Abstract
BACKGROUND: Renal cell carcinoma (RCC) is one of the most common urological cancers and has a poor prognosis. RCC is classified into several subtypes, among which kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) are the two most common subtypes. Due to the lack of adequate screening and comparative analysis of RCC subtypes, effective diagnosis and treatment strategies have not yet been achieved. METHODS: In this study, 450K methylation array data were collected from The Cancer Genome Atlas (TCGA). The 'limma moderated t-test' and LASSO were used to construct diagnostic and subtyping models, and survival analysis was conducted online by GEPIA. RESULTS: We built a model with 15 methylation sites, which showed high diagnostic and subtyping performance in specificity and sensitivity. At the same time, for potential clinical usability, we calculated the diagnostic and subtyping scores to classify RCC from normal tissue and distinguish the different RCC subtypes. Additionally, the CpG sites were mapped to their corresponding genes, which could also be used to predict the prognosis of RCC. CONCLUSIONS: Different methylation sites can be used as diagnostic and subtyping markers that are specific to RCC and RCC subtypes (KIRC and KIRP) with high sensitivity and accuracy.