INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) is a common urological malignant tumor. Dysregulated peroxisomes contribute to the progression of cancers. However, the prognostic significance of peroxisome-related genes (PGs) in ccRCC is still poorly understood. METHODS: PGs were collected from MsigDB. Prognostic differentially expressed genes were filtered via differentially expression analysis and univariate Cox regression analysis. The construction of risk model was performed by the least absolute shrinkage selection operator Cox regression analysis. Subsequently, the clinical application of risk model in prognosis prediction, tumor microenvironment (TME) and drug sensitivity was comprehensively evaluated. The expression levels of genes were measured by qRT-PCR and immunohistochemistry. Finally, the role of the genes of this risk model in biological behaviors of RCC cells was further verified via CCK-8, transwell invasion and wound healing assay. RESULTS: A risk model, including 9 PGs, was established. The risk model exhibited a robust and accurate performance in prognostic prediction across TCGA, GSE167573 and the local cohorts. Moreover, the risk model was closely correlated with clinical characteristics, TME and drug sensitivity. Silencing of the key genes attenuated the proliferation, migration, and invasion ability of RCC cells. CONCLUSION: The novel peroxisome-related risk model holds promise as a prognostic tool for estimating the prognosis of ccRCC patients and provides insights into treatment strategies.