scholarly journals A Risk Score Model Based on Nine Differentially Methylated mRNAs for Predicting Prognosis of Patients with Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jingmin Zhou ◽  
Guanghua Liu ◽  
Xingcheng Wu ◽  
Zhien Zhou ◽  
Jialin Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Clear Cell ◽  
Good Prognosis ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Mrna Methylation ◽  
Score Model ◽  
Validation Set

Purpose. DNA methylation alterations play important roles in initiation and progression of clear cell renal cell carcinoma (ccRCC). In this study, we attempted to identify differentially methylated mRNA signatures with prognostic value for ccRCC. Methods. The mRNA methylation and expression profiling data of 306 ccRCC tumors were downloaded from The Cancer Genome Atlas (TCGA) to screen differentially methylated lncRNAs and mRNAs (DMLs and DMMs) between bad and good prognosis patients. Uni- and multivariable Cox regression analyses and LASSO Cox-PH regression analysis were used to select prognostic lncRNAs and mRNAs. Corresponding risk scores were calculated and compared for predictive performance in the training set using Kaplan-Meier OS and ROC curve analyses. The optimal risk score was then identified and validated in the validation set. Function enrichment analysis was conducted. Results. This study screened 461 DMMs and 63 DMLs between good prognosis and bad prognosis patients, and furthermore, nine mRNAs and six lncRNAs were identified as potential prognostic molecules. Compared to nine-mRNA status risk score model, six-lncRNA methylation risk score model, and six-lncRNA status risk score model, the nine-mRNA methylation risk score model showed superiority for prognosis stratification of ccRCC patients in the training set. The prognostic ability of the nine-mRNA methylation risk score model was validated in the validation set. The nine prognostic mRNAs were functionally associated with neuroactive ligand receptor interaction and inflammation-related pathways. Conclusion. The nine-mRNA methylation signature (DMRTA2, DRGX, FAM167A, FGGY, FOXI2, KRTAP2-1, TCTEX1D1, TTBK1, and UBE2QL1) may be a useful prognostic biomarker and tool for ccRCC patients. The present results would be helpful to elucidate the possible pathogenesis of ccRCC.

scholarly journals Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Claudia-Gabriela Moldovanu ◽  
Bianca Boca ◽  
Andrei Lebovici ◽  
Attila Tamas-Szora ◽  
Diana Sorina Feier ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Predictive Ability ◽  
Nuclear Grade ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Validation Set ◽  
Sensitivity Specificity

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92–1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

scholarly journals Construction and Validation of a Prognostic Nomogram for Clear Cell Renal Cell Carcinoma Based on DNA Methylation-driven Differentially Expressed Genes

2020 ◽  
Author(s):  
Zheng Wang ◽  
Yanlong Zhang ◽  
Shuaishuai Fan ◽  
Yuan Ji ◽  
Jianchao Ren ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma ◽  
Score Model

Abstract Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent type of kidney cancer. This study aimed to establish a nomogram to predict ccRCC prognosis.Methods: By integrating DNA methylation (DNAm) data and gene expression profiles of ccRCC obtained from The Cancer Genome Atlas (TCGA), DNAm-driven genes were identified by differential and correlation analyses. Next, risk genes were selected by multiple algorithms (univariate Cox and Kaplan-Meier survival analyses) and various databases (TCGA, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and The Human Protein Atlas (HPA)). A risk score model was established by multivariate Cox analyses. ConsensusPathDB and Gene Set Enrichment Analysis (GSEA) were used to identify the biological functions of the selected genes. After comprehensively evaluating the clinical data, we established and assessed a dynamic nomogram available on a webserver.Results: In total, 220 differentially expressed DNAm-driven genes were identified, and five-gene signature (EPB41L4B, HHLA2, IFI16, CMTM3, and XAF1) was related to overall survival (OS). Next, we integrated the DNAm-driven genes into the prognostic risk score model and found that age, histologic grade, pathological stage, and risk level were correlated with OS in ccRCC patients. Based on these variables, a dynamic nomogram was established to predict the ccRCC prognosis. Finally, Functional enrichment analysis showed that the functions of these genes were relevant to immune reactions.Conclusions: We identified a 5 DNAm-driven gene signature whose altered status was highly correlated with ccRCC patient OS. We constructed a dynamic nomogram to provide individualized survival predictions for ccRCC patients.

scholarly journals Profiles of overall survival-related gene expression-based risk signature and their prognostic implications in clear cell renal cell carcinoma

2020 ◽  
Vol 40 (9) ◽  
Author(s):  
Zihao He ◽  
Tuo Deng ◽  
Xiaolu Duan ◽  
Guohua Zeng
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Family Member ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Training Set ◽  
Cell Renal Cell Carcinoma ◽  
Validation Set

