Objective: Sunitinib is a first-line drug in the treatment of metastatic renal cell carcinoma, but patients will inevitably develop drug resistance after 6-15 months of systematic treatment, which seriously affects the prognosis in KIRC.
Methods: During the study, the Gene Expression Omnibus (GEO) database was used to perform a systematic bioinformatics analysis,so that we could determine the genes (DEGs) which are differentially expressed between sunitinib-sensitive and sunitinib-resistant RCC (SRRC) cells.
Results: A total of 31 DEGs were identified. Gene ontology (GO) was used to analyze the function of DEGS. These DEGs were found mainly enriched in organic aniontransmembrane transporter. The Cytohubba plug-in, STRING database and Cytoscape software were involved to construct a protein-protein interaction (PPI) network, and the pivot genes were identified by single-gene and multi-gene Cox regression analysis. Finally, DDX58 and MX2 were identified as prognostic genes. Survival analysis was performed by using prognostic nomogram, prognostic histogram and GEPIA database to verify the relationship between DDX58 and MX2 expression and survival. The relationship between the two pivot genes and the prognosis of patients was further verified by using the KM survival analyses and Time Dependency ROC curve analyses from TCGA database. Immunohistochemical analyses confirmed that, in tumor tissues and normal tissues, DDX58 and MX2 were differentially expressed. The expression of these two genes have relationship with the immune checkpoint.
Conclusions: This study provides insights into the molecular mechanisms of SRRC, as well as the selection of therapeutic and prognostic biomarkers for SRRC.
Keywords: Biomarkers; Immune; RCC; Sunitinib-resistant.
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