups of ccRCC patients to guide timely intervention and treatment. Recently, MiRNAs have been shown to be closely related to the procession of a variety of tumors, and they have high stability in various tissues, which makes them suggested to have the potential as a prognostic biomarker of ccRCC. In this study, by analyzing and processing the miRNAs expression profile of ccRCC patients from the TCGA database, we finally constructed an excellent miRNAs signature and verified it through a variety of methods. In order to build a more accurate and reliable clinical predictive model, we integrated the miRNAs signature with other prognostic-related clinical parameters to construct a nomogram. Functional enrichment analysis showed that miRNAs in the signature may regulate the genes involved in the Hippo signaling pathway, Tight junction, and Wnt signaling pathway to cause different prognoses of ccRCC patients, which may provide a reference for subsequent basic research and targeted therapy. To conclude, our study constructed a useful miRNAs signature, which allows the prognosis stratification for ccRCC patients and thereby guides the timely and effective interventions on high-risk patients. At the same time, this study also found the potential biological pathways involved in the procession of ccRCC.
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