A 13-gene risk score system and a nomogram survival model for predicting the prognosis of clear 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.

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A new survival model based on ferroptosis-related genes for prognostic prediction in clear cell renal cell carcinoma

Aging ◽

10.18632/aging.103553 ◽

2020 ◽

Vol 12 (14) ◽

pp. 14933-14948

Author(s):

Guangzhen Wu ◽

Qifei Wang ◽

Yingkun Xu ◽

Quanlin Li ◽

Liang Cheng

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Survival Model ◽

Cell Renal Cell Carcinoma ◽

Model Based ◽

Prognostic Prediction

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Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system

Aging ◽

10.18632/aging.202594 ◽

2021 ◽

Vol 13 (5) ◽

pp. 7361-7381

Author(s):

Qiliang Peng ◽

Yi Shen ◽

Kai Fu ◽

Zheng Dai ◽

Lu Jin ◽

...

Keyword(s):

Artificial Intelligence ◽

Renal Cell Carcinoma ◽

Overall Survival ◽

Prediction Model ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Prognostic Score ◽

Cell Renal Cell Carcinoma ◽

Score System

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

10.21203/rs.3.rs-1183282/v1 ◽

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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A New Survival Model Based on ADAMTSs for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

Journal of Oncology ◽

10.1155/2021/2606213 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Guangzhen Wu ◽

Jianyi Li ◽

Yingkun Xu ◽

Xiangyu Che ◽

Feng Chen ◽

...

Keyword(s):

Gene Expression ◽

Renal Cell Carcinoma ◽

Regression Analysis ◽

Cell Carcinoma ◽

Renal Cell ◽

Prognostic Model ◽

Clear Cell ◽

Survival Model ◽

Cell Renal Cell Carcinoma ◽

Cancer Types

The main purpose of this study was to explore the genetic variation, gene expression, and clinical significance of ADAMTSs (a disintegrin and metalloprotease domains with thrombospondin motifs) across cancer types. Analysis of data from the TCGA (The Cancer Genome Atlas) database showed that the ADAMTSs have extensive CNV (copy number variation) and SNV (single nucleotide variation) across cancer types. Compared with normal tissues, the methylation of ADAMTSs in cancer tissues is also significantly different, which affects the expression of ADAMTS gene and the prognosis of cancer patients. Through gene expression analysis, we found that ADAMTS family has significant changes in gene expression across cancer types and is closely related to the prognosis of carcinoma, especially in ccRCC (clear cell renal cell carcinoma). LASSO regression analysis was used to establish a prognostic model based on the ADAMTSs to judge the prognosis of patients with ccRCC. Multiple Cox regression analysis suggested that age, grade, stage, and risk score of the prognostic model of ccRCC were independent prognostic factors in patients with renal clear cell carcinoma. These findings indicate that the ADAMTSs-based survival model can accurately predict the prognosis of patients with ccRCC and suggest that ADAMTSs are a potential prognostic biomarker and therapeutic target in ccRCC.

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

10.21203/rs.3.rs-75475/v1 ◽

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.

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A New Survival Model Based on Cholesterol Biosynthesis-Related Genes for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

BioMed Research International ◽

10.1155/2021/9972968 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Xiaochen Qi ◽

Xin Lv ◽

Xiaoxi Wang ◽

Zihao Ruan ◽

Peizhi Zhang ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

Cholesterol Biosynthesis ◽

Kidney Tissue ◽

Survival Model ◽

Risk Scores ◽

Cell Renal Cell Carcinoma

In our study, the value of cholesterol biosynthesis is related to clinical analysis in 32 cancer forms in the GSEA database facility. We have a mutation between 25 CBRGs. In The Cancer Genome Atlas database, clear cell renal cell carcinoma (ccRCC, n = 539 ) was upregulated or downregulated in 22 out of 25 cases ( n = 72 ) compared with normal kidney tissue. Then, using LASSO regression analysis, the survival model that is based on nine risk-related CBRGs (CYP51A1, HMGCR, HMGCS1, IDI1, FDFT1, SQLE, ACAT2, FDPS, and NSDHL) is established. ROC curves confirmed the good omen of the new survival mode, and the area under the curve is 0.72 (5 years) and 0.709 (10 years). High SQLE and ACAT2 expression and low NSDHL, FDPS, CYP51A1, FDFT1, HMGCS1, HMGCR, and IDI1 expression were closely related to patients with high-risk renal clear cell carcinoma. Two types of Cox regression, uni- and multivariate, were used to determine risk scores, age, staging, and grade as independent risk factors for prognosis in patients with clear cell renal cell carcinoma. The results showed the prediction model established by 9 selected CBRGs could predict the prognosis more accurately.

