Palmitoylation, a key post-translational modification, plays a crucial role in tumor progression, yet its landscape in clear cell renal cell carcinoma (ccRCC) remains poorly characterized. This study aims to systematically identify and validate key palmitoylation-modifying enzymes in ccRCC and explore their clinical significance.

We integrated multi-omics data from TCGA-KIRC and GEO datasets to evaluate palmitoylation levels using the PalmScore system. Machine learning algorithms were applied to identify diagnostic and prognostic key genes. Functional roles of ZDHHC11 were validated in vitro using siRNA-mediated knockdown in ccRCC cell lines. Single-cell RNA sequencing data further confirmed expression patterns.

PalmScore effectively stratified ccRCC patients into high- and low-risk groups, with the high PalmScore group showing enriched immune infiltration and poorer survival outcomes. Machine learning identified ZDHHC2 and ABHD17C as diagnostic markers, while ZDHHC11 emerged as a prognostic key gene. In vitro experiments demonstrated that ZDHHC11 knockdown significantly suppressed proliferation, migration, and invasion in ccRCC cells. Single-cell analysis validated the expression patterns of these key genes across different cell types.

Our study unveils the critical roles of palmitoylation-modifying enzymes in ccRCC progression and immune regulation. The identified key genes hold promise as biomarkers for diagnosis and prognosis, offering potential targets for future therapeutic strategies.