The vast majority of primary renal tumors were malignant and had a greater damage to the kidneys. The cause and development of tumor diseases were not only related to a single gene, but also caused by molecular aberrations in a network of complex genes or their generations. In this paper, we used differential network analysis method. The gene chip data of renal tumor related gene from Affymetrix hgu13b was firstly screened to obtain differential expressed genes, and the gene network was constructed separately under disease samples and normal samples. Secondly, the difference between disease network and normal genes was constructed, and the renal tumor related gene was analyzed. In our differential network model, we used a more accurate method than before. What's more, we used Bayesian information criteria to adjust the parameters, and the gene network under two different conditions was coded as the difference between the precision covariance matrices. More than two hundred pairs of gene pairs were identified from the differential network. Based on the differential network analysis of the central gene, GO enrichment analysis showed that the gene TBX3 had the highest degree of enrichment, indicating that it may be related to the formation of renal tumors. In the KEGG pathway enrichment analysis, it was found that p<0.05 was mainly enriched as a chemokine pathway, and chemokines had an important influence on the occurrence, metastasis and treatment of renal tumors.