An effective seven-CpG-based signature to predict survival in renal clear cell carcinoma by integrating DNA methylation and gene expression

Aim: To develop a free and quick analysis online tool that allows users to easily investigate the prognostic potencies of interesting genes in kidney renal clear cell carcinoma (KIRC). Patients & methods: A total of 629 KIRC cases with gene expression profiling data and clinical follow-up information are collected from public Gene Expression Omnibus and The Cancer Genome Atlas databases. Results: One web application called Online consensus Survival analysis for KIRC (OSkirc) that can be used for exploring the prognostic implications of interesting genes in KIRC was constructed. By OSkirc, users could simply input the gene symbol to receive the Kaplan–Meier survival plot with hazard ratio and log-rank p-value. Conclusion: OSkirc is extremely valuable for basic and translational researchers to screen and validate the prognostic potencies of genes for KIRC, publicly accessible at http://bioinfo.henu.edu.cn/KIRC/KIRCList.jsp

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LRRC19—A Bridge between Selenium Adjuvant Therapy and Renal Clear Cell Carcinoma: A Study Based on Datamining

Genes ◽

10.3390/genes11040440 ◽

2020 ◽

Vol 11 (4) ◽

pp. 440

Author(s):

Yitong Zhang ◽

Jiaxing Wang ◽

Xiqing Liu

Keyword(s):

Gene Expression ◽

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Independent Risk Factor ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Renal Clear Cell Carcinoma ◽

Cell Renal Cell Carcinoma ◽

Low Expression

Kidney renal clear cell carcinoma (KIRC) is the most common and fatal subtype of renal cancer. Antagonistic associations between selenium and cancer have been reported in previous studies. Selenium compounds, as anti-cancer agents, have been reported and approved for clinical trials. The main active form of selenium in selenoproteins is selenocysteine (Sec). The process of Sec biosynthesis and incorporation into selenoproteins plays a significant role in biological processes, including anti-carcinogenesis. However, a comprehensive selenoprotein mRNA analysis in KIRC remains absent. In the present study, we examined all 25 selenoproteins and identified key selenoproteins, glutathione peroxidase 3 (GPX3) and type 1 iodothyronine deiodinase (DIO1), with the associated prognostic biomarker leucine-rich repeat containing 19 (LRRC19) in clear cell renal cell carcinoma cases from The Cancer Genome Atlas (TCGA) database. We performed validations for the key gene expression levels by two individual clear cell renal cell carcinoma cohorts, GSE781 and GSE6344, datasets from the Gene Expression Omnibus (GEO) database. Multivariate survival analysis demonstrated that low expression of LRRC19 was an independent risk factor for OS. Gene set enrichment analysis (GSEA) identified tyrosine metabolism, metabolic pathways, peroxisome, and fatty acid degradation as differentially enriched with the high LRRC19 expression in KIRC cases, which are involved in selenium therapy of clear cell renal cell carcinoma. In conclusion, low expression of LRRC19 was identified as an independent risk factor, which will advance our understanding concerning the selenium adjuvant therapy of clear cell renal cell carcinoma.

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A methylomics-correlated nomogram predicts the recurrence free survival risk of kidney renal clear cell carcinoma

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021424 ◽

2021 ◽

Vol 18 (6) ◽

pp. 8559-8576

Author(s):

Xiuxian Zhu ◽

◽

Xianxiong Ma ◽

Chuanqing Wu

Keyword(s):

Dna Methylation ◽

Cell Carcinoma ◽

Roc Analysis ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

External Validation ◽

Renal Clear Cell Carcinoma ◽

Recurrence Free Survival ◽

Free Survival ◽

Validation Set

<abstract> <sec><title>Background</title>Various studies have suggested that the DNA methylation signatures were promising to identify novel hallmarks for predicting prognosis of cancer. However, few studies have explored the capacity of DNA methylation for prognostic prediction in patients with kidney renal clear cell carcinoma (KIRC). It's very promising to develop a methylomics-related signature for predicting prognosis of KIRC. </sec> <sec><title>Methods</title>The 282 patients with complete DNA methylation data and corresponding clinical information were selected to construct the prognostic model. The 282 patients were grouped into a training set (70%, n = 198 samples) to determine a prognostic predictor by univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The internal validation set (30%, n = 84) and an external validation set (E-MTAB-3274) were used to validate the predictive value of the predictor by receiver operating characteristic (ROC) analysis and Kaplan–Meier survival analysis. </sec> <sec><title>Results</title>We successfully identified a 9-DNA methylation signature for recurrence free survival (RFS) of KIRC patients. We proved the strong robustness of the 9-DNA methylation signature for predicting RFS through ROC analysis (AUC at 1, 3, 5 years in internal dataset (0.859, 0.840, 0.817, respectively), external validation dataset (0.674, 0.739, 0.793, respectively), entire TCGA dataset (0.834, 0.862, 0.842, respectively)). In addition, a nomogram combining methylation risk score with the conventional clinic-related covariates was constructed to improve the prognostic predicted ability for KIRC patients. The result implied a good performance of the nomogram. </sec> <sec><title>Conclusions</title>we successfully identified a DNA methylation-associated nomogram, which was helpful in improving the prognostic predictive ability of KIRC patients. </sec> </abstract>

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