Dataset Information

LAT, HOXD3 and NFE2L3 identified as novel DNA methylation-driven genes and prognostic markers in human clear cell renal cell carcinoma by integrative bioinformatics approaches.

ABSTRACT: Background: Abnormal DNA methylation of is one of the important mechanisms leading to tumor pathogenesis. The purpose of this study was to explore differentially methylated genes that may drive the development of renal clear cell carcinoma through a comprehensive analysis of the TCGA database. Materials and methods: Methylation data and RNA-seq data for clear cell renal cell carcinoma were downloaded from The Cancer Genome Atlas (TCGA). Differentially methylated genes and the differential genes associated with survival were then screened by MethylMix R package and univariate Cox proportional-hazards model, respectively. Their common genes were then intersected and obtained for further analysis. Correlation of gene expression and methylation levels, gene set enrichment analysis (GSEA) enrichments, survival curve, and ROC curve plotting for DNA methylation-driven genes were finally performed. The methylation alterations of the three genes were validated via two GEO datasets (GSE70303 and GSE113501), and the genes expression level was verified through two GEO datasets (GSE6344 and GSE53757). Results: Three novel DNA methylation-driven genes LAT, HOXD3 and NFE2L3 were identified in clear cell renal cell carcinoma. Expression analysis further revealed that hypomethylation levels of LAT and NFE2L3 showed higher gene expression levels, while HOXD3 exhibited opposite methylation-expression pattern. The CpG sites of LAT (cg16462073), HOXD3 (cg24000528) and NFE2L3 (cg16882373) that may affect respective gene expressions were also identified. For the survival analysis, we found that hypomethylation and over-expression of LAT and NFE2L3 were correlated with poor survival, while hypermethylation and low-expression HOXD3 was correlated with poor survival of clear cell renal cell carcinoma patients. In addition, GSEA KEGG analysis and biological processes of these genes were also enriched for functional analysis. Kaplan-Meier survival and ROC analyses of these genes showed an average risk score of 0.9140593, AUC = 0.692, which suggested a good clinical application value. Finally, the opposite methylation-expression pattern of these three genes were verified in GEO datasets. Conclusions: In this study, we successfully exhibited the potential DNA methylation-driven genes LAT, HOXD3, and NFE2L3 involved in clear cell renal cell carcinoma. Moreover, gene functions and prognostic risk models were also elucidated, which facilitated the expansion of the current study on the role of methylation in the pathology process of clear cell renal cell carcinoma.

SUBMITTER: Wang J

PROVIDER: S-EPMC6856894 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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LAT, HOXD3 and NFE2L3 identified as novel DNA methylation-driven genes and prognostic markers in human clear cell renal cell carcinoma by integrative bioinformatics approaches.

Wang Jie J Zhao Hu H Dong Huiyue H Zhu Ling L Wang Shuiliang S Wang Ping P Ren Qun Q Zhu Hehuan H Chen Junqiu J Lin Zhijie Z Cheng Yuanhang Y Qian Benjiang B Zhang Yi Y Jia Ruxue R Wu Weizheng W Lu Jun J Tan Jianming J

Journal of Cancer 20191022 26

<b>Background:</b> Abnormal DNA methylation of is one of the important mechanisms leading to tumor pathogenesis. The purpose of this study was to explore differentially methylated genes that may drive the development of renal clear cell carcinoma through a comprehensive analysis of the TCGA database. <b>Materials and methods:</b> Methylation data and RNA-seq data for clear cell renal cell carcinoma were downloaded from The Cancer Genome Atlas (TCGA). Differentially methylated genes and the diffe ...[more]

PMID: 31777602

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Project description:Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.

Project description:Background and objectivePrevious germline studies on renal cell carcinoma (RCC) have usually pooled clear and non-clear cell RCCs and have not adequately accounted for population stratification, which might have led to an inaccurate estimation of genetic risk. Here, we aim to analyze the major germline drivers of RCC risk and clinically relevant but underexplored germline variant types.MethodsWe first characterized germline pathogenic variants (PVs), cryptic splice variants, and copy number variants (CNVs) in 1436 unselected RCC patients. To evaluate the enrichment of PVs in RCC, we conducted a case-control study of 1356 RCC patients ancestry matched with 16 512 cancer-free controls using approaches accounting for population stratification and histological subtypes, followed by characterization of secondary somatic events.Key findings and limitationsClear cell RCC patients (n = 976) exhibited a significant burden of PVs in VHL compared with controls (odds ratio [OR]: 39.1, p = 4.95e-05). Non-clear cell RCC patients (n = 380) carried enrichment of PVs in FH (OR: 77.9, p = 1.55e-08) and MET (OR: 1.98e11, p = 2.07e-05). In a CHEK2-focused analysis with European participants, clear cell RCC (n = 906) harbored nominal enrichment of low-penetrance CHEK2 variants-p.Ile157Thr (OR: 1.84, p = 0.049) and p.Ser428Phe (OR: 5.20, p = 0.045), while non-clear cell RCC (n = 295) exhibited nominal enrichment of CHEK2 loss of function PVs (OR: 3.51, p = 0.033). Patients with germline PVs in FH, MET, and VHL exhibited significantly earlier age of cancer onset than patients without germline PVs (mean: 46.0 vs 60.2 yr, p < 0.0001), and more than half had secondary somatic events affecting the same gene (n = 10/15, 66.7%). Conversely, CHEK2 PV carriers exhibited a similar age of onset to patients without germline PVs (mean: 60.1 vs 60.2 yr, p = 0.99), and only 30.4% carried somatic events in CHEK2 (n = 7/23). Finally, pathogenic germline cryptic splice variants were identified in SDHA and TSC1, and pathogenic germline CNVs were found in 18 patients, including CNVs in FH, SDHA, and VHL.Conclusions and clinical implicationsThis analysis supports the existing link between several RCC risk genes and RCC risk manifesting in earlier age of onset. It calls for caution when assessing the role of CHEK2 due to the burden of founder variants with varying population frequency. It also broadens the definition of the RCC germline landscape of pathogenicity to incorporate previously understudied types of germline variants.Patient summaryIn this study, we carefully compared the frequency of rare inherited mutations with a focus on patients' genetic ancestry. We discovered that subtle variations in genetic background may confound a case-control analysis, especially in evaluating the cancer risk associated with specific genes, such as CHEK2. We also identified previously less explored forms of rare inherited mutations, which could potentially increase the risk of kidney cancer.

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Dataset Information

LAT, HOXD3 and NFE2L3 identified as novel DNA methylation-driven genes and prognostic markers in human clear cell renal cell carcinoma by integrative bioinformatics approaches.

Publications

LAT, HOXD3 and NFE2L3 identified as novel DNA methylation-driven genes and prognostic markers in human clear cell renal cell carcinoma by integrative bioinformatics approaches.

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