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In silico DNA methylation analysis identifies potential prognostic biomarkers in type 2 papillary renal cell carcinoma.

ABSTRACT: There are currently no effective treatments for advanced-stage papillary renal cell carcinoma (PRCC). The goal of this study is to define potential DNA methylation-based markers and treatment targets for advanced-stage type 2 PRCC. Progressive DNA methylation changes and copy number variation (CNV) from localized to advanced-stage type 2 PRCC are analyzed by using methylation data generated by TCGA's kidney renal papillary cell carcinoma (TCGA-KIRP, 450k array) project. Survival analyses are performed for the identified biomarkers and genes with CNV. In addition, expression of the corresponding genes is investigated by RNA-seq analysis. Progressive methylation changes in several CpGs from localized to advanced-stage type 2 PRCC are observed. Four CpGs (cg00489401, cg27649239, cg20555674, and cg07196505) in particular are identified as markers for differentiating between localized and advanced-stage type 2 PRCC. Copy number analysis reveals that copy gain of PTK7 mostly occurs in advanced-stage type 2 PRCC. Both the four CpG methylation changes and PTK7 copy number gain are associated with patient survival. RNA-seq analysis demonstrates that PTK7 copy gain leads to higher PTK7 expression relative to tumors without copy number gain. Moreover, PTK7 is significantly upregulated from localized to advanced-stage type 2 PRCC and is linked to cancer cell invasion. In conclusion, DNA methylation markers that differentiate between localized and advanced-stage type 2 PRCC may serve as useful markers for disease staging or outcome, while PTK7 copy gain represents a potential treatment target for advanced-stage type 2 PRCC. Stepwise methylation changes and copy number gain also associate with disease stage in PRCC patients.

SUBMITTER: Yang M

PROVIDER: S-EPMC6745825 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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In silico DNA methylation analysis identifies potential prognostic biomarkers in type 2 papillary renal cell carcinoma.

Yang Man M Hlady Ryan A RA Zhou Dan D Ho Thai H TH Robertson Keith D KD

Cancer medicine 20190730 12

There are currently no effective treatments for advanced-stage papillary renal cell carcinoma (PRCC). The goal of this study is to define potential DNA methylation-based markers and treatment targets for advanced-stage type 2 PRCC. Progressive DNA methylation changes and copy number variation (CNV) from localized to advanced-stage type 2 PRCC are analyzed by using methylation data generated by TCGA's kidney renal papillary cell carcinoma (TCGA-KIRP, 450k array) project. Survival analyses are per ...[more]

PMID: 31361072

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Project description:The aim of the present study was to identify long non-coding RNA (lncRNA)-based prognostic biomarkers in papillary renal cell carcinoma (pRCC). lncRNA expression data and corresponding clinical data from patients with pRCC were obtained from The Cancer Genome Atlas. R software and packages were used for data analysis. Univariate Cox regression analysis and least absolute shrinkage and selection operator regression were performed to identify key lncRNAs, which were then used to construct a prognostic model using multivariate Cox regression analysis. Patients were divided into high- and low-risk groups, and Kaplan-Meier (KM) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted. The C-index was calculated to estimate the model's prognostic power. The hazard ratio (HR), 95% confidence interval (CI), and statistical significance of each key lncRNA were also calculated by multivariate Cox regression. Based on the result of the multivariate Cox regression analysis, KM survival plots were plotted for each significantly associated lncRNA. The subcellular locations of the prognostic biomarkers were predicted using lncRNAMap and lncLocator. A total of 17 lncRNA signatures were identified as key lncRNAs. Overall survival rate was significantly higher in the low-risk group compared with the high-risk group. The areas under the ROC curve were 0.93 (3-year ROC) and 0.902 (5-year ROC), and the C-index was 0.915. A forest plot was used to illustrate the HR and 95% CI of key lncRNAs. KM survival analysis revealed the prognostic significance of two protective biomarkers, AC024022.1 and GAS6-AS1, and three adverse biomarkers, AC087379.2, AL352984.1, and AL499627.1. It was predicted that AC024022.1 and AC087379.2 may be located in the cytoplasm and GAS6-AS1 may be located in the cytosol. The present study may contribute to the management of pRCC and serve as a foundation for further investigations into the underlying mechanism of tumorigenesis and progression of pRCC.

Project description:BackgroundIdentification of specific biological functions, pathways, and appropriate prognostic biomarkers is essential to accurately predict the clinical outcomes of and apply efficient treatment for breast cancer patients.MethodsTo search for metastatic breast cancer-specific biological functions, pathways, and novel biomarkers in breast cancer, gene expression datasets of metastatic breast cancer were obtained from Oncomine, an online data mining platform. Over- and under-expressed genesets were collected and the differentially expressed genes were screened from four datasets with large sample sizes (N > 200). They were analyzed for gene ontology (GO), KEGG pathway, protein-protein interaction, and hub gene analyses using online bioinformatic tools (Enrichr, STRING, and Cytoscape) to find enriched functions and pathways in metastatic breast cancer. To identify novel prognostic biomarkers in breast cancer, differentially expressed genes were screened from the entire twelve datasets with any sample sizes and tested for expression correlation and survival analyses using online tools such as KM plotter and bc-GenExMiner.ResultsCompared to non-metastatic breast cancer, 193 and 144 genes were differentially over- and under-expressed in metastatic breast cancer, respectively, and they were significantly enriched in regulating cell death, epidermal growth factor receptor signaling, and membrane and cytoskeletal structures according to the GO analyses. In addition, genes involved in progesterone- and estrogen-related signalings were enriched according to KEGG pathway analyses. Hub genes were identified via protein-protein interaction network analysis. Moreover, four differentially over-expressed (CCNA2, CENPN, DEPDC1, and TTK) and three differentially under-expressed genes (ABAT, LRIG1, and PGR) were further identified as novel biomarker candidate genes from the entire twelve datasets. Over- and under-expressed biomarker candidate genes were positively and negatively correlated with the aggressive and metastatic nature of breast cancer and were associated with poor and good prognosis of breast cancer patients, respectively.ConclusionsTranscriptome datasets of metastatic breast cancer obtained from Oncomine allow the identification of metastatic breast cancer-specific biological functions, pathways, and novel biomarkers to predict clinical outcomes of breast cancer patients. Further functional studies are needed to warrant validation of their roles as functional tumor-promoting or tumor-suppressing genes.

Dataset Information

In silico DNA methylation analysis identifies potential prognostic biomarkers in type 2 papillary renal cell carcinoma.

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In silico DNA methylation analysis identifies potential prognostic biomarkers in type 2 papillary renal cell carcinoma.

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