An effective seven-CpG-based signature to predict survival in renal clear cell carcinoma by integrating DNA methylation and gene expression.

AIMS: Currently, using clinicopathological risk factors only is not far from effective to evaluate the risk of disease progression in renal clear cell carcinoma (KIRC) patients. Molecular biomarkers might improve risk stratification of KIRC. DNA methylation occurs the whole process of tumor development and transcriptional disorders are also one of the important characteristics of tumor. Hence, this study aims to develop an effective and independent prognostic signature for KIRC patients by Integrating DNA methylation and gene expression. MAIN
METHODS: Difference analysis was conducted on DNA methylation sites and gene expression data. The Spearman's rank correlation and univariate Cox regression analysis were used to screen out the CpG sites that related with RNAs' expression and KIRC patients' overall survival. Then, a five-CpG-based prognostic classifier was established using LASSO Cox regression method. KEY
FINDINGS: The seven-CpG-based classifier can successfully divide KIRC patients into high-risk from low-risk groups, even after adjustment for standard clinical prognostic factors, such as age, stage, gender and grade. Moreover, the seven-CpG-based signature was more effective as independent prognostic factors than the combined model of these clinical factors. Six differential mRNA genes corresponding to the seven CpG sites are all related to human cancers by functional exploration. The gene functional and pathway enrichment analysis found that genes in immune-related pathways were remarkably different in high and low-risk groups. SIGNIFICANCE: The new seven-CpG-based signature could helpfully provide insights into the underlying mechanism of KIRC and may be a powerful independent biomarker for predicting of the survival of KIRC patients.