Background:Clear-cell renal cell carcinoma (ccRCC) is the most common malignant kidney cancer. However, the tumor microenvironment and crosstalk involved in metabolic reprogramming in ccRCC are not well-understood.Methods:We used The Cancer Genome Atlas to obtain ccRCC transcriptome data and clinical information. The E-MTAB-1980 cohort was used for external validation. The GENECARDS database contains the first 100 solute carrier (SLC)-related genes. The predictive value of SLC-related genes for ccRCC prognosis and treatment was assessed using univariate Cox regression analysis. An SLC-related predictive signature was developed through Lasso regression analysis and used to determine the risk profiles of patients with ccRCC. Patients in each cohort were separated into high- and low-risk groups based on their risk scores. The clinical importance of the signature was assessed through survival, immune microenvironment, drug sensitivity, and nomogram analyses using R software.Results:SLC25A23,SLC25A42,SLC5A1,SLC3A1,SLC25A37,SLC5A6,SLCO5A1, andSCP2comprised the signatures of the eight SLC-related genes. Patients with ccRCC were separated into high- and low-risk groups based on the risk value in the training and validation cohorts; the high-risk group had a significantly worse prognosis (p< 0.001). The risk score was an independent predictive indicator of ccRCC in the two cohorts according to univariate and multivariate Cox regression (p< 0.05). Analysis of the immune microenvironment showed that immune cell infiltration and immune checkpoint gene expression differed between the two groups (p< 0.05). Drug sensitivity analysis showed that compared to the low-risk group, the high-risk group was more sensitive to sunitinib, nilotinib, JNK-inhibitor-VIII, dasatinib, bosutinib, and bortezomib (p< 0.001). Survival analysis and receiver operating characteristic curves were validated using the E-MTAB-1980 cohort.Conclusions:SLC-related genes have predictive relevance in ccRCC and play roles in the immunological milieu. Our results provide insight into metabolic reprogramming in ccRCC and identify promising treatment targets for ccRCC.