Lung cancer is the leading cause of cancer-related death. Despite a number of studies that have provided prognostic biomarkers for lung cancer, a paucity of reliable markers and therapeutic targets exist to diagnose and treat this aggressive disease. In this study we investigated the potential of nuclear receptors (NRs), many of which are well-established drug targets, as therapeutic markers in lung cancer. Using quantitative real-time PCR, we analyzed the expression of the 48 members of the NR superfamily in a human panel of 55 normal and lung cancer cell lines. Unsupervised cluster analysis of the NR expression profile segregated normal from tumor cell lines and grouped lung cancers according to type (i.e. small vs. non-small cell lung cancers). Moreover, we found that the NR signature was 79% accurate in diagnosing lung cancer incidence in smokers (n = 129). Finally, the evaluation of a subset of NRs (androgen receptor, estrogen receptor, vitamin D receptor, and peroxisome proliferator-activated receptor-γ) demonstrated the therapeutic potential of using NR expression to predict ligand-dependent growth responses in individual lung cancer cells. Preclinical evaluation of one of these receptors (peroxisome proliferator activated receptor-γ) in mouse xenografts confirmed that ligand-dependent inhibitory growth responses in lung cancer can be predicted based on a tumor's receptor expression status. Taken together, this study establishes NRs as theragnostic markers for predicting lung cancer incidence and further strengthens their potential as therapeutic targets for individualized treatment.