The behavior and outcome of lung cancers are highly variable, and not only is the molecular basis of this variability unknown, but neither standard histopathology nor currently available molecular markers can predict these characteristics. Accordingly, the identification of novel biomarkers to differentiate tumor from normal cells and predict tumor behavior such as pathologic stage, response to chemotherapy, and site of relapse, is of great importance in clinical practice. None of the hundreds of single markers evaluated to date have demonstrated significant clinical utility, but by surveying thousands of genes at once with use of microarrays or proteomic technologies, it is now possible to read the molecular signature of an individual patient's tumor. When the signature is mathematically analyzed, new classes of cancer can be observed and insight can be gained into prediction, prognosis, and mechanism. Although some success has been achieved with genomic approaches, proteomics-based approaches allow examination of expressed proteins of a tissue or cell type, complement the genome initiatives, and are increasingly being used to address biomedical questions. This review aims to summarize the state of the art of gene and protein expression profiling for non-small-cell lung cancer