Background and Objective <p>Renal clear cell carcinoma (ccRCC) is highly heterogeneous, with significant differences in clinical outcomes such as prognosis and sensitivity to target therapy. The development of predictive, preventive, and personalized medicine (3PM) strategies in the area is essential to execute personalized treatment for ccRCC patients against disease progression effectively.</p> Working Hypothesis and Methods <p>We hypothesized that multi-omics for patients with ccRCC can provide more molecular characteristic information, aiming to develop and validate a personalized, multi-omics prognostic modeling framework for individualized risk stratification of clinical outcomes in ccRCC. We employed a suite of machine learning algorithms for multi-dimensional omics biomarker fusion and for subtype association with single-cell sequencing to classify ccRCC patients into distinct subtypes. Optimal subtypes were determined by comparing silhouette values across various omics combinations. The classifier was validated using two independent external datasets (ICGC-KIRC, 91 patients and GSE167573, 55 patients) and verified with single-cell sequencing data we collected. An interactive web page was developed to facilitate clinical application, enhancing predictive potential.</p> Results and data interpretation in the framework of 3PM <p>After considering distribution and screening, five omics information from 325 ccRCC patients, including 1000 transcriptome biomarkers, 500 methylation biomarkers, 190 mutation biomarkers, 30 protein biomarkers, and 200 miRNA biomarkers, were included and integrated. Two distinct ccRCC subtypes were identified for personalized treatment: the immune-activated type, characterized by higher immune infiltration and sensitivity to TKIs like sunitinib and sorafenib; and the pathology-characterized type, which has a better prognosis and is more likely to respond to immune checkpoint inhibitor immunotherapy. Single-cell analysis revealed that immune-activated subtypes are significantly associated with myeloid cells and B cells, while pathology-characterized subtypes are significantly associated with endothelial cells and fibroblasts. The interactive web page (<a href="https://zclab-cnp.shinyapps.io/biomarker/">https://zclab-cnp.shinyapps.io/biomarker/</a>) provides a convenient tool for clinical precision medicine research. Patients or their treating physicians can upload their sequencing data, and Nearest Template Prediction based on the differentially expressed genes identified in this study can be conducted to ultimately obtain the corresponding patient subgroups.</p> Conclusions and 3PM-relevant outlook <p>Our study leverages multi-dimensional omics biomarker fusion and machine learning to support accurate risk stratification, personalized ccRCC management and individualized protection against ccRCC progression. Successful clinical application requires transfer learning in local patients, regular patients recalibration, and labortary validation, leading to a valuable reference for ccRCC 3PM strategies.</p>