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Identification of a three-m6A related gene risk score model as a potential prognostic biomarker in clear cell renal cell carcinoma

PeerJ ◽

10.7717/peerj.8827 ◽

2020 ◽

Vol 8 ◽

pp. e8827 ◽

Cited By ~ 5

Author(s):

Yiqiao Zhao ◽

Zijia Tao ◽

Xiaonan Chen

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

Experimental Studies ◽

The Cancer Genome Atlas ◽

Cell Renal Cell Carcinoma ◽

Cox Regression Analysis

Background Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent malignancies worldwide, N6-methyladenosine (m6A) has been shown to play important roles in regulating gene expression and phenotypes in both health and disease. Here, our purpose is to construct a m6A-regulrator-based risk score (RS) for prediction of the prognosis of ccRCC. Methods We used clinical and expression data of m6A related genes from The Cancer Genome Atlas (TCGA) dataset and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to develop an RS to predict survival of patients with ccRCC, and analyzed correlations between RS and other clinical indicators such as age, grade and stage. Validation of this RS was then engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset. Finally, we used quantitative real-time PCR to analyze the expression profile of genes consists of the RS. Results A three-gene RS including METTL3, METTL14 and HNRNPA2B1 which can predict overall survival (OS) of ccRCC patients from TCGA. After applying this RS into the validation cohort from Arrayexpress, we found that it successfully reproduced the result; furthermore, the results of PCR validation were in line with our analysis. Conclusion To sum up, our study has identified an RS composed of m6A related genes that may predict the prognosis of ccRCC patients, which might be helpful for future therapeutic strategies. Our results call for further experimental studies for validations.

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A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma

10.21203/rs.3.rs-153932/v1 ◽

2021 ◽

Author(s):

Yingkai Hong ◽

Mingen Lin ◽

Dehua Ou ◽

Zhuangkai Huang ◽

Peilin Shen

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

Risk Group ◽

Gene Signature ◽

Cell Renal Cell Carcinoma ◽

Prognostic Predictor

Abstract Background Clear cell renal cell carcinoma (ccRCC) is still highly aggressive and lethal even with various therapeutic approaches. As kidney is an iron-metabolism-related organ, exploring and assessing the clinical value of ferroptosis, an iron-dependent regulated cell death, is practical and significant. Methods Prognostic ferroptosis-related differentially expressed genes (DEGs) were identified from KIRC cohort in TCGA database, from which a prognostic signature was established using the Lasso-penalized Cox regression analysis. Each patient in the KIRC cohort and the E-MTAB-1980 cohort (from the ArrayExpress database) was assigned with a calculated signature-correlated risk score and categorized to be either in high- or low-risk group divided by the median risk score in the KIRC cohort. Then, the independent prognostic value of the signature was further assessed by Kaplan-Meier (K-M) survival, time-dependent receiver operating characteristic (ROC) and Cox regression analyses base on overall survival (OS) in both cohorts. Lastly, risk-related DEGs were identified in both cohorts and applied with the enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration. Results Within 60 ferroptosis-related genes, 32 prognostic DEGs were identified, from which we constructed a prognostic 12-gene signature including CARS1, HMGCR, CHAC1, GOT1, CD44, STEAP3, AKR1C1, CBS, DPP4, FANCD2, SLC1A5 and NCOA4. Patients in both cohorts were divided into high- and low-risk group which were visually distributed in two sets and with positive-risk-related mortality. The K-M survival and the ROC curves validated the signature as prognostic valuable with P <0.05 and area under the curve >0.7 in both cohorts, respectively. Multivariate Cox regression further confirmed the risk score as an independent prognostic predictor for OS. Commonly enriched term in GO and KEGG not only shown a highly iron correlation, but also, interesting, an immunity relevancy of 3 immune cells (macrophages, mast cells and regulatory T cell) and 1 immune-related function (antigen processing cell co-stimulation). Conclusion We established a novel 12 ferroptosis-related-gene signature which was proved as an independent prognostic predictor for OS and inferred as relating to tumor immunity in ccRCC, however, the underlying mechanism is still poorly characterized and needed further exploration.

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