Lung cancer is the cause of nearly 170,000 cancer deaths in the United States each year, accounting for nearly 25% of all deaths from cancer. The 5-yr survival rate for lung cancer is < 15% from the time of diagnosis. This is largely due to the late stage of diagnosis and the lack of effective treatments, reflecting the need for a better understanding of the mechanisms that underlie lung carcinogenesis. Unlike the study of a single gene, protein, or pathway, genomic and proteomic technologies enable a systematic overview that provides the potential to improve our understanding of this disease. Ultimately, this could improve the diagnosis, prognosis, and clinical management of patients with lung cancer. Here, we review studies that generated profiles of gene and protein expression in lung cancer specimens and relevant model systems, and make recommendations to facilitate the clinical application of these technologies.