A 13-gene risk score system and a nomogram survival model for predicting the prognosis of clear cell renal cell carcinoma

Abstract

BackgroundRenal cell carcinoma (RCC) is the second common malignant tumor in the urinary system, and 85% of RCC cases are clear cell RCC (ccRCC). This study is designed to build a risk score system for ccRCC. MethodsThe gene methylation and expression data of ccRCC samples were downloaded from The Cancer Genome Atlas database (training set) and ArrayExpress database (validation set). The differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were identified by limma package, and their intersecting genes with negative Pearson correlation coefficients were remained using cor.test function. Prognosis-associated genes were identified by survival package, and the optimal DMGs were obtained using penalized package. After risk score system was built, nomogram survival model was constructed using rms package. Additionally, pathways were enriched for the DEGs between high- and low-risk groups using Gene Set Enrichment Analysis. ResultsThere were 3,638 DMGs and 2,702 DEGs between tumor and normal samples. Among the 312 intersecting genes, 43 prognosis-associated genes were identified. A total of 13 optimal DMGs (BTBD19, ADAM8, BGLAP, TNFRSF13C, JPH4, BEST1, GNRH2, UBE2QL1, CHODL, GDF9, UPB1, KCNH3; and ADAMTSL4) were obtained for building the risk score system. After pathological M, pathological T, platelet qualitative, and RS status were revealed to be independent prognostic factors, a nomogram survival model was constructed. For the 920 DEGs between the high- and low-risk samples, 6 significant pathways were enriched. ConclusionThe 13-gene risk score system and the nomogram survival model might be used for prognostic prediction of ccRCC patients.