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Updated: Nov 23, 2025
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
cg04448376, cg24387542, cg08548498, and cg14621323 as a Novel Signature to Predict Prognosis in Kidney Renal
Ying-Lei Wang1, Ying-Ying Zhang2
1Department of Urinary Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong 264100, China.
Introduction:
DNA methylation plays a vital role in prognosis prediction of cancers. In this study, we aimed to identify novel DNA methylation site biomarkers and create an efficient methylated site model for predicting survival in kidney renal papillary cell carcinoma (KIRP).
Methods:
DNA methylation and gene expression profile data were downloaded from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Differential methylated genes (DMGs) and differential expression genes (DEGs) were identified and then searched for the hub genes. Cox proportional hazards regression was applied to identify DNA methylated site biomarkers from the hub genes. Kaplan-Meier survival and ROC analyses were used to validate the effective prognostic ability of the methylation gene site biomarker. The biomarker sites were validated in the GEO cohorts. The GO and KEGG annotation was done to explore the biological function of DNA methylated site signature.
Results:
Nine DMGs with opposite expression patterns containing 47 methylated sites were identified. Finally, four methylated sites were identified using the hazard regression model (cg04448376, cg24387542, cg08548498, and cg14621323) located in UTY, LGALS9B, SLPI, and PFN3, respectively. These sites classified patients into high- and low-risk groups in the training cohort. The 5-year survival rates for patients with low-risk and high-risk scores were 97.5% and 75.9% (P < 0.001). The prognostic accuracy and signature methylation sites were validated in the test (TCGA, n = 87) and GEO cohorts (n = 14). Multivariate regression analysis showed that the signature was an independent prediction prognostic factor for KIRP. Based on this analysis, we developed methylated site signature nomogram that predicts an individual's risk of survival. Functional analysis suggested that these signature genes are involved in the biological processes of protein binding.
Conclusions:
Our study demonstrated that the methylated gene site signature might be a powerful prognostic tool for evaluating survival rate and guiding tailored therapy for KIRP patients.
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