Objective To mine stemness-related biomarkers of renal cancer based on the renal cancer stem cell microarray data from Gene Expression Omnibus (GEO) database, and to construct a new model for the prognosis of renal cancer with the clinical and transcriptome data of renal cancer in the Cancer Genome Atlas (TCGA) database. Methods The microarray data were downloaded from the GSE48550 dataset of GEO database to screen the differentially expressed genes between renal cancer stem cells and normal renal tubular epithelium cells. Gene function and pathway were identified by Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). The hub genes of renal cancer stem cells were identified by protein-protein interaction (PPI) network construction. Age, clinical stage, prognosis and expression levels of related genes of patients with renal cancer were downloaded from the TCGA database. The independent risk factors of prognosis of renal cancer were screened by univariate and multivariate Cox regression analyses, and a nomogram model for predicting the overall survival of patients with renal cancer was constructed. Results By analyzing the microarray data of renal cancer stem cells and normal renal tubular epithelial cells, we found that the differentially expressed genes were enriched in the biological processes such as cell chemotaxis, extracellular matrix formation and receptor ligand activity; and inflammatory response, P53 and tumor necrosis factor α (TNF-α)/nuclear factor κB (NF-κB) pathways were significantly activated in renal cancer stem cells. Univariate and multivariate Cox regression analyses showed that age and clinical stage were independent risk factors for the prognosis of renal cancer, and C-X3-C motif chemokine ligand 1 (CX3CL1) in chemokine family was an independent protective factor for the prognosis of renal cancer. The risk model based on age, clinical stage, and CX3CL1 expression level could accurately predict the overall survival rate of patients with renal cancer, with a C-index of 0.803. Conclusion Stemness-related genes of renal cancer is screened through the joint analysis of GEO and TCGA. A new model combining patient age, clinical stage and CX3CL1 expression level is constructed to evaluate the prognosis of renal cancer patients.