Integrated Genomic and Functional Characterization of Palmitoylation in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a highly aggressive cancer with a poor prognosis. Palmitoylation, a posttranslational modification, plays a key role in regulating cancer progression and immune responses. However, its influence on ccRCC prognosis and immune therapy efficacy remains underexplored.

Multiple publicly available ccRCC datasets were integrated and harmonized through batch effect correction. A prognostic model based on palmitoylation-related genes was constructed using a combination of 101 machine learning algorithms. Single-cell RNA sequencing was employed to analyze cellular heterogeneity within the tumor microenvironment. Genomic profiling, including tumor mutational burden (TMB), copy number variation (CNV), and tumor stemness, was conducted to identify genomic differences between the high- and low-risk groups. Immune infiltration levels were assessed using various algorithms to compare immune profiles across patient subgroups, while immune therapy responses were predicted using multiple prediction models. Experimental validation of ZDHHC18, a key gene in the prognostic model, was performed in ccRCC cell lines (786-O and Caki-1) to evaluate its impact on cell proliferation, migration, and invasion.

The palmitoylation-related prognostic model effectively stratified ccRCC patients into the high- and low-risk groups, with distinct differences in survival outcomes. Genomic analysis demonstrated higher TMB and CNV alterations in the high-risk group. Immune response predictions indicated that low-risk patients were more likely to benefit from immunotherapy. Additionally, ZDHHC18 was significantly upregulated in ccRCC tumor tissues, and its knockdown notably inhibited cell proliferation, migration, and invasion.

Palmitoylation-related genes, particularly ZDHHC18, serve as promising prognostic biomarkers and predictive indicators for immune therapy in ccRCC. These findings offer new insights into ccRCC biology and highlight potential therapeutic targets for improving patient outcomes.