Dataset Information

DNA Methylation-Based Panel Predicts Survival of Patients With Clear Cell Renal Cell Carcinoma and Its Correlations With Genomic Metrics and Tumor Immune Cell Infiltration.

ABSTRACT: DNA methylation based prognostic factor for patients with clear cell renal cell carcinoma (ccRCC) remains unclear. In the present study, we identified survival-related DNA methylation sites based on the differentially methylated DNA CpG sites between normal renal tissue and ccRCC. Then, these survival-related DNA methylation sites were included into an elastic net regularized Cox proportional hazards regression (CoxPH) model to build a DNA methylation-based panel, which could stratify patients into different survival groups with excellent accuracies in the training set and test set. External validation suggested that the DNA methylation-based panel could effectively distinguish normal controls from tumor samples and classify patients into metastasis group and non-metastasis group. The nomogram containing DNA methylation-based panel was reliable in clinical settings. Higher total mutation number, SCNA level, and MATH score were associated with higher methylation risk. The innate immune, ratio between CD8⁺T cell versus Treg cell as well as Th17 cell versus Th2 cell were significantly decreased in high methylation risk group. In inclusion, we developed a DNA methylation-based panel which might be independent prognostic factor in ccRCC. Patients with higher methylation risk were associated genomic alteration and poor immune microenvironment.

SUBMITTER: Liu XP

PROVIDER: S-EPMC7593608 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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DNA Methylation-Based Panel Predicts Survival of Patients With Clear Cell Renal Cell Carcinoma and Its Correlations With Genomic Metrics and Tumor Immune Cell Infiltration.

Liu Xiao-Ping XP Ju Lingao L Chen Chen C Liu Tongzu T Li Sheng S Wang Xinghuan X

Frontiers in cell and developmental biology 20201015

DNA methylation based prognostic factor for patients with clear cell renal cell carcinoma (ccRCC) remains unclear. In the present study, we identified survival-related DNA methylation sites based on the differentially methylated DNA CpG sites between normal renal tissue and ccRCC. Then, these survival-related DNA methylation sites were included into an elastic net regularized Cox proportional hazards regression (CoxPH) model to build a DNA methylation-based panel, which could stratify patients i ...[more]

PMID: 33178689

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Project description:BackgroundPromoter methylation has emerged as a promising class of epigenetic biomarkers for diagnosis and prognosis of renal cell tumors (RCTs). Although differential gene promoter methylation patterns have been reported for the major subtypes (clear cell, papillary and chromophobe renal cell carcinoma, and oncocytoma), validation of diagnostic performance in independent series have been seldom performed. Herein, we aimed at assessing the diagnostic performance of genes previously shown to be hypermethylated in RCTs in different clinical settings.MethodsPromoter methylation levels of HOXA9 and OXR1 were assessed by quantitative methylation specific PCR. ROC curves were generated for OXR1, OXR1 combined with MST1R and HOXA9. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy were computed, maximizing specificity. Methylation levels were also correlated with clinical and pathological relevant parameters.ResultsHOXA9 and OXR1 promoter methylation was disclosed in 73 and 87% of RCTs, respectively. A two-gene methylation panel comprising OXR1 and MST1R identified malignancy with 98% sensitivity and 100% specificity, and clear cell renal cell carcinoma with 90% sensitivity and 98% specificity. HOXA9 promoter methylation allowed for discrimination between oncocytoma and both papillary and chromophobe renal cell carcinoma but only with 77% sensitivity and 73% specificity. Significantly higher OXR1 promoter methylation levels (p = 0.005) were associated with high nuclear grade in ccRCC.ConclusionsA panel including OXR1 and MST1R promoter methylation allows specific and sensitive identification of renal cell tumors, and, especially, of clear cell renal cell carcinoma. Moreover, higher OXR1 promoter methylation levels associate with clear cell renal cell carcinoma nuclear grade, a surrogate for tumor aggressiveness. Thus, gene promoter methylation analysis might a useful ancillary tool in diagnostic management of renal masses.

Project description:Background: Oxidative stress is related to oncogenic transformation in kidney renal clear cell carcinoma (KIRC). We intended to identify a prognostic antioxidant gene signature and investigate its relationship with immune infiltration in KIRC. Methods: With the support of The Cancer Genome Atlas (TCGA) database, we researched the gene expression and clinical data of KIRC patients. Antioxidant related genes with significant differences in expression between KIRC and normal samples were then identified. Through univariate and multivariate Cox analysis, a prognostic gene model was established and all patients were divided into high- and low-risk subgroups. Single sample gene set enrichment analysis was adopted to analyze the immune infiltration, HLA expression, and immune checkpoint genes in different risk groups. Finally, the prognostic nomogram model was established and evaluated. Results: We identified six antioxidant genes significantly correlated with the outcome of KIRC patients as independent predictors, namely DPEP1 (HR = 0.97, P < 0.05), GSTM3 (HR = 0.97, P < 0.05), IYD (HR = 0.33, P < 0.05), KDM3B (HR = 0.96, P < 0.05), PRDX2 (HR = 0.99, P < 0.05), and PRXL2A (HR = 0.96, P < 0.05). The high- and low-risk subgroups of KIRC patients were grouped according to the six-gene signature. Patients with higher risk scores had poorer prognosis, more advanced grade and stage, and more abundance of M0 macrophages, regulatory T cells, and follicular helper T cells. There were statistically significant differences in HLA and checkpoint gene expression between the two risk subgroups. The performance of the nomogram was favorable (concordance index = 0.766) and reliably predicted the 3-year (AUC = 0.792) and 5-year (AUC = 0.766) survival of patients with KIRC. Conclusion: The novel six antioxidant related gene signature could effectively forecast the prognosis of patients with KIRC, supply insights into the interaction between cellular antioxidant mechanisms and cancer, and is an innovative tool for selecting potential patients and targets for immunotherapy.

