Identification of Prognostic Biomarkers for Clear Cell Renal Cell Carcinoma (ccRCC) by Transcriptomics

Clear cell renal cell carcinoma (ccRCC) is the most common histological type of renal malignancies. Our aim was to find the prognostic crux of ccRCC.

The data for ccRCC was acquired from The Cancer Genome Atlas (TCGA) and the International Genome Consortium (ICGC) database. Weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs), and univariate Cox analysis were applied to classify the gene groups. A Venn diagram was used to find the intersection of the gene groups. The prognostic efficiency was proved by Kaplan-Meier analysis. Heatmap and volcano plots were utilized for differential analysis. The risk score (RS) was calculated based on the multivariate Cox analysis.

The ccRCC samples were analyzed respectively via WGCNA, DEGs, and univariate Cox analysis based on five-year overall survival (OS) and intersected at two genes, ADGRF5 and EFNB2. The expressions of ADGRF5 and EFNB2 were negatively correlated with the OS and disease-specific survival (DSS) of the TCGA dataset, which was proved by Kaplan-Meier analysis (P<0.001). The RS was constructed and demonstrated great prognostic efficiency in predicting the OS, DSS, and progression-free interval (PFI) in TCGA (P<0.001) and the OS in ICGC (p=0.037). The area under the receiver operating characteristic (ROC) curve (AUC) of RS achieved 0.647 in the prediction of three-year survival and 0.714 for five-year survival. The KEGG pathway enrichment in high RS samples filtered five enriched pathways, in which CTNNB1 and LRP6 showed the best accordance with RS (P<0.001). xCell analysis revealed increased T cell CD4+ Th1 cell infiltration in high RS samples (P<0.001). PD-1 expression was higher in the high RS patients.

We constructed RS as a convincing prognostic index for ccRCC patients and found potential scientific target pathways.