Clear cell renal cell carcinoma (ccRCC) is an aggressive and difficult to manage cancer. Immunotherapy has the potential to induce long-lasting regression in a small group of patients. However, severe side effects limit broad application which highlights the need for a marker to distinguish responder from nonresponder. TNMG staging, referring to tumor size, lymph node involvement, presence of metastasis, and grade of tumor differentiation, represents an important prognostic system but is not useful for predicting responders to immunotherapy. NK cells are potent antitumor effector cells, and a role as prognostic marker in some solid tumors has been suggested. As NK cells are responsive to various immune modifiers, they may be important mediators of patient response to immunotherapies, in particular those including IL-2. We report that the NK cell percentage within RCC-infiltrating lymphocytes, as determined by flow cytometry, allows ccRCC subgrouping in NK(high)/NK(low) tissues independent of TNMG classification. Quantitative reverse transcriptase polymerase chain reaction using whole-tissue RNA identified four markers (NKp46, perforin, CX(3)CL1, and CX(3)CR1) whose transcript levels reproduced the NK(high)/NK(low) tissue distinction identified by flow cytometry with high selectivity and specificity. Combined in a multiplex profile and analyzed using neural network, the accuracy of predicting the NK(high)/NK(low) groups was 87.8%, surpassing that of each single marker. The tissue transcript signature, based on a robust high-throughput methodology, is easily amenable to archive material and clinical translation. This now allows the analysis of large patient cohorts to substantiate a role of NK cells in cancer progression or response to immunotherapy.