Abstract The present work aimed to evaluate the prognostic value of overall survival (OS)-related genes in clear cell renal cell carcinoma (ccRCC) and to develop a nomogram for clinical use. Transcriptome data from The Cancer Genome Atlas (TCGA) were collected to screen differentially expressed genes (DEGs) between ccRCC patients with OS &gt; 5 years (149 patients) and those with &lt;1 year (52 patients). In TCGA training set (265 patients), seven DEGs (cytochrome P450 family 3 subfamily A member 7 (CYP3A7), contactin-associated protein family member 5 (CNTNAP5), adenylate cyclase 2 (ADCY2), TOX high mobility group box family member 3 (TOX3), plasminogen (PLG), enamelin (ENAM), and collagen type VII α 1 chain (COL7A1)) were further selected to build a prognostic risk signature by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Survival analysis confirmed that the OS in the high-risk group was dramatically shorter than their low-risk counterparts. Next, univariate and multivariate Cox regression revealed the seven genes-based risk score, age, and Tumor, lymph Node, and Metastasis staging system (TNM) stage were independent prognostic factors to OS, based on which a novel nomogram was constructed and validated in both TCGA validation set (265 patients) and the International Cancer Genome Consortium cohort (ICGC, 84 patients). A decent predictive performance of the nomogram was observed, the C-indices and corresponding 95% confidence intervals of TCGA training set, validation set, and ICGC cohort were 0.78 (0.74–0.82), 0.75 (0.70–0.80), and 0.70 (0.60–0.80), respectively. Moreover, the calibration plots of 3- and 5 years survival probability indicated favorable curve-fitting performance in the above three groups. In conclusion, the proposed seven genes signature-based nomogram is a promising and robust tool for predicting the OS of ccRCC, which may help tailor individualized therapeutic strategies.

scholarly journals Ferroptosis-Related Gene-Based Prognostic Model and Immune Infiltration in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guo-Jiang Zhao ◽  
Zonglong Wu ◽  
Liyuan Ge ◽  
Feilong Yang ◽  
Kai Hong ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Clear Cell ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cancer Genome Atlas ◽  
Score Model ◽  
Genome Atlas

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. CARS1, CD44, FANCD2, HMGCR, NCOA4, SLC7A11, and ACACA were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan–Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank P = 7.815 × 10–11). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies.

scholarly journals Overexpression of FZD1 is Associated with a Good Prognosis and Resistance of Sunitinib in Clear Cell Renal Cell Carcinoma

2019 ◽  
Vol 10 (5) ◽  
pp. 1237-1251 ◽  
Author(s):  
Qiang Peng ◽  
Lu Wang ◽  
Danfeng Zhao ◽  
Yulin Lv ◽  
Hongzhi Wang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Good Prognosis ◽  
Cell Renal Cell Carcinoma

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 A Novel Immune-Related lncRNA-Based Model for Survival Prediction in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-37
Author(s):  
Zedan Zhang ◽  
Yanlin Tang ◽  
Yanjun Liu ◽  
Hongkai Zhuang ◽  
Enyu Lin ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
T Cells ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Functional Enrichment ◽  
Clinical Parameters ◽  
Training Set ◽  
Cell Renal Cell Carcinoma

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction. Methods. The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature. Results. Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8+ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature. Conclusion. Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

scholarly journals Prognostic Value of The Ratio of Maximum To Minimum Diameter of Primary Tumor In Metastatic Clear Cell Renal Cell Carcinoma

2022 ◽  
Author(s):  
Hongzhe Shi ◽  
Chuanzhen Cao ◽  
Li Wen ◽  
Lianyu Zhang ◽  
Jin Zhang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Prognostic Factor ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Primary Tumor ◽  
Renal Cell ◽  
Prognostic Value ◽  
Tumor Necrosis ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

Abstract Background: Several models and markers were developed and found to predict outcome of advanced renal cell carcinoma. This study aimed to evaluate the prognostic value of the ratio of maximum to minimum tumor diameter (ROD) in metastatic clear cell renal cell carcinoma (mccRCC).Methods: Patients with mccRCC (n=213) treated with sunitinib from January 2008 to December 2018 were identified. Cut-off value for ROD was determined using receiver operating characteristic. Patients with different ROD scores were grouped and evaluated. Survival outcomes were estimated by Kaplan-Meier method.Results: The optimal ROD cutoff value of 1.34 was determined for progression free survival (PFS) and overall survival (OS). Patients in ROD≥1.34 group had shorter PFS (9.6 versus 17.7 months, p<0.001) and OS (25.5 versus 32.6 months, p<0.001) than patients in ROD<1.34 group. After adjustment for other factors, multivariate analysis showed ROD≥1.34 was an independent prognostic factor for PFS (p<0.001) and OS (p=0.006). Patients in ROD³1.34 group presented higher proportions of T3/4 stage (92.9% versus 7.1%, p=0.012), WHO/ISUP grade III/IV (72.0% versus 28.0%, p=0.010), tumor necrosis (71.0% versus 29.0%, p=0.039), sarcomatoid differentiation (79.1% versus 20.9%, p=0.007), poor MSKCC risk score (78.4% versus 21.6%, p<0.001) and poor IMDC risk score (74.4% versus 25.6%, p<0.001) than ROD<1.34 group.Conclusion: Primary tumor with higher ROD was an independently prognostic factor for both PFS and OS in patients with mccRCC who received targeted therapy. Higher ROD was also associated with high T stage, high WHO/ISUP grade, sarcomatoid features, tumor necrosis, poor MSKCC and IMDC risk score.

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