In silico DNA methylation analysis identifies potential prognostic biomarkers in type 2 papillary renal cell carcinoma.
Man YangRyan A HladyDan ZhouThai H HoKeith D RobertsonPublished in: Cancer medicine (2019)
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.
Keyphrases
- copy number
- genome wide
- dna methylation
- mitochondrial dna
- rna seq
- gene expression
- renal cell carcinoma
- single cell
- multiple sclerosis
- poor prognosis
- squamous cell carcinoma
- lymph node
- newly diagnosed
- ejection fraction
- magnetic resonance
- computed tomography
- artificial intelligence
- human health
- high resolution
- risk assessment
- chronic kidney disease
- binding protein
- deep learning
- climate change