Project description:Background and objectiveEmerging evidence has proven the robust role of tumor mutation burden (TMB) and immune cell infiltration (ICI) in cancer immunotherapy. However, the precise effect of TMB and ICI on clear cell renal cell carcinoma (ccRCC) remains elusive and merits further investigation. Therefore, we aim to identify the TMB-related genes in predicting prognosis and to explore the potential mechanisms of the identified Interleukin 20 receptor subunit beta (IL20RB) in ICI in ccRCC.MethodThe relative information of patients with ccRCC was obtained from The Cancer Genome Atlas database (TCGA). Immune-related genes were downloaded from the Immunology Database and Analysis Portal database. Cox regression analysis was used to identify prognosis-related immune genes for ccRCC. The relationship of IL20RB expression levels with clinicopathological parameters was analyzed using the "limma" and "survival" packages. Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) databases were used as external validation. Quantitative Real-time PCR (qRT-PCR) and western blots were used to validate the expression levels of IL20RB in tumor cells. Cell counting kit-8 (CCK-8) assay and colony formation assay were used to examine the effect of IL20RB on the viability of ccRCC cells. Gene set enrichment analysis (GSEA) was introduced for the analysis of IL20RB-related signaling pathways. Tumor Immune Estimation Resource (TIMER) and Tumor and Immune System Interaction Database (TISIDB) were utilized to determine the correlation of IL20RB expression levels with tumor-infiltrating immune cells (TIICs).ResultsIL20RB was significantly overexpressed in different ccRCC tissues and cells. High IL20RB expression in ccRCC patients was associated with short overall survival, high tumor grade, and advanced TNM stage. After knockdown of IL20RB with small interfering RNA (siRNA) technology, ccRCC cells' proliferation was significantly attenuated. Moreover, overexpression of IL20RB could increase the infiltration level of several immune cells, especially T follicular helper cells (Tfh), and overexpressed Tfh cells were correlated with poor prognosis in ccRCC.ConclusionsIL20RB may function as an immune-associated therapeutic target for it determines cancer progression and regulates immune cell infiltration in ccRCC.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a prevalent urogenital malignancy characterized by heterogeneous patterns. Stemness is a pivotal factor in tumor progression, recurrence, and metastasis. Nevertheless, the impact of stemness-related long non-coding RNAs (SRlncRNAs) on the prognosis of ccRCC remains elusive. In this study, we aimed to delve into the SRlncRNAs of ccRCC and develop a signature for risk stratification and prognosis prediction.MethodGene-expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We calculated RNA stemness scores (RNAss) for the samples to evaluate their stemness. SRlncRNAs and stemness-related mRNAs (SRmRNAs) in ccRCC were identified through weighted correlation network analysis (WGCNA), which employed sophisticated statistical methodologies to identify interconnected modules of related genes. Enrichment analysis was performed to explore the potential functions of SRmRNAs. Multiple machine learning algorithms were employed to construct a prognostic signature. Samples from TCGA-KIRC and GSE29609 cohorts were designated as the training and validation cohorts, respectively. Based on their risk scores, samples were stratified into low- and high-risk groups. Prognosis analysis, immune infiltration assessment, drug sensitivity prediction, mutation landscape, and gene set enrichment analysis (GSEA) were conducted to investigate the distinct characteristics of the low- and high-risk groups. Additionally, a web-based calculator was developed to facilitate clinical application. Expression and effects of SRlncRNAs in ccRCC were further corroborated through the utilization of single-cell RNA-seq (scRNA-seq), as well as in vitro and in vivo experiments.ResultsSRlncRNAs and SRmRNAs were identified based on RNAss and WGCNA. The least absolute shrinkage and selection operator (LASSO) in combination with multivariate Cox regression was selected as the optimal approach. Six SRlncRNAs were used to construct the prognostic signature. Samples in the low- and high-risk groups exhibited distinct characteristics in terms of prognosis, GSEA pathways, immune infiltration profiles, drug sensitivity, and mutation status. A nomogram and a web-based calculator were developed to facilitate the clinical application of the model. ScRNA-seq and RT-qPCR demonstrated the differential expression of SRlncRNAs between ccRCC tumors and normal tissues. In vitro and in vivo experiments demonstrated that downregulation of EMX2OS and LINC00944 affected the proliferation, migration, invasion, apoptosis, and metastasis of ccRCC cells.ConclusionWe uncovered the crucial associations between SRlncRNAs and the prognosis of ccRCC. By leveraging these findings, we developed a novel SRlncRNA-related signature and a user-friendly web calculator. This signature holds great potential in facilitating risk stratification and guiding tailored treatment strategies for ccRCC patients. Both in vitro and in vivo experiments confirmed the role of SRlncRNAs in the progression of ccRCC.

Dataset Information

DNA Methylation-Based Panel Predicts Survival of Patients With Clear Cell Renal Cell Carcinoma and Its Correlations With Genomic Metrics and Tumor Immune Cell Infiltration.

Publications

DNA Methylation-Based Panel Predicts Survival of Patients With Clear Cell Renal Cell Carcinoma and Its Correlations With Genomic Metrics and Tumor Immune Cell Infiltration